0:\\n return (total_tokens - total_errors)/total_tokens\\n else:\\n return np.nan\",\n \"def mape(true, predictions):\\n true = np.array(true)\\n predictions = np.array(predictions) \\n return np.mean(np.abs((true - predictions)) / true) * 100\",\n \"def Median_Forecast(df_test):\\n\\n df_pred = pd.DataFrame(\\n {\\\"Median Forecast\\\": np.median(df_test, axis=1)},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def get_avg_sentiment_scores_on_link_id_df(input_file_name):\\n\\n subreddit_name = input_file_name.split('_')[0]\\n\\n list_of_columns_to_be_graphed = ['vader_compound_score', 'vader_negative_score', 'vader_neutral_score',\\n 'vader_positive_score', 'whole_comment_sentiment_flair']\\n\\n avg_scores = {'avg_vader_compound_score': 0, 'avg_vader_negative_score': 0, 'avg_vader_neutral_score': 0,\\n 'avg_vader_positive_score': 0, 'avg_whole_comment_sentiment_flair': 0}\\n\\n # gets the dataframe\\n df = get_df_from_csv(input_file_name)\\n\\n # creates date object column for matplotlib\\n df['date'] = df['created_utc'].apply(lambda x: mdate.epoch2num(x))\\n\\n # sorts df according to created_utc\\n df = df.sort_values(by=['date'])\\n\\n # get total number of comments\\n num_comments = len(df)\\n\\n # print(df['link_id'].nunique())\\n\\n df_link_id_group = df.groupby('link_id').agg({'vader_compound_score': ['mean'], 'vader_negative_score': ['mean'],\\n 'vader_neutral_score': ['mean'], 'vader_positive_score': ['mean'],\\n 'whole_comment_sentiment_flair': ['mean'],\\n 'date': ['min']}).copy()\\n\\n df_link_id_group.columns = ['avg_vader_compound_score', 'avg_vader_negative_score',\\n 'avg_vader_neutral_score', 'avg_vader_positive_score',\\n 'avg_whole_comment_sentiment_flair', 'date']\\n\\n return df_link_id_group\",\n \"def execQ10():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n banana = frame[(dFrame.Series_title_1 == \\\"Bananas, 1kg\\\") & (dFrame.Period >= 2013.01) & (dFrame.Period < 2014.01)]\\n average = banana['Price'].mean()\\n return average\",\n \"def average_speed(self): # pylint: disable=no-self-use\\n query = read_sql(\\\"ave_speed.sql\\\")\\n ave_speed_df = get_dataframe_from_bigquery(query, multipart=True)\\n ave_speed_df.pipe(save_to_gcs, settings.ASSETS.FILES.AVESPEED)\",\n \"def get_forecast_summary(self):\\n sum = {\\\"prior_var\\\":[], \\\"post_var\\\":[], \\\"percent_reduction\\\":[]}\\n for forecast in self.prior_forecast.keys():\\n pr = self.prior_forecast[forecast]\\n pt = self.posterior_forecast[forecast]\\n ur = 100.0 * (1.0 - (pt/pr))\\n sum[\\\"prior_var\\\"].append(pr)\\n sum[\\\"post_var\\\"].append(pt)\\n sum[\\\"percent_reduction\\\"].append(ur)\\n return pd.DataFrame(sum,index=self.prior_forecast.keys())\",\n \"def total(evictiondata):\\r\\n total = 0\\r\\n for index, row in evictiondata.iterrows():\\r\\n total += row['filings_2020']\",\n \"def get_product_means(df):\\n try:\\n mean_dataframe = df.groupby(['asin'])['overall'].mean()\\n print mean_dataframe[:10]\\n write_df_tocsv(mean_dataframe, 'product_means.csv')\\n return mean_dataframe\\n except Exception as e:\\n print \\\"Error getting product means\\\"\\n print str(e)\\n pass\",\n \"def _summarize_results(df, metrics):\\n def return_cm(x):\\n if isinstance(x, int):\\n return (0, 0, 0)\\n\\n elif len(x) > 3:\\n return x[1:]\\n\\n return x\\n\\n def get_status(x):\\n return {\\n \\\"OK\\\": 0,\\n \\\"ERROR\\\": 1\\n }[x]\\n\\n df['status'] = df['status'].apply(get_status)\\n df['confusion_matrix'] = df['confusion_matrix'].apply(ast.literal_eval)\\n df['confusion_matrix'] = df['confusion_matrix'].apply(return_cm)\\n df[['fp', 'fn', 'tp']] = pd.DataFrame(df['confusion_matrix'].tolist(), index=df.index)\\n\\n # calculate f1 score\\n df_ = df.groupby(['dataset', 'pipeline'])[['fp', 'fn', 'tp']].sum().reset_index()\\n\\n precision = df_['tp'] / (df_['tp'] + df_['fp'])\\n recall = df_['tp'] / (df_['tp'] + df_['fn'])\\n df_['f1'] = 2 * (precision * recall) / (precision + recall)\\n\\n result = dict()\\n\\n # number of wins over ARIMA\\n arima_pipeline = 'arima'\\n intermediate = df_.set_index(['pipeline', 'dataset'])['f1'].unstack().T\\n arima = intermediate.pop(arima_pipeline)\\n\\n result['# Wins'] = (intermediate.T > arima).sum(axis=1)\\n result['# Wins'][arima_pipeline] = None\\n\\n # number of anomalies detected\\n result['# Anomalies'] = df_.groupby('pipeline')[['tp', 'fp']].sum().sum(axis=1).to_dict()\\n\\n # average f1 score\\n result['Average F1 Score'] = df_.groupby('pipeline')['f1'].mean().to_dict()\\n\\n # failure rate\\n result['Failure Rate'] = df.groupby(\\n ['dataset', 'pipeline'])['status'].mean().unstack('pipeline').T.mean(axis=1)\\n\\n result = pd.DataFrame(result)\\n result.index.name = 'pipeline'\\n result.reset_index(inplace=True)\\n\\n rank = 'Average F1 Score'\\n result = _sort_leaderboard(result, rank, metrics)\\n result = result.drop('rank', axis=1).set_index('pipeline')\\n\\n return result\",\n \"def mape(actual: np.ndarray, predicted: np.ndarray):\\n return np.mean(np.abs(_percentage_error(actual, predicted)))\",\n \"def obtain_monthly_mean(data=pd.DataFrame()):\\n return data.resample(\\\"MS\\\").mean()\",\n \"def mean_average_precision(sort_data):\\n count_1 = 0\\n sum_precision = 0\\n for index in range(len(sort_data)):\\n if sort_data[index][1] == 1:\\n count_1 += 1\\n sum_precision += 1.0 * count_1 / (index + 1)\\n return sum_precision / count_1\",\n \"def strikeout_percentage_average(df,start_year, end_year,bat_met, player_name):\\n\\n base_fields = ['PA']\\n emp_list = check_base_fields(df,base_fields)\\n\\n if not emp_list:\\n k_val = round((pd.to_numeric(df['K.'].str.split('%').str[0])/100)*df['PA'],0).sum()\\n pa_total = df['PA'].fillna(0).sum()\\n return \\\"{:.2%}\\\".format(k_val / pa_total)\\n else:\\n df = original_dataframe(start_year,end_year,bat_met+emp_list,player_name)\\n return strikeout_percentage_average(df,start_year, end_year,bat_met, player_name)\",\n \"def depart_arrive_stats_by_month(flights):\\n\\n return ...\",\n \"def compute_metrics(self):\\n overall_ret = OrderedDict()\\n for ap_iou_thresh in self.ap_iou_thresh:\\n ret_dict = OrderedDict()\\n rec, prec, ap = eval_det_multiprocessing(self.pred_map_cls, self.gt_map_cls, ovthresh=ap_iou_thresh)\\n for key in sorted(ap.keys()):\\n clsname = self.class2type_map[key] if self.class2type_map else str(key)\\n ret_dict[\\\"%s Average Precision\\\" % (clsname)] = ap[key]\\n ap_vals = np.array(list(ap.values()), dtype=np.float32)\\n ap_vals[np.isnan(ap_vals)] = 0\\n ret_dict[\\\"mAP\\\"] = ap_vals.mean()\\n rec_list = []\\n for key in sorted(ap.keys()):\\n clsname = self.class2type_map[key] if self.class2type_map else str(key)\\n try:\\n ret_dict[\\\"%s Recall\\\" % (clsname)] = rec[key][-1]\\n rec_list.append(rec[key][-1])\\n except:\\n ret_dict[\\\"%s Recall\\\" % (clsname)] = 0\\n rec_list.append(0)\\n ret_dict[\\\"AR\\\"] = np.mean(rec_list)\\n overall_ret[ap_iou_thresh] = ret_dict\\n return overall_ret\",\n \"def evaluate_percentage_of_class_for_each_flight(df_flights_info):\\n df_new = df_flights_info[['FlightId', 'FareClass', 'Booking']]\\n df_new = df_new.pivot(index='FlightId', columns='FareClass', values='Booking').astype(float)\\n df_new.loc[:, 'Total'] = df_new.sum(axis=1).astype(float)\\n for row, col in df_new.iterrows():\\n for item in list(df_new):\\n number_booking = df_new.loc[row, item]\\n total_booking = df_new.loc[row]['Total']\\n percentage = float(number_booking / total_booking)\\n df_new.at[row, item] = percentage\\n df_new = df_new.drop(columns=['Total'])\\n df_new = df_new.stack()\\n df_new = df_new.reset_index(level=[0, 1])\\n df_flights_info = pandas.merge(df_flights_info, df_new, how='left', on=['FlightId', 'FareClass'])\\n df_flights_info.rename(columns={0: 'Percentage'}, inplace=True)\\n return df_flights_info\",\n \"def generate_admission_prediction():\\n query = \\\"SELECT * FROM admissions_data\\\"\\n result = general_db_func.query_database(query)\\n admission_data = pd.DataFrame(result)\\n admission_data.rename(columns={0: \\\"year\\\", 1: \\\"university\\\", 2: \\\"admissions\\\"}, inplace=True)\\n admission_data = admission_data.sort_values(by=[\\\"university\\\", \\\"year\\\"], ascending=True)\\n admission_data = admission_data.groupby(\\\"university\\\")\\n admission_data = [admission_data.get_group(x) for x in admission_data.groups]\\n\\n for uni in admission_data:\\n uni_name = list(uni[\\\"university\\\"])[0]\\n uni = uni.drop(columns=[\\\"university\\\"])\\n uni.set_index(uni.year, inplace=True)\\n\\n historic_points = generate_admission_year_points()\\n historic_admissions = fill_none_admissions(uni[\\\"admissions\\\"].tolist())\\n\\n historic_data = (historic_points, historic_admissions)\\n\\n try:\\n # Generate autoregressive model\\n model = AR(uni[\\\"admissions\\\"])\\n model_fit = model.fit()\\n\\n # predict next 2 years\\n prediction_points = generate_admission_year_points(future=True)\\n predicted_admissions = model_fit.predict(len(uni[\\\"year\\\"]), len(uni[\\\"year\\\"])+1).to_list()\\n predicted_data = (prediction_points, predicted_admissions)\\n insert_predicted_admission(uni_name, historic_data, predicted_data)\\n except ValueError as e:\\n print(e)\",\n \"def average_current_salaries(employees_list):\\n # data frame for all employees\\n df = pd.DataFrame(employees_list)\\n df['date'] = pd.to_datetime(df['date'])\\n\\n # sort by employees and date, keeping only the most recent entry\\n # for a given employee\\n df = df.sort_values(\\n ['employee', 'date'], ascending=[True, False]).drop_duplicates(\\n subset='employee', keep='first')\\n\\n # compute mean salary for each department, grouped by gender\\n df = df[['dept', 'salary', 'gender']].groupby(['dept',\\n 'gender']).agg('mean')\\n\\n mean_salaries = defaultdict(dict)\\n for ((dept, gender), mean_salary) in df['salary'].to_dict().items():\\n mean_salaries[dept][gender] = round(mean_salary, 2)\\n\\n return dict(mean_salaries)\",\n \"def get_totals_across_years(df, tract_col, data_col):\\n return df.groupby(tract_col)[data_col].agg([\\\"sum\\\", \\\"mean\\\"]).reset_index()\",\n \"def get_avg_pka(predicted_pkas):\\n # Use a stupid (but effective in this case) test for infinity\\n sm, np = 0, 0\\n for val in predicted_pkas:\\n if abs(val) > 99999999.0: continue\\n sm += val\\n np += 1\\n if np == 0: return 1\\n return sm / np\",\n \"def add_average_discount_to_target(data):\\n\\n data['adj_price'] = data.price.map(lambda x: (x - x * 0.05))\\n data['adj_price_sqrm'] = data.price_sqrm.map(lambda x: (x - x * 0.05))\\n\\n return data\",\n \"def create_avg_speed_cols(df):\\n #create speed column. this should be correlated with the day component\\n #and may give additional insight\\n df['avg_speed_haversine'] = 1000*df['distance'].values/df['trip_duration']\\n df['avg_speed_manhattan'] = 1000*df['manhattan_distance'].values/df['trip_duration']\\n\\n return df\",\n \"def analyze_predictions(submission_model_path, csv_result):\\n all_data_info = pd.read_csv(path.join(DATA_PATH, 'all_data_info.csv'))\\n submission_model = pd.read_csv(submission_model_path)\\n\\n genre_label = get_genre_labels(True)\\n null_genre = 0\\n null_label = 0\\n exact_labels = []\\n errors = 0\\n progress = progressbar.ProgressBar(\\n maxval=len(submission_model),\\n widgets=[progressbar.Bar('=', '[', ']'), ' ',\\n progressbar.Percentage()])\\n progress.start()\\n for i, row in submission_model.iterrows():\\n progress.update(i + 1)\\n genre = all_data_info[all_data_info['new_filename'] == row['img']][\\n 'genre'].dropna()\\n # some paintings don't have a genre. Checking it\\n if len(genre) < 1:\\n null_genre += 1\\n exact_labels.append(row['label'])\\n continue\\n if genre.values[0] not in genre_label:\\n # No label. It's strange, but let's go on...\\n null_label += 1\\n exact_labels.append(row['label'])\\n continue\\n exact_labels.append(int(genre_label[genre.values[0]]['label']))\\n if row['label'] != exact_labels[-1]:\\n errors += 1\\n progress.finish()\\n\\n submission_model['exact_label'] = exact_labels\\n submission_model.to_csv(csv_result, index=False)\\n\\n # plot_confusion_matrix(submission_model)\\n\\n print('Genre is null: ' + str(null_genre))\\n print('Label is null: ' + str(null_label))\\n print('Errors: ' + str(errors) + ' (out of ' + str(len(submission_model)) +\\n ')')\\n print('Accuracy: ' + str(1 - (errors / float(len(submission_model)))))\",\n \"def reduce_data():\\n snapshots = Snapshot.objects.all()\\n locations = Location.objects.all()\\n lst = []\\n for snapshot in snapshots:\\n lst.append([snapshot.location.name, snapshot.avail_bikes,\\n snapshot.free_stands, snapshot.timestamp])\\n cols = ['location', 'avail_bikes', 'free_stands', 'timestamp']\\n df = pd.DataFrame(lst, columns=cols)\\n df['time'] = df['timestamp'].dt.round('30min').dt.strftime('%H:%M')\\n\\n group = df.groupby(['location', 'time'])\\n means = group.mean()\\n sd = group.std()\\n today = date.today()\\n first = today.replace(day=1)\\n last_month = first - timedelta(days=1)\\n\\n for name, time in means.index:\\n subset_mean = means.xs((name, time), level=(0, 1), axis=0)\\n subset_sd = sd.xs((name, time), level=(0, 1), axis=0)\\n m = Stat.objects.get_or_create(\\n location=locations.get(name=name),\\n avail_bikes_mean=subset_mean['avail_bikes'],\\n free_stands_mean=subset_mean['free_stands'],\\n avail_bikes_sd=subset_sd['avail_bikes'],\\n free_stands_sd=subset_sd['free_stands'],\\n time=time,\\n month=last_month\\n )\\n\\n # snaps = Snapshot.objects.all()\\n # i = 0\\n # length = len(snaps)\\n # for s in snaps:\\n # i += 1\\n # print(i)\\n # if i > 35000:\\n # s.save()\\n # reduce_data()\",\n \"def _predict(self, fh, X):\\n names, _ = self._check_forecasters()\\n y_pred = pd.concat(self._predict_forecasters(fh, X), axis=1, keys=names)\\n y_pred = y_pred.groupby(level=1, axis=1).agg(\\n _aggregate, self.aggfunc, self.weights\\n )\\n return y_pred\",\n \"def zonal_stats_workflow():\\n save_as = \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/summary/monthly_quickflow.csv\\\"\\n scenario_dict = {\\n 'pre-decline': \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/pre_decline\\\",\\n 'post-decline': \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/post_decline\\\",\\n }\\n df_list = []\\n for scenario in scenario_dict.iterkeys():\\n results_dict = {\\n 'scenario': [],\\n 'month': [],\\n 'sum_quickflow': [],\\n }\\n folder = scenario_dict[scenario]\\n aoi_shp = os.path.join(folder, 'aggregated_results.shp')\\n for month in xrange(1, 13):\\n qf_raster = os.path.join(\\n folder, 'intermediate_outputs', 'qf_{}.tif'.format(month))\\n zonal_stats = pygeoprocessing.zonal_statistics(\\n (qf_raster, 1), aoi_shp)\\n sum_QF = zonal_stats[0]['sum']\\n results_dict['scenario'].append(scenario)\\n results_dict['month'].append(month)\\n results_dict['sum_quickflow'].append(sum_QF)\\n results_df = pandas.DataFrame(data=results_dict)\\n df_list.append(results_df)\\n combined_list = pandas.concat(df_list)\\n combined_list.to_csv(save_as, index=False)\",\n \"def averaged_impact(impact, normalize=True):\\n impact = pandas.DataFrame(impact)\\n average = pandas.Series(index=impact.columns, dtype=float)\\n for col in impact:\\n average[col] = impact[col].mean()\\n if normalize:\\n average /= average.sum()\\n return average\",\n \"def get_popularity(rest_data, item_dict):\\n max_review_count = rest_data.review_count.max()\\n min_review_count = rest_data.review_count.min()\\n result = np.zeros((len(rest_data), 2))\\n for i in range(len(rest_data)):\\n result[i, 0] = item_dict[rest_data.business_id[i]]\\n result[i, 1] = (((rest_data.review_count[i] - min_review_count)/(max_review_count - min_review_count))*4 + 1)\\n result = result[result[:, 0].argsort()]\\n return result\",\n \"def predict_progression(final):\\n #Import the dataset for prediction\\n dataset = read_excel('SampleProgressionData.xlsx', sheet_name=0)\\n \\\"\\\"\\\"\\n Split the data into a training and a testing set\\n \\\"\\\"\\\"\\n train_features = dataset.iloc[0:197,:-1]\\n train_targets = dataset.iloc[0:197,-1]\\n i=0\\n df_cluster_data_col = {}\\n Dict_progression = {} \\n Dict_rec_progression = {} \\n while i < 5:\\n # Number of employees in this cluster\\n array_n = final[final.cluster == i][\\\"EmployeeID\\\"].unique()\\n len(array_n)\\n print(\\\"cluster \\\", i )\\n print(\\\"Employees: \\\", array_n)\\n # List of employees\\n final[final.cluster == i]\\n cluster_data_n=final[final.cluster == i]\\n cluster_data_n=cluster_data_n[0:len(array_n)]\\n # cluster_data_n\\n df_cluster_data_n=pd.DataFrame(cluster_data_n, columns = ['EmployeeID', 'MajorityBand','CurrentBand','NumberYears','Progression']) \\n df_cluster_data_n['MajorityBand']=df_cluster_data_n['CurrentBand'].max()\\n df_cluster_data_n.fillna(0, inplace=True)\\n dataset_cluster_n = pd.DataFrame(df_cluster_data_n,columns = ['EmployeeID','MajorityBand', 'CurrentBand','NumberYears','Progression'])\\n test_features = dataset_cluster_n.iloc[:,1:4]\\n test_targets = dataset_cluster_n.iloc[:,-1]\\n \\\"\\\"\\\"\\n Train the model\\n \\\"\\\"\\\"\\n tree = DecisionTreeClassifier(criterion = 'entropy').fit(train_features,train_targets)\\n \\\"\\\"\\\"\\n Predict the classes of new, unseen data\\n \\\"\\\"\\\"\\n prediction = tree.predict(test_features)\\n \\\"\\\"\\\"\\n Check the accuracy\\n \\\"\\\"\\\"\\n print(\\\"The prediction accuracy is: \\\",tree.score(test_features,test_targets)*100,\\\"%\\\")\\n df_cluster_data_n['Progression']=list(prediction)\\n strmax = df_cluster_data_n['CurrentBand'].max()\\n print(\\\"strmax \\\", strmax)\\n print(\\\"prediction \\\", prediction)\\n print('---------------------------------------------------------------------')\\n print(\\\"PREDICTION FOR BAND \\\", strmax)\\n print('---------------------------------------------------------------------')\\n df_cluster_data_n = df_cluster_data_n.loc[df_cluster_data_n[\\\"Progression\\\"]==1]\\n df_cluster_data_empid=pd.DataFrame(df_cluster_data_n, columns = ['EmployeeID'])\\n df_cluster_data_col[i]=df_cluster_data_empid\\n Dict_progression[strmax]=list(df_cluster_data_empid['EmployeeID'].unique())\\n print(\\\"Dict_progression \\\", Dict_progression)\\n i=i+1\\n # Show the next promotion band and not the cluster highest band for recommendation to employees\\n list_promotion = Dict_progression[strmax]\\n for emp_band_promo in list_promotion:\\n array = df_cluster_data_n.loc[df_cluster_data_n['EmployeeID'] == emp_band_promo]['CurrentBand'].unique()\\n promotion_band = array[0]+1 \\n list_emp_band = []\\n if promotion_band in Dict_rec_progression:\\n list_emp_band = Dict_rec_progression[promotion_band]\\n list_emp_band.append(emp_band_promo) \\n list_emp_band.sort()\\n Dict_rec_progression[promotion_band] = list_emp_band\\n print(\\\"Dict_progression\\\", Dict_progression)\\n print(\\\"Dict_rec_progression\\\", Dict_rec_progression) \\n return Dict_rec_progression\",\n \"def purity_score(label, pred):\\n \\n df = pd.concat([label, pd.DataFrame(pred)], axis=1)\\n df.set_axis(['label', 'pred'], axis=1, inplace=True)\\n \\n s = 0\\n\\n for x, cluster in df.groupby('pred'):\\n s += cluster['label'].value_counts().iloc[0] # adding the most occuring class in a cluster\\n\\n return s / label.shape[0]\",\n \"def weighted_average(model_df,cols):\\n #wa = model_df[cols].apply(lambda x: (x[0]*x[1]).sum()/x[0].sum())\\n wa = (model_df[cols[0]]*model_df[cols[1]]).sum()/model_df[cols[0]].sum()\\n return wa\",\n \"def predictions(dataframe):\\n # Select Features\\n features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']]\\n\\n # Add UNIT to features using dummy variables\\n dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\\n features = features.join(dummy_units)\\n\\n # Values\\n values = dataframe['ENTRIESn_hourly']\\n m = len(values)\\n\\n features, mu, sigma = normalize_features(features)\\n features['ones'] = np.ones(m) # Add a column of 1s (y intercept)\\n\\n # Convert features and values to numpy arrays\\n features_array = np.array(features)\\n values_array = np.array(values)\\n\\n # Set values for alpha, number of iterations.\\n alpha = 0.1 # please feel free to change this value\\n num_iterations = 75 # please feel free to change this value\\n\\n # Initialize theta, perform gradient descent\\n theta_gradient_descent = np.zeros(len(features.columns))\\n theta_gradient_descent, cost_history = gradient_descent(features_array,\\n values_array,\\n theta_gradient_descent,\\n alpha,\\n num_iterations)\\n\\n plot = None\\n # -------------------------------------------------\\n # Uncomment the next line to see your cost history\\n # -------------------------------------------------\\n # plot = plot_cost_history(alpha, cost_history)\\n #\\n # Please note, there is a possibility that plotting\\n # this in addition to your calculation will exceed\\n # the 30 second limit on the compute servers.\\n\\n predicted = np.dot(features_array, theta_gradient_descent)\\n return predicted, plot\",\n \"def at_birth(df,variable,npoint):\\n return df.groupby('cell')[['{}'.format('{}'.format(variable)),'pred_growth_rate']].apply(lambda x: x.head(npoint).mean()).rename(columns={'pred_length_box_um':'{}_at_birth'.format(variable)})\",\n \"def plot_average_impl(df, regexps, y_value='return_mean', window=1, agg='mean', \\n x_value='frames'):\\n df = df.dropna(subset=[y_value])\\n\\n unique_models = df['model'].unique()\\n model_groups = [[m for m in unique_models if re.match(regex, m)]\\n for regex in regexps]\\n\\n for regex, models in zip(regexps, model_groups):\\n df_re = df[df['model'].isin(models)]\\n # the average doesn't make sense if most models are not included,\\n # so we only for the period of training that has been done by all models\\n num_frames_per_model = [df_model[x_value].max()\\n for _, df_model in df_re.groupby('model')]\\n median_progress = sorted(num_frames_per_model)[(len(num_frames_per_model) - 1) // 2]\\n mean_duration = np.mean([\\n df_model['duration'].max() for _, df_model in df_re.groupby('model')])\\n df_re = df_re[df_re[x_value] <= median_progress]\\n\\n # smooth\\n parts = []\\n for _, df_model in df_re.groupby('model'):\\n df_model = df_model.copy()\\n df_model.loc[:, y_value] = df_model[y_value].rolling(window).mean()\\n parts.append(df_model)\\n df_re = pandas.concat(parts)\\n\\n df_agg = df_re.groupby([x_value]).agg([agg])\\n values = df_agg[y_value][agg]\\n pyplot.plot(df_agg.index, values, label=regex)\\n print(regex, median_progress, mean_duration / 86400.0, values.iloc[-1])\",\n \"def avg_pay(input: list) -> float:\\n pay = 0\\n for emp in input:\\n pay += emp.get_salary()\\n return pay / len(input)\",\n \"def avg_ttm_2y(df):\\n return 0.5 * (df + df.shift(4))\"\n]","isConversational":false},"negative_scores":{"kind":"list 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predictions from an ARIMAX model at the month level, as well as the number of months into the future to predict."},"document":{"kind":"string","value":"def top_down_forecast(original_df,predictions_by_month,months_ahead):\n\n group_by_zip_df = top_down_forecast_data_processing(original_df,predictions_by_month)\n\n zip_perc_df= pd.DataFrame(np.random.randn(1, 4), columns=['zip_predicted','zip_code','perc_of_month','month_year'])\n\n for month in range(months_ahead):\n array_of_predictions = np.asarray([predictions_by_month.iloc[month]['predicted_evictions']]*group_by_zip_df.shape[0])\n zip_predicted = np.asarray(group_by_zip_df.perc_of_month*array_of_predictions)\n temp_df = pd.DataFrame({'total_evictions':array_of_predictions,'zip_predicted':zip_predicted,'zip_code':np.asarray(group_by_zip_df.Address_Zipcode), 'perc_of_month':np.asarray(group_by_zip_df.perc_of_month)})\n temp_df['month_year']=predictions_by_month.iloc[month]['month_year']\n zip_perc_df = zip_perc_df.append(temp_df,ignore_index=True)\n\n zip_perc_df = zip_perc_df[1:]\n zip_perc_df['month_year']=pd.to_datetime(zip_perc_df['month_year'])\n\n return zip_perc_df"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\n columns=['month_year', 'predicted_evictions'])\n\n now = datetime.datetime.now()\n\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\n model.fit()\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\n\n predicted = y_hat.Eviction_Notice.values.tolist()\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\n temp_df['month_year']=months_list\n\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\n predictions_df = predictions_df[1:]\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\n\n return predictions_df, y_hat","def arimax_by_month_train_test(model_data,future_data,months_ahead):\n transformed_df = arima_by_zip_data_transform(model_data)\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\n\n y_train = transformed_df\n\n\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]\n\n now = datetime.datetime.now()\n this_month = pd.to_datetime(str(now.year)+ '-'+ str(now.month) + '-'+str(1))\n y_test=y_test[(y_test.Month_Year>this_month)&(y_test.Month_Year<(this_month+pd.offsets.MonthBegin(months_ahead+1)))].sort_values('Month_Year')\n months_list = np.asarray(y_test.Month_Year)\n\n y_test = y_test.set_index(['Month_Year'],inplace=False)\n\n\n for i,row in y_train.Eviction_Notice.iteritems():\n if row>(y_train.Eviction_Notice.mean()+ y_train.Eviction_Notice.std()*3):\n y_train.Eviction_Notice.iloc[i]=y_train.Eviction_Notice.mean()\n\n y_train = y_train[['Month_Year','Eviction_Notice','CASANF0URN']].set_index(['Month_Year'],inplace=False)\n\n return y_train, y_test,months_list","def get_predictions(year, month):\n \n start_date = str(year)+\"-\"+str(month)+\"-01\"\n end_date = str(year)+\"-\"+str(month)+\"-\"+str(monthrange(year, month)[1])\n\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\"%Y-%m-%d\").tolist()\n\n # predictfunction \n # do predictions\n pred_arr = []\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\n \n try:\n predictions = load_predictions(file_name)\n predictions = predictions.round()\n except:\n print(\"An exception occurred\")\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\n \n \n for index,row in predictions.iterrows():\n \n pred_mail = 0\n pred_counter = 0\n pred_tel = 0\n \n # check predictions dataframe for 'Datum'\n if 'Datum' in predictions.columns:\n date = row['Datum']\n else:\n break;\n\n # check predictions dataframe for 'Mail'\n if 'Mail' in predictions.columns:\n pred_mail = row['Mail']\n\n # check predictions dataframe for 'Schalter'\n if 'Schalter' in predictions.columns:\n pred_counter = row['Schalter']\n\n # check predictions dataframe for 'Tel'\n if 'Tel' in predictions.columns:\n pred_tel = row['Tel']\n \n \n pred_dict = {'date': date, \n 'predictions':{'mail' : pred_mail, \n 'tel' : pred_tel, \n 'counter' : pred_counter\n }\n }\n\n pred_arr.append(pred_dict)\n\n print(pred_arr) \n \n return pred_arr","def top_down_forecast_data_processing(original_df,predictions_by_month):\n transformed_df=original_df.groupby('Month_Year').sum().reset_index()\\\n [['Month_Year','Eviction_Notice']]\n\n percentage_of_month_df = pd.merge(original_df[['Month_Year','Address_Zipcode',\\\n 'Eviction_Notice']],transformed_df,how = 'left',left_on='Month_Year',\\\n right_on='Month_Year',suffixes=('','_for_month'))\n\n percentage_of_month_df.dropna(inplace=True)\n\n percentage_of_month_df['perc_of_month']=\\\n percentage_of_month_df['Eviction_Notice']/percentage_of_month_df['Eviction_Notice_for_month']\n\n group_by_zip_df = percentage_of_month_df.groupby('Address_Zipcode').mean().reset_index()[['Address_Zipcode','perc_of_month']]\n\n return group_by_zip_df","def predict_sales() -> str:\r\n # HTML tables and list that are filled depending on the user input.\r\n html_gr_month_table = app.config[\"table_monthly_growth\"]\r\n html_gr_week_table = app.config[\"table_weekly_growth\"]\r\n html_csv_list = \"\"\r\n # Empty filename and path to sales forecast graph; also dep. on user input.\r\n graph_filename = \"\"\r\n graph_filepath = \"'File has not been created yet!'\"\r\n # Contains HTML form data inputted by the user submitted using POST.\r\n response = request.form\r\n num_months_input = response.get(\"num_months_input\")\r\n csv_filename_input = response.get(\"csv_filename_input\")\r\n # True if user inputted num of past months for prediction and uploaded CSV.\r\n if num_months_input is not None and csv_filename_input is not None:\r\n # Loads data from CSV into dataframe & removes rows with missing values\r\n file_name = csv_filename_input\r\n dataframe = load_csv_clean_to_dataframe(file_name)\r\n try:\r\n # Replaces index numbers of df with datetimes to get a time series.\r\n dataframe = convert_to_time_series(dataframe)\r\n except KeyError as error:\r\n error_message = (\"The following column name is missing \"\r\n \"in the data: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Calculates Quantity ordered averages for every past week.\r\n column_to_avg = \"Quantity ordered\"\r\n average_by = \"W\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\r\n # Calculates Quantity ordered averages for every past month.\r\n average_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_month = average_data(dataframe, column_to_avg,\r\n average_by)\r\n # Calculates past Quantity ordered average growth rates.\r\n growth_rates_per_week = past_avgs_per_week.pct_change()\r\n growth_rates_per_month = past_avgs_per_month.pct_change()\r\n # Creates HTML table containing past monthly sales growth rates.\r\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\r\n app.config[\"table_monthly_growth\"] = html_gr_month_table\r\n # Creates HTML table containing past weekly sales growth rates.\r\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\r\n app.config[\"table_weekly_growth\"] = html_gr_week_table\r\n # Filters dataframe for beer types.\r\n dunkel = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Dunkel\"]\r\n pilsner = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Pilsner\"]\r\n red_helles = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Red Helles\"]\r\n # Monthly basis;\r\n # calculates dunkel's Quantity ordered sums for each month.\r\n column_to_sum = \"Quantity ordered\"\r\n sum_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\r\n # Calculates pilsner's Quantity ordered sums for each month.\r\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\r\n # Calculates red_helles' Quantity ordered sums for each month.\r\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\r\n all_order_sum_month = {\"dunkers\": dunk_order_sum_per_month,\r\n \"pilsner\": pils_order_sum_per_month,\r\n \"red_helles\": redh_order_sum_per_month}\r\n # Clears monthly sales forecasts from the dictionary.\r\n app.config[\"monthly_sales_forecasts\"] = {}\r\n # Hyperparameters (hp) for seasonal ARIMA model.\r\n order = app.config[\"order\"]\r\n # Sets num_months_in. as num of past periods to be considered by model.\r\n app.config[\"seasonal_order\"][3] = int(num_months_input)\r\n seasonal_order = app.config[\"seasonal_order\"]\r\n # Fits ARIMA model and forecasts monthly sales for each beer type.\r\n try:\r\n for beer_type in all_order_sum_month:\r\n model = define_and_fit_model(all_order_sum_month[beer_type],\r\n False, order, seasonal_order)\r\n # Forecasts 12 months into the future with the fitted model.\r\n forecast = model.get_forecast(steps=12)\r\n # Stores monthly sales forecasts in dictionary by beer_type.\r\n app.config[\"monthly_sales_forecasts\"][beer_type] = forecast\r\n # Plots actual and predicted num of beers sold and saves plot.\r\n plot_size = (15, 10)\r\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\r\n plot_size, beer_type)\r\n except ValueError as error:\r\n error_message = (\"Wrong hyperparameters are set, the model \"\r\n + \"can not be fitted: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Gets filepath of this module and thus filepath to graph (png file).\r\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\r\n graph_filename = \"monthly_forecast_and_past.png\"\r\n try:\r\n # Opens plotted graph.\r\n img = Image.open(graph_filename)\r\n img.show()\r\n except (FileNotFoundError, PermissionError) as error:\r\n app.config[\"logger\"].error(error)\r\n # Finds names of all uploaded CSV files in folder where this module is.\r\n all_uploaded_csv = []\r\n for csv_file in glob.glob(\"*.csv\"):\r\n all_uploaded_csv.append(csv_file)\r\n # Creates HTML drop-down list containing all uploaded CSV filenames.\r\n html_csv_list = update_csv_list(all_uploaded_csv)\r\n return (\"\"\"<style>\r\n h1, h2, h3 {\r\n font-family: arial, sans-serif;\r\n }\r\n table {\r\n font-family: arial, sans-serif;\r\n border-collapse: collapse;\r\n width: 100%;\r\n }\r\n td, th {\r\n border: 1px solid #dddddd;\r\n text-align: left;\r\n padding: 8px;\r\n }\r\n tr:nth-child(even) {\r\n background-color: #dddddd;\r\n }\r\n </style>\r\n <h2>Predict sales</h2>\r\n <form action=\"/predict_sales\" method=\"POST\">\r\n Please select an uploaded CSV file that should be used for the \r\n sales forecast (if the list is empty, no CSV file has been \r\n uploaded yet; to upload a file, please go back to the \r\n tracking screen).<br>\r\n <select name=\"csv_filename_input\">\"\"\"\r\n + html_csv_list\r\n + \"\"\"</select><br><br>\r\n Please enter how many <b>months</b> of past data should be\r\n used for the sales forecast:<br>\r\n (Note: If the number of past periods is higher than the number\r\n of periods in the past sales data, the nonexistent periods are\r\n recorded as 0 sales, which, if too many periods are 0,\r\n makes the prediction zero.\r\n <br>\r\n <input type=\"number\" name=\"num_months_input\" min=\"2\"\r\n required=\"required\">\r\n <br><br>\r\n <input type=\"submit\" value=\"Predict sales\">\r\n </form>\r\n In case the graph is not displayed, please open the file \"\"\"\r\n + graph_filename + \" manually using the path: \"\r\n + graph_filepath\r\n + \"\"\"<form action=\"/\" method=\"POST\">\r\n <input type=\"hidden\">\r\n <br>\r\n <input type=\"submit\" value=\"Go back to tracking screen\">\r\n </form> \r\n <h3>Average sales growth rates of the past months</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Monthly average</th>\r\n </tr>\"\"\" + html_gr_month_table + \"</table>\"\r\n + \"\"\"<h3>Average sales growth rates of the past weeks</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Weekly average</th>\r\n </tr>\"\"\" + html_gr_week_table + \"</table>\")","def _forecast(model, predict_steps):\n\n pred_uc = model.get_forecast(steps=predict_steps)\n\n # Produce the forecasted tables\n predicted_mean_df = pred_uc.predicted_mean.reset_index()\n predicted_mean_df.columns = ['Date', 'Predicted_Mean']\n return predicted_mean_df","def test_model_avg_3_weeks_bt(df_y_train, df_y_predict, df_y_ground_truth_scaled, predict_period, n_samples_week, target):\n\n # Use average of last 3 weeks (for same time stamps) as predicted values\n df_hist = pd.concat([df_y_train, df_y_ground_truth_scaled], 0)\n df_hist_1week = df_hist.shift(n_samples_week)\n df_hist_2week = df_hist.shift(2*n_samples_week)\n df_hist_3week = df_hist.shift(3*n_samples_week)\n df_hist_all = pd.concat([df_hist_1week, df_hist_2week, df_hist_3week], 1)\n df_hist_all = df_hist_all[df_hist_all.index.isin(df_y_predict.index)]\n \n if target == \"count\":\n # Average\n prediction = df_hist_all.mean(axis = 1)\n elif target == \"level\":\n # Majority class\n prediction = df_hist_all.mode(axis = 1).iloc[:, 0]\n \n return prediction","def forecast_ARIMA(train, test, p, d, q, model_selection, prod_num):\n model = ARIMA(train['STU'].dropna(), order=(p, d, q))\n model_fit = model.fit(disp=0)\n if model_selection:\n forecast, st_errs, conf_int = model_fit.forecast(len(test), alpha=0.05)\n else:\n predictions_range = pd.date_range('2019-04-01', '2019-09-29', freq=\"W-MON\").to_series()\n\n df_predictions = pd.DataFrame(predictions_range, columns=[\"Week\"])\n\n forecast, st_errs, conf_int = model_fit.forecast(26, alpha=0.05)\n df_predictions['Sales_Prediction'] = forecast\n df_predictions['Week'] = pd.to_datetime(df_predictions[\"Week\"])\n df_predictions.set_index(\"Week\", inplace=True)\n\n plt.figure(figsize=(12, 5))\n plt.plot(train['STU'], label='training')\n plt.plot(df_predictions['Sales_Prediction'], label='forecast')\n plt.title('Forecast vs Actuals')\n plt.legend(loc='upper left', fontsize=8)\n plt.savefig(\"Product\" + f\"_{prod_num}\" + \".pdf\")\n if model_selection:\n MSE = mean_squared_error(test, forecast)\n return MSE\n return df_predictions","def predict(self, next_days):\n last_date = self.series.index[-1]\n for time in range(next_days):\n row = {}\n for c in self.countries:\n history = self.series[c]\n series = history.astype(float)\n model = ARIMA(series, order=(2,1,0))\n model_fit = model.fit()\n output = model_fit.forecast()\n row[c] = output[0][0]\n last_date = last_date + pd.DateOffset(1)\n self.series.loc[last_date] = row","def generate_admission_prediction():\n query = \"SELECT * FROM admissions_data\"\n result = general_db_func.query_database(query)\n admission_data = pd.DataFrame(result)\n admission_data.rename(columns={0: \"year\", 1: \"university\", 2: \"admissions\"}, inplace=True)\n admission_data = admission_data.sort_values(by=[\"university\", \"year\"], ascending=True)\n admission_data = admission_data.groupby(\"university\")\n admission_data = [admission_data.get_group(x) for x in admission_data.groups]\n\n for uni in admission_data:\n uni_name = list(uni[\"university\"])[0]\n uni = uni.drop(columns=[\"university\"])\n uni.set_index(uni.year, inplace=True)\n\n historic_points = generate_admission_year_points()\n historic_admissions = fill_none_admissions(uni[\"admissions\"].tolist())\n\n historic_data = (historic_points, historic_admissions)\n\n try:\n # Generate autoregressive model\n model = AR(uni[\"admissions\"])\n model_fit = model.fit()\n\n # predict next 2 years\n prediction_points = generate_admission_year_points(future=True)\n predicted_admissions = model_fit.predict(len(uni[\"year\"]), len(uni[\"year\"])+1).to_list()\n predicted_data = (prediction_points, predicted_admissions)\n insert_predicted_admission(uni_name, historic_data, predicted_data)\n except ValueError as e:\n print(e)","def feature_engineer_ts(self, month=12):\n st_data_dt = self.get_st_data_dt()\n end_data_dt = self.get_end_data_dt()\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\n population = self.get_population()\n is_raw_partition = self.get_is_raw_partition()\n# Lag 2 months\n all_data = []\n# join past is_raw columns\n for d in date_list:\n \n population_partition = population[population['ft_data_dt'] == d] \n old_date = d - relativedelta(months=month)\n y = old_date.year\n m = old_date.month\n day = calendar.monthrange(y, m)[1]\n old_date = date(y, m, day)\n old_date = max(old_date, st_data_dt)\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\n date_list_join.reverse()\n for index, date_join in enumerate(date_list_join):\n if date_join.strftime(\"%Y-%m-%d\") not in is_raw_partition.keys():\n continue\n \n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\"%Y-%m-%d\")]\n \n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\n new_col = [c+'_'+str(index+1) for c in rename_col]\n name_dict = dict(list(zip(rename_col, new_col)))\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\n all_data.append(population_partition)\n ts_df = pd.concat(all_data)\n threshold_null = len(ts_df.columns) - 4\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\n \n def sum_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_sum_'+str(duration)+'mth'\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def mean_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_avg_'+str(duration)+'mth'\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def std_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_std_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def med_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_med_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def min_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_min_'+str(duration)+'mth'\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def max_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_max_'+str(duration)+'mth'\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q1_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q1_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q3_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q3_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def last_ts(self, df, feature):\n ft_name = feature+ '_last'\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\n return tmp_df\n \n ts_duration = [1, 3, 6, 9, 12]\n feature_list = self.get_is_raw_col()\n df = ts_df[['idd', 'ft_data_dt']]\n# Time Series Features\n for duration in ts_duration:\n for col in feature_list:\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\n df = pd.concat([df\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\n , med_ts(self, ts_df, col_list, col, duration)\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\n , min_ts(self, ts_df, col_list, col, duration)\\\n , max_ts(self, ts_df, col_list, col, duration)]\n , axis=1)\n self.set_all_data(df)","def generate_time_series_df(eviction_df):\n evictions_by_month = get_counts_by_month(eviction_df, \"month\", \"total-eviction-filings\")\n timeseries_df = evictions_by_month\n return timeseries_df","def predict(self):\n # format data\n df = self.normalize(self.daily)\n x = df.index.astype(np.int64).values.reshape(-1, 1)\n y = self.normalize(df[['Adj Close']]).values\n\n # format time\n one_day_time = 86400000000000\n x_tomorrow = x[-1] + one_day_time\n x_incl_tomorrow = np.append(x, [x_tomorrow], axis=0)\n dates = pd.to_datetime(x_incl_tomorrow.reshape(-1))\n\n # average the predictions\n lin_reg = self.linear_regression(x, y, x_tomorrow, x_incl_tomorrow, dates)\n knn = self.knn(x, y, x_tomorrow, x_incl_tomorrow, dates)\n tomorrow_norm = [(lin_reg + knn) / 2]\n today_norm = [df['Adj Close'][-1]]\n tomorrow = round((tomorrow_norm[0] * self.daily['Adj Close'][0]), 2)\n today = self.daily['Adj Close'][-1]\n percent_gain = round((((tomorrow / today) - 1) * 100), 2)\n percent_gain_int = abs(int(round(percent_gain, 0)))\n\n if percent_gain > 0:\n self.debug += '\\nExpected price gain: {} %, buys + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\n self.buys += percent_gain_int\n else:\n self.debug += '\\nExpected price gain: {} %, sells + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\n self.sells += percent_gain_int\n\n # plots dotted line connecting stock today with tomorrow's prediction\n predicting_line = np.append(today_norm, tomorrow_norm, axis=0)\n\n if self.will_plot:\n self.ax.plot(dates[-2:], predicting_line, color='cyan', dashes=([1, 1, 1, 1]))\n self.ax.plot(pd.to_datetime(x_tomorrow), tomorrow_norm, marker='o', markersize=3, color=\"cyan\")","def model_predict(country,year,month,day,all_models=None,test=False):\r\n\r\n ## start timer for runtime\r\n time_start = time.time()\r\n\r\n ## load model if needed\r\n if not all_models:\r\n all_data,all_models = model_load(training=False)\r\n \r\n ## input checks\r\n if country not in all_models.keys():\r\n #log\r\n raise Exception(\"ERROR (model_predict) - model for country '{}' could not be found\".format(country))\r\n\r\n for d in [year,month,day]:\r\n if re.search(\"\\D\",d):\r\n #log\r\n raise Exception(\"ERROR (model_predict) - invalid year, month or day\")\r\n \r\n ## load data\r\n model = all_models[country]\r\n data = all_data[country]\r\n\r\n ## check date\r\n target_date = \"{}-{}-{}\".format(year,str(month).zfill(2),str(day).zfill(2))\r\n print(target_date)\r\n\r\n if target_date not in data['dates']:\r\n raise Exception(\"ERROR (model_predict) - date {} not in range {}-{}\".format(target_date,\r\n data['dates'][0],\r\n data['dates'][-1]))\r\n date_indx = np.where(data['dates'] == target_date)[0][0]\r\n query = data['X'].iloc[[date_indx]]\r\n \r\n ## sainty check\r\n if data['dates'].shape[0] != data['X'].shape[0]:\r\n #log\r\n raise Exception(\"ERROR (model_predict) - dimensions mismatch\")\r\n\r\n ## make prediction and gather data for log entry\r\n y_pred = model.predict(query)\r\n y_proba = None\r\n if 'predict_proba' in dir(model) and 'probability' in dir(model):\r\n if model.probability == True:\r\n y_proba = model.predict_proba(query)\r\n\r\n\r\n m, s = divmod(time.time()-time_start, 60)\r\n h, m = divmod(m, 60)\r\n runtime = \"%03d:%02d:%02d\"%(h, m, s)\r\n\r\n ## update predict log\r\n update_predict_log('Prediction completed for country {0} and target date {1}. Total runtime {2}'.format(country,target_date,runtime))\r\n return({'y_pred':y_pred,'y_proba':y_proba})","def predict(self, **kwargs):\n\n logging.debug(\n \"Call predict() with parameters. \"\n \"Forecast 1 step only, kwargs:{kwargs}\".format(kwargs=kwargs)\n )\n\n X_test = self.df[-self.step :][self.feature_names]\n X_test = self.scaler.transform(X_test)\n y_predict = self.model.predict(X_test)\n poly_now = self.y_train_season_obj[-1]\n first_occ = np.where(self.y_train_season_obj == poly_now)\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\n now = self.df[\"y\"][-self.step :]\n return (now - poly_now) - y_predict + polynext","def run():\n\n df = read_input() # the parameters\n df = add_time_period(df) # a feature\n df = is_holiday(df) # a feature\n df = scale_continous(df) # continous feature transformation\n df = encode_dummy(df) # categorical feature transformation\n df = order_columns(df) # ordering model inputs\n model = load_model() # the multiple linear regression model\n prediction = int(model.predict(df)) # form a prediction\n return prediction # return the prediction","def mlp_forecast(model_data, x_filename):\n # extract model and tree from model data\n model = model_data[0]\n window = model_data[1]\n # grab test data from file\n x = fio.read_from_file(x_filename)\n x = x.to_numpy()\n # predict values\n y_hat = model.predict(x)\n # interpolate predicted values to real size\n y_hat = mlp_output_mapper(y_hat, window)\n return y_hat","def test_03_predict(self):\n country = 'all'\n year = 2018\n month = 2\n n_next = 20\n query_data = {'country': 'all',\n 'year': 2018,\n 'month': 2,\n 'n_next': 20\n }\n\n request_json = {'query':query_data,'mode':'test'}\n\n r = requests.post('http://localhost:{}/predict'.format(port),json=request_json)\n response = literal_eval(r.text)\n\n self.assertTrue(len(response['y_pred'])==n_next)\n self.assertTrue(len(response['y_lower'])==n_next)\n self.assertTrue(len(response['y_upper'])==n_next)\n\n for p in response['y_pred']:\n self.assertTrue(isinstance(p,numbers.Number))\n\n for p in response['y_lower']:\n self.assertTrue(isinstance(p,numbers.Number))\n\n for p in response['y_upper']:\n self.assertTrue(isinstance(p,numbers.Number))","def arima_model(self, run_id):\n # Retrieve data for modelling\n measures = self.daily_avg(run_id)\n\n # don't try to compute if there aren't any measures\n if measures is None:\n return pd.DataFrame()\n\n # Take past 7-day average of exogenous predictors to use for\n # future prediction\n exog_future_predictors = pd.concat(\n [measures.iloc[-7:, :].mean(axis=0).to_frame().T]*7,\n ignore_index=True\n )\n\n try:\n # Find optimal order for model\n params = arma_order_select_ic(measures['flow'], ic='aic')\n try:\n # Build and fit model\n mod = ARIMA(measures['flow'],\n order=(params.aic_min_order[0], 0,\n params.aic_min_order[1]),\n exog=measures[['temp', 'precip']]).fit()\n\n prediction = pd.DataFrame(\n [mod.forecast(steps=7,\n exog=exog_future_predictors[\n ['temp', 'precip']],\n alpha=0.05)[0]]).T\n except Exception:\n # If model doesn't converge, return \"prediction\"\n # of most recent day\n prediction = pd.concat([measures.iloc[-1, :].to_frame().T]*7,\n ignore_index=True)['flow']\n except ValueError:\n # If order fitting doesn't converge, return \"prediction\"\n # of most recent day\n prediction = pd.concat([measures.iloc[-1, :].to_frame().T]*7,\n ignore_index=True)['flow']\n\n # Add dates and return past 21 days for plotting\n prediction_dates = [measures.index[-2] +\n datetime.timedelta(days=x) for x in range(0, 7)]\n prediction.index = prediction_dates\n past = measures['flow'][-22:-1]\n prediction = pd.concat([past[:-1], prediction], axis=0)\n\n return prediction","def forecast(\n input_values: np.ndarray, future_dates: List, model: Sequential, scaler\n) -> pd.DataFrame:\n if scaler:\n future_values = scaler.inverse_transform(\n model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\n )\n else:\n future_values = model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\n\n df_future = pd.DataFrame(\n future_values, index=future_dates, columns=[\"Predicted Price\"]\n )\n return df_future","def predict_Keras():\n #read future games\n future_mstr = pd.read_csv(FG_filename)\n #extract features and prepare X for predictions\n features = future_mstr.columns[6:]\n X_new = future_mstr[features]\n #load saved model\n loaded_model = load_model(K_filename)\n # make predictions on the future games\n y_new = loaded_model.predict(X_new)\n # round predictions\n y_new = [round(x[0]) for x in y_new]\n # find prediction probabilities on the future games\n y_new_proba = loaded_model.predict_proba(X_new)\n # grab the future games and make a copy so you can add the predictions side by side\n prediction_mstr_w_preds = future_mstr.copy()\n # add the predictions and probabilities\n prediction_mstr_w_preds['Prediction'] = y_new\n prediction_mstr_w_preds['Proba'] = y_new_proba\n # keep only the columns you want to see\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Prediction', 'Proba']\n prediction_final = prediction_mstr_w_preds[cols]\n \n return prediction_final","def predictions_for_df(df, inferrer):\n working_df = df.copy()\n working_df['predictions'] = inferrer.get_activations(\n working_df.sequence.values).tolist()\n return working_df","def test_04_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n\n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\n self.assertEqual(y_pred,logged_y_pred)","def _predict(cls, model, is_log_transformed,\n raw_actual, interpolated_actual,\n training_end=None, seasonal_feature_scoring=None, pred_date=None, order_of_diff=None,\n training_tail=None, ext_training_features=None, pred_len=None, freq=None,\n include_holidays_exog=None):\n\n import numpy as np\n import pandas as pd\n import scipy.stats as st\n from numpy.linalg import LinAlgError\n import math\n\n alpha = cls._sig_level\n alpha_extreme = cls._sig_level_extreme\n\n include_holidays_exog = include_holidays_exog if ext_training_features else 0\n\n index = pd.date_range(start=training_end, end=pred_date, freq=freq)[1:] # Holidays are always daily.\n\n de_obj = DataExploration()\n pred_exog = de_obj._get_exog_data(pred_date, pred_date, index) if include_holidays_exog else None\n\n if pred_exog is not None and set(pred_exog.columns.values) != set(ext_training_features):\n missing_col_list = list(set(ext_training_features) - set(pred_exog.columns.values))\n common_cols = list(set(ext_training_features).intersection(set(pred_exog.columns.values)))\n temp_df = pred_exog[common_cols]\n missing_feat_df = pd.DataFrame(np.zeros([len(pred_exog), len(missing_col_list)]),\n columns=missing_col_list, index=pred_exog.index.values)\n pred_exog = pd.concat([temp_df, missing_feat_df], axis=1)\n pred_exog = pred_exog[ext_training_features]\n\n freq = \"1\" + freq if not any(char.isdigit() for char in freq) else freq\n\n forecast_ndays = int((pred_date - pd.Timestamp(training_end)) / pd.Timedelta(freq))\n model_freshness = forecast_ndays / float(pred_len)\n\n try:\n if forecast_ndays > pred_len:\n raise ValueError('Current trained model object expired')\n\n float_min = 1e-10\n\n # set exogenous (holiday) variables for input data\n if include_holidays_exog:\n pred_exog = pred_exog.loc[pd.Timestamp(training_end) + pd.Timedelta(freq): pred_date]\n else:\n pred_exog = None\n\n if seasonal_feature_scoring:\n if not include_holidays_exog:\n pred_exog = seasonal_feature_scoring[:forecast_ndays]\n else:\n pred_exog['fourier_feature'] = seasonal_feature_scoring[:forecast_ndays]\n\n forecast = list(model.forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog))\n interpolated_training_data = list(zip(*training_tail))[1]\n\n for order in list(reversed(range(order_of_diff))):\n training_data_diff = np.diff(interpolated_training_data,\n order) if order > 0 else interpolated_training_data\n\n forecast_diff_mean = [training_data_diff[-1]]\n forecast_diff_ci = []\n\n for i in range(forecast_ndays):\n forecast_diff_mean.append(forecast_diff_mean[-1] + forecast[0][i])\n forecast_diff_ci.append([forecast_diff_mean[-1] -\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i]),\n forecast_diff_mean[-1] +\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i])])\n forecast[0] = forecast_diff_mean[1:]\n forecast[2] = forecast_diff_ci\n\n if is_log_transformed:\n transformed_back_forecast = np.exp(forecast[0][-1] + ((forecast[1][-1] ** 2) / 2.0)) - 1\n transformed_back_std_err = np.sqrt((np.exp(forecast[1][-1] ** 2) - 1) * (np.exp((2 * forecast[0][-1]) +\n (forecast[1][\n -1] ** 2))))\n transformed_back_CILower = transformed_back_forecast - \\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\n if transformed_back_std_err != 0 else transformed_back_forecast\n transformed_back_CIUpper = transformed_back_forecast + \\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\n if transformed_back_std_err != 0 else transformed_back_forecast\n transformed_back_interpolated_actual = float(np.exp(interpolated_actual) - 1)\n if np.sum(np.isnan(forecast[0][-1])) or np.isnan(forecast[1][-1]):\n raise ValueError('Predicted null value')\n\n if is_log_transformed:\n zscore = (transformed_back_interpolated_actual -\n transformed_back_forecast) / max(float(transformed_back_std_err), float_min)\n\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\n if math.isnan(anomaly_probability) or math.isnan(transformed_back_CILower) \\\n or math.isnan(transformed_back_CIUpper):\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN under log transform')\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\n\n result = {'Success': True,\n 'IsLogTransformed': is_log_transformed,\n 'LogTransformedAdjustedActual': interpolated_actual,\n 'LogTransformedPrediction': float(forecast[0][-1]),\n 'LogTransformedStdErr': float(forecast[1][-1]),\n 'LogTransformedCILower': float(forecast[2][-1][0]),\n 'LogTransformedCIUpper': float(forecast[2][-1][1]),\n 'AdjustedActual': transformed_back_interpolated_actual,\n 'Prediction': float(transformed_back_forecast) if not float(\n transformed_back_forecast) == float('inf') else 0.0,\n 'StdErr': float(transformed_back_std_err) if not float(\n transformed_back_std_err) == float('inf') else 0.0,\n 'CILower': float(transformed_back_CILower) if not float(\n transformed_back_CILower) == float('-inf') else 0.0,\n 'CIUpper': float(transformed_back_CIUpper) if not float(\n transformed_back_CIUpper) == float('inf') else 0.0,\n 'ConfLevel': float(1.0 - alpha) * 100,\n 'ExogenousHolidays': include_holidays_exog,\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\n 'ModelFreshness': model_freshness}\n\n else:\n zscore = (interpolated_actual - forecast[0][-1]) / max(float(forecast[1][-1]), float_min)\n\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\n if math.isnan(anomaly_probability) or math.isnan(forecast[2][-1][0]) or math.isnan(forecast[2][-1][1]):\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN')\n\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\n\n result = {'Success': True,\n 'IsLogTransformed': is_log_transformed,\n 'AdjustedActual': interpolated_actual,\n 'Prediction': float(forecast[0][-1]) if not float(\n forecast[0][-1]) == float('inf') else 0.0,\n 'StdErr': float(forecast[1][-1]) if not float(\n forecast[1][-1]) == float('inf') else 0.0,\n 'CILower': float(forecast[2][-1][0]) if not float(\n forecast[2][-1][0]) == float('-inf') else 0.0,\n 'CIUpper': float(forecast[2][-1][1]) if not float(\n forecast[2][-1][1]) == float('inf') else 0.0,\n 'ConfLevel': float(1.0 - alpha) * 100,\n 'ExogenousHolidays': include_holidays_exog,\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\n 'ModelFreshness': model_freshness}\n\n except (LinAlgError, ValueError, LADStructuralError) as e:\n result = {'Success': False,\n 'AdjustedActual': interpolated_actual,\n 'ErrorMessage': str(e)}\n\n return result","def predict():\r\n global model_pred\r\n if request.method == 'POST':\r\n date = int(request.form['x'])\r\n pred_args =[date]\r\n pred_args_arr=np.array(pred_args)\r\n pred_features=pred_args_arr.reshape(1,-1)\r\n model_reg=open(\"classical_decomposition_regression_model\", \"rb\")\r\n ml_model=joblib.load(model_reg)\r\n model_pred=ml_model.predict(pred_features)\r\n model_pred=round(float(model_pred), 2)\r\n\r\n return render_template('predict.html', predictions=model_pred)","def make_predictions(df):\n t_labels = get_labels(\"labels_pca\")\n # clean data\n df = clean_data(df)\n # engineer data\n df = engineer_features(df)\n # predict\n with open(\"model.pkl\",\"r\") as mdl:\n model = pickle.load(mdl)\n mdl.close()\n predictions = model.predict(df[t_labels])\n return predictions","def get_prediction(amazon_id, df):\n\n # convert values to pd DataFrame\n df, predict_window = process_data(df)\n\n #check if model for the product exists and use it, otherwise create new one\n if(not os.path.exists(get_model_path(amazon_id))):\n\n #process data\n dataset_train, dataset_test = split_dataset(df, predict_window)\n\n #transform data\n dataset_train, dataset_test = transform_data(dataset_train, dataset_test)\n\n #scale data\n x_train, y_train, x_test, y_test = reshape_datasets(dataset_train, dataset_test, predict_window)\n\n #create model\n model = get_model(x_train)\n\n #fit data \n model.fit(x_train, y_train, epochs=500, batch_size=512)\n\n #save model \n model.save(get_model_path(amazon_id))\n\n #clear the session\n Clear.clear_session()\n \n if os.path.exists(get_model_path(amazon_id)):\n\n #upload model\n model = load_model(get_model_path(amazon_id)) \n\n #run scaler\n scaler.fit(df)\n\n #get inputs for prediction\n inputs = df[len(df) - predict_window:]\n\n #transform inputs\n inputs = scaler.transform(inputs)\n\n # Slide the window forward by one, so the last predicted value now becomes the head of \n # the new window and predict the next, slide again, and so on\n for i in range(predict_window):\n x_predict = []\n\n x_predict.append(inputs[i:i + predict_window,0])\n x_predict = np.array(x_predict)\n x_predict = np.reshape(x_predict, (x_predict.shape[0], x_predict.shape[1],1))\n nextPrice = model.predict(x_predict)\n \n inputs = np.append(inputs, nextPrice, axis=0)\n\n #inverse transformation we did\n predictions = scaler.inverse_transform(inputs[predict_window:])\n\n #convert numpy array to python list\n python_list = get_python_list(predictions)\n\n #clear the session after creating or loading model\n Clear.clear_session()\n\n return python_list","def ml_pred_post(ticker=None, stock_last_day=None, train_pred_df=None, val_pred_df=None, test_pred_df=None, \nX_features=None, y_features=None, n_past_days=90, n_future_days=5, loss='mean_squared_error', lr=0.01, epochs=12, batch_size=32, RMSE_train_pred=None, RMSE_val_pred=None):\n\n try:\n train_pred_df_dict = train_pred_df.to_dict(orient='records')\n val_pred_df_dict = val_pred_df.to_dict(orient='records')\n test_pred_df_dict = test_pred_df.to_dict(orient='records')\n \n n_X_features = len(X_features)\n n_y_features = len(y_features)\n\n pred_post_to_upload = {\n 'Stock': ticker,\n 'Datetime': stock_last_day,\n 'X_features': X_features,\n 'y_features': y_features,\n 'n_past_days': n_past_days,\n 'n_future_days': n_future_days,\n 'ML Model': {\n 'model': 'LSTM',\n 'parameters': {\n 'layers': {'LSTM_1 units': 64,\n 'LSTM_1 input_shape': (n_past_days, n_X_features),\n 'LSTM_2 units': 32,\n 'Dropout': 0.2,\n 'Dense units': n_future_days*n_y_features\n },\n 'compile': {\n 'optimizer': 'Adam',\n 'loss': str(loss),\n 'lr': lr\n },\n 'fit': {\n 'epochs': epochs,\n 'batch_size': batch_size\n } \n },\n\n },\n 'RMSE_train_pred': float(RMSE_train_pred),\n 'RMSE_val_pred': float(RMSE_val_pred),\n 'train_pred': train_pred_df_dict,\n 'val_pred': val_pred_df_dict,\n 'test_pred': test_pred_df_dict\n }\n return pred_post_to_upload\n\n except Exception as e:\n print('Something went wrong...')\n print(f'str(e) = {str(e)}')\n # print(f'repr(e) = {repr(e)}')\n\n # return pred_post_to_upload","def __predict(self, predict_start_date, predict_end_date)->dict:\n X, y = self.__get_data(self.universe, self.factor_pool, self.ret_label, predict_start_date, predict_end_date)\n res_dict = self.model.predict(X)\n return res_dict","def predict(self, forecastyear, scenario=None):\n\n start_time = time()\n\n results = []\n for m in self.model:\n\n # \"TARGET\" -> ISO-3 country code of destination country\n R = {}\n R['target'] = m['target']\n\n # Forecast year in the input query\n R['year'] = forecastyear\n\n # Scenario in the input query\n R['scenario'] = scenario if scenario else {}\n\n Xv = self.__adjust_features(m['feature'], scenario)\n # logger.info(\"Return adjust features in {:3.2f} sec.\".format(time() - start_time))\n\n R['forecast'] = m['clf'].predict(Xv)[0]\n # logger.info(\"Return forecast predict in {:3.2f} sec.\".format(time() - start_time))\n\n # Explanation of point forecast using SHAP values\n # R['explanation'] = self.__get_explanations(m['explainer'], Xv, m['Xt'].columns)\n\n # Confidence intervals are now one-sided\n # R['CI'] = [0.0, m['CI'].predict(Xv)[0]]\n # logger.info(\"Return CI predict in {:3.2f} sec.\".format(time() - start_time))\n\n results.append(R)\n\n logger.debug(\"Return predict in {:3.2f} sec.\".format(time() - start_time))\n\n return results","def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\n df.dropna(inplace=True)\n ## day and month\n df['Date'] = pd.to_datetime(df['Date'])\n df['Month'] = df['Date'].dt.month\n df['dayowk'] = df['Date'].dt.dayofweek\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\n \n ##Previos n-day pct_changes\n df['1day_pct'] = df['Adj Close'].pct_change()\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\n \n ##Cumulative sum of 1day_pct\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\n \n ##EWMA of 7, 50 and 200 days\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\n ## Golden Cross vs Death Cross etc.\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n \n ##RSI and MACD\n df = RSI(df,14)\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\n \n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\n \n ##Classification target\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\n \n ## IS the stock Overbought or Oversold based on RSI?\n df['RSI_overbought'] = df['RSI']>70\n df['RSI_oversold'] = df['RSI']<30\n \n \n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\n# df = df.dropna()\n \n #df = df.reset_index(drop=True)\n \n ## Calculating how large the previos hot and cold streaks were\n f = 0\n df['prev_hot_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==1:\n f += 1\n if df['target'][i+1] ==0:\n df['prev_hot_streak'][i+1] = f\n f = 0\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_hot_streak'][i]==0:\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\n \n \n df['prev_cold_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==0:\n f += 1\n if df['target'][i+1] ==1:\n df['prev_cold_streak'][i+1] = f\n f = 0\n\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_cold_streak'][i]==0:\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\n \n ## Calculating current hot and cold streaks\n df['current_hot_streak'] = np.zeros(df.shape[0])\n df['current_cold_streak'] = np.zeros(df.shape[0])\n fhot=0\n fcold=0\n for i in range(df.shape[0]):\n if df['target'][i]==1:\n fhot += 1\n fcold = 0\n df['current_hot_streak'][i] = fhot\n elif df['target'][i]==0:\n fcold += 1\n fhot = 0\n df['current_cold_streak'][i] = fcold\n \n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\n \n ## Combinations of previos streaks\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\n \n ##Calculating days since max\n current_max = df['Adj Close'][0]\n df['days_from_max'] = np.zeros(df.shape[0])\n df['pct_from_max'] = np.zeros(df.shape[0])\n #print('blah')\n for i in range(1,df.shape[0]):\n if df['Adj Close'][i] > current_max:\n current_max = df['Adj Close'][i]\n # print(current_max)\n else:\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\n #print(df['days_from_max'][i])\n \n \n \n df.dropna(inplace=True)\n df = df.reset_index(drop=True)\n return df","def get_pred_score(file_name, kpi, start_train, end_train, periods=365):\n # set names for columns in order\n names = ['date', 'handle_time', 'handle_time_forecast',\n 'volume', 'volume_forecast']\n df = pd.read_csv('./data/'+file_name, names=names, header=1, index_col=0)\n\n df['date'] = pd.to_datetime(df['date'])\n df.index = df['date']\n\n # create column for aht\n df['aht'] = df['handle_time']/df['volume']\n df['aht_forecast'] = df['handle_time_forecast']/df['volume_forecast']\n\n # df = df[(df['aht'] > 200) & (df['aht'] < 1500)]\n\n df1 = df.copy()\n\n # create business unit name\n bu = file_name[:6]\n\n # # remove outliers if it helps the model\n # df = remove_outliers(df, bu)\n\n # remove holidays\n df = df[~df.index.isin(h.iloc[:,0].tolist())]\n\n # remove weekends\n df = df[~df.index.weekday.isin([5,6])]\n\n # # plot data in a notebook\n # plot data\n # plot_time_vol(df)\n\n # create training\n df = create_training_data(df, kpi, start_train, end_train)\n\n \n # display(df.head())\n m,future = create_forecast(df, periods=int(periods))\n forecast = m.predict(future)\n\n # # plot forecast in a notebook\n # plot_forecast(forecast)\n\n # set forecast beginning and end date\n f = forecast.copy()\n end_test = df1.index.max()\n f = f[(forecast['ds'] > end_train) & (f['ds'] <= end_test)]\n\n # create validation dataset\n df2 = df1.copy()\n df2['ds'] = pd.to_datetime(df2.index.date) \n\n # set start date of validation data equal to June 1st, 2020 or any other date\n df2 = df2[df2['ds'] > end_train]\n\n # remove weekends and holidays from data\n df2 = df2[~df2.index.isin(h.iloc[:,0].tolist())]\n df2 = df2[~df2.index.weekday.isin([5,6])]\n os.makedirs('./preds/'+kpi, exist_ok=True)\n\n future_forecast=forecast[['ds', 'yhat_lower', 'yhat', 'yhat_upper']][forecast['ds']>datetime.datetime.now()]\n\n f[['ds', 'yhat_lower', 'yhat', 'yhat_upper']].to_csv('./preds/'+kpi+'/'+bu+'.csv')\n forecast.to_csv('./preds/analysis/'+kpi+'_'+bu+'.csv')\n\n # Validate test data, it must match for scoring\n mae = evaluate_model(f,df2, kpi, metric='mae')\n\n mae.update({'kpi': kpi, 'start_train':start_train, 'end_train': end_train, 'end_test': end_test}) \n\n curr = pd.read_csv('scores/'+kpi+'_score.csv',index_col=0)\n new = pd.DataFrame(mae, index=[bu])\n\n new.to_csv('./scores/current/'+bu+'_'+kpi+'.csv')\n \n if bu not in curr.index:\n curr = pd.concat([curr, new], 0)\n # create log everytime a score is superceded\n elif new.loc[bu,'prophet'] < curr.loc[bu,'prophet']:\n data = pd.concat([curr.loc[[bu],:],new.loc[[bu],:]],0)\n data.to_csv('./scores/logs/'+bu+'_'+kpi+'_'+datetime.datetime.now()\n .strftime(\"%b %d %Y %H:%M:%S\").replace(' ', '_')+'.csv')\n curr.update(new)\n curr.to_csv('./scores/'+kpi+'_score.csv')\n\n return 'error with metric'\n print('\\ntraining',file_name, 'on', kpi, 'start train:',start_train, \n 'end train:', end_train, 'end_test:', )","def predict_XGB():\n #read future games\n future_mstr = pd.read_csv(FG_filename)\n #extract features and prepare X for predictions\n features = future_mstr.columns[6:]\n X_new = future_mstr[features]\n #load saved model\n xgb = joblib.load(XG_filename)\n #make predictions\n XG_preds = xgb.predict(X_new)\n future_mstr['Predictions'] = XG_preds\n #return the relevant prediction information\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Predictions']\n prediction_mstr_all = future_mstr[cols]\n \n return prediction_mstr_all","def predict_progression(final):\n #Import the dataset for prediction\n dataset = read_excel('SampleProgressionData.xlsx', sheet_name=0)\n \"\"\"\n Split the data into a training and a testing set\n \"\"\"\n train_features = dataset.iloc[0:197,:-1]\n train_targets = dataset.iloc[0:197,-1]\n i=0\n df_cluster_data_col = {}\n Dict_progression = {} \n Dict_rec_progression = {} \n while i < 5:\n # Number of employees in this cluster\n array_n = final[final.cluster == i][\"EmployeeID\"].unique()\n len(array_n)\n print(\"cluster \", i )\n print(\"Employees: \", array_n)\n # List of employees\n final[final.cluster == i]\n cluster_data_n=final[final.cluster == i]\n cluster_data_n=cluster_data_n[0:len(array_n)]\n # cluster_data_n\n df_cluster_data_n=pd.DataFrame(cluster_data_n, columns = ['EmployeeID', 'MajorityBand','CurrentBand','NumberYears','Progression']) \n df_cluster_data_n['MajorityBand']=df_cluster_data_n['CurrentBand'].max()\n df_cluster_data_n.fillna(0, inplace=True)\n dataset_cluster_n = pd.DataFrame(df_cluster_data_n,columns = ['EmployeeID','MajorityBand', 'CurrentBand','NumberYears','Progression'])\n test_features = dataset_cluster_n.iloc[:,1:4]\n test_targets = dataset_cluster_n.iloc[:,-1]\n \"\"\"\n Train the model\n \"\"\"\n tree = DecisionTreeClassifier(criterion = 'entropy').fit(train_features,train_targets)\n \"\"\"\n Predict the classes of new, unseen data\n \"\"\"\n prediction = tree.predict(test_features)\n \"\"\"\n Check the accuracy\n \"\"\"\n print(\"The prediction accuracy is: \",tree.score(test_features,test_targets)*100,\"%\")\n df_cluster_data_n['Progression']=list(prediction)\n strmax = df_cluster_data_n['CurrentBand'].max()\n print(\"strmax \", strmax)\n print(\"prediction \", prediction)\n print('---------------------------------------------------------------------')\n print(\"PREDICTION FOR BAND \", strmax)\n print('---------------------------------------------------------------------')\n df_cluster_data_n = df_cluster_data_n.loc[df_cluster_data_n[\"Progression\"]==1]\n df_cluster_data_empid=pd.DataFrame(df_cluster_data_n, columns = ['EmployeeID'])\n df_cluster_data_col[i]=df_cluster_data_empid\n Dict_progression[strmax]=list(df_cluster_data_empid['EmployeeID'].unique())\n print(\"Dict_progression \", Dict_progression)\n i=i+1\n # Show the next promotion band and not the cluster highest band for recommendation to employees\n list_promotion = Dict_progression[strmax]\n for emp_band_promo in list_promotion:\n array = df_cluster_data_n.loc[df_cluster_data_n['EmployeeID'] == emp_band_promo]['CurrentBand'].unique()\n promotion_band = array[0]+1 \n list_emp_band = []\n if promotion_band in Dict_rec_progression:\n list_emp_band = Dict_rec_progression[promotion_band]\n list_emp_band.append(emp_band_promo) \n list_emp_band.sort()\n Dict_rec_progression[promotion_band] = list_emp_band\n print(\"Dict_progression\", Dict_progression)\n print(\"Dict_rec_progression\", Dict_rec_progression) \n return Dict_rec_progression","def alspostprocess(data, prediction, features, user_features, movie_features, n_features=10):\r\n \r\n\r\n data['ALS'] = prediction[data.loc[:, 'userID']-1, data.loc[:, 'movieID']-1]\r\n features.append('ALS')\r\n \r\n total_features = len(movie_features)\r\n if n_features>total_features:\r\n n_features = total_features\r\n \r\n for i in range(n_features):\r\n data[\"UserFeature{}\".format(i)] = user_features[data.loc[:, 'userID']-1, i]\r\n features.append(\"UserFeature{}\".format(i))\r\n data[\"MovieFeature{}\".format(i)] = movie_features[i, data.loc[:, 'movieID']-1]\r\n features.append(\"MovieFeature{}\".format(i))\r\n return data, features","def predict(self, trained_model, prediction_datetime):\n return trained_model.predict()","def depart_arrive_stats_by_month(flights):\n\n return ...","def predict(self,X_test):\r\n self.X_test_data=X_test.reset_index(drop=True)\r\n \r\n temp=pd.DataFrame()\r\n count=0\r\n for each_model in self.fitted_model:\r\n count=count+1\r\n \r\n temp_str='model_'+str(count)+'_predictions'\r\n temp[temp_str]=each_model.predict(self.X_test_data)\r\n \r\n temp_str='model_'+str(count)+'_probablities'\r\n out = each_model.predict_proba(self.X_test_data)\r\n temp[temp_str]=[max(each) for each in out]\r\n \r\n \"\"\"self.outcomes=temp_df\r\n return self.out()\"\"\"\r\n \r\n \r\n \r\n \"\"\"columns list form all the predictions and probabalities individually\"\"\"\r\n pred_list=[x for x in temp.columns if x.split('_')[-1]=='predictions']\r\n prob_list=[x for x in temp.columns if x.split('_')[-1]=='probablities']\r\n \r\n \"\"\"getting max probablity column name out of alla the probablity\"\"\"\r\n \r\n max_prob=temp[prob_list].max(axis=1)\r\n max_prob_col_name=temp[prob_list].idxmax(axis=1)\r\n \r\n \"\"\"getting final prediction column name for each instance using max probabaility column name\"\"\"\r\n final_pred_col=[]\r\n for every in max_prob_col_name:\r\n final_pred_col.append('_'.join(every.split('_')[:-1])+'_predictions')\r\n \r\n \"\"\"final_pred=[]\r\n for each in range(len(final_pred_col)):\r\n final_pred.append(temp[pred_list].loc[each][final_pred_col[i]])\"\"\"\r\n \r\n final_pred=[]\r\n for each in range(len(final_pred_col)):\r\n final_pred.append(temp[pred_list].loc[each][final_pred_col[each]])\r\n \r\n \r\n \r\n self.final_preditions=pd.DataFrame(final_pred,columns=['prediction'])\r\n self.final_probablaties=pd.DataFrame(max_prob,columns=['probablity'])\r\n \r\n \r\n return (pd.concat([self.X_test_data, self.final_preditions,self.final_probablaties], axis=1, sort=True))","def predict(df,x):\n ml,g,sl2,sm2,sd2= x\n _,in_dic = rl.build_data_strucutre(df,'length_box_um',1)\n pred_mat = rl.prediction_total(ml,g,sl2,sm2,in_dic['reind_v'],in_dic['dat_v'],in_dic['s'],in_dic['S'],in_dic['dt'],in_dic['lane_ID_v'],in_dic['val_v'],1,sd2,nproc=10)\n df = rl.merge_df_pred(df,pred_mat)\n return df","def anomaly_detect(data):\n reconstruction_dict = {}\n for location, dataframe in data.items():\n model_dir_path = os.path.join(THIS_DIR, './models')\n print(location)\n samples = len(dataframe.index.codes[0])\n timesteps = len(dataframe.columns.levels[1])\n np_data_o1 = dataframe[['occupancy1']].values\n np_data_o2 = dataframe[['occupancy2']].values\n np_data_diff = np.abs(np_data_o1 - np_data_o2)\n scaler = MinMaxScaler()\n np_data_o1 = scaler.fit_transform(np_data_o1)\n np_data_o2 = scaler.fit_transform(np_data_o2)\n np_data_diff = scaler.fit_transform(np_data_diff)\n np_data = np.stack((np_data_o1, np_data_o2, np_data_diff), axis=-1)\n print(str(np_data.shape[0]) + ' weeks processing') \n\n ae = LstmAutoEncoder()\n\n # fit the data and save model into model_dir_path\n ae.fit(np_data[:, :, :], model_dir_path=model_dir_path, std_dev_threshold=1.5)\n\n # load back the model saved in model_dir_path detect anomaly\n ae.load_model(model_dir_path)\n anomaly_information = ae.anomaly(np_data[:, :, :])\n reconstruction_error = {}\n if ae.threshold == 0.0:\n continue\n else:\n reconstruction_error['threshold'] = ae.threshold\n years = list(dataframe.index.get_level_values(0))\n weeks = list(dataframe.index.get_level_values(1))\n for idx, (is_anomaly, dist) in enumerate(anomaly_information):\n reconstruction_error[str(years[idx]) + ', ' +str(weeks[idx])] = [is_anomaly, dist]\n if is_anomaly == True:\n print(location + ' year ' + str(years[idx]) + ', week ' + str(weeks[idx]) + ' is anomalous')\n reconstruction_dict[location] = reconstruction_error\n\n return reconstruction_dict","def store_predictions(df):\n ts = df[df.columns[1]]\n base = pd.DataFrame(ts)\n preds = make_preds(ts, 'Predicted '+ df.columns[1])\n base.index = df['year']\n base = base.append(pd.DataFrame(preds), sort = True)\n for col in df.columns[2:]:\n ts = df[col]\n temp = pd.DataFrame(ts)\n preds = make_preds(ts, 'Predicted ' + col)\n temp.index = df['year']\n temp = temp.append(pd.DataFrame(preds), sort = True)\n base = base.join(temp)\n return base","def recursive(model_dic_item, Std2=True, skip=60, leave=0): \n\n key, model_obj = model_dic_item\n \n # Use internal variable names\n Xn = model_obj.Xn\n monthR = model_obj.target\n continuous = model_obj.continuous\n h = model_obj.horizon\n Std = model_obj.std\n minH = model_obj.minH\n minR = model_obj.minR\n lagBM = model_obj.lagBM\n\n if lagBM != 0:\n # Do not change original\n Xn = Xn.copy(deep=True)\n Xn.index = Xn.index + MonthEnd(lagBM)\n \n # Merge already copy the dfs => not change input df\n data = pd.merge(Xn, model_obj.Y, how='left', left_index=True, \n right_index=True)\n idxR = Xn.shape[1] # Index of 'RM' column\n\n # Ret. in proper frequency, 'fR', (h=1 => no change)\n if h != 1:\n data['fR'] = changeFreq(data[monthR], new_frequency=h,\n continuous=continuous, percent=False) \n else:\n data['fR'] = data[monthR]\n \n # Returns that will be realized over h periods starting *after* t:\n # ex: t=Jan (end) => Ret(01/Feb - 31/Jan)\n data['Future'] = data['fR'].shift(-h)\n \n # historical mean prediction (observable at t):\n ###############################################\n data['Ehist'] = data['fR'].expanding().mean()\n # Errors historical:\n data['eNested'] = data['Ehist'] - data['Future']\n \n ############################################################\n # Forecasts from the model (larger) *at the end of month t*\n ############################################################\n data['Er'] = np.nan\n # make one prediction w/ info until (and including) t\n for t in range(skip, data.shape[0] - leave):\n \n # The last *position* is t!\n subsample = data.iloc[: t + 1, :].copy()\n lastDateIS = subsample.index[-1] # last date in subsample\n \n subsample['F'] = estim_F(subsample.iloc[:, :idxR], # the first t \"BMs\"\n subsample['fR'],\n continuous, h, Std, Std2, minH, minR,\n IS=False, lagBM=0)\n \n # because conditions minR, etc => F=Nan often\n if not subsample['F'].isnull().all():\n results = sm.OLS(subsample['fR'].shift(-h), # .iloc[:-h]\n sm.add_constant(subsample['F']), # .iloc[:-h]\n missing='drop').fit()\n # Save result in main df:\n data['Er'].loc[lastDateIS] = (\n results.params[0] \n + results.params[1] * subsample['F'].loc[lastDateIS])\n\n # Prediction error (model)\n # NOTE: I register the prediction error *at the prediction date*,\n # not \"1y\" later, when the return is realized!\n # KP place the prediction in t+1 always, even for 12m. \n # This can be confusing to compare the results.\n data['eLarger'] = data['Er'] - data['Future']\n \n # Return a tupple with the forecast DF and the key:\n return data.filter(['Er', 'Ehist', 'eLarger', 'eNested', 'Future']\n ).copy(), model_obj.key","def predict(data, model, final_columns, category_features, averages):\n new_data = extend_dummy_columns(data, final_columns, category_features)\n # np array to use for our model.\n test_data = np.ones(len(final_columns))\n for i, col in enumerate(final_columns):\n if new_data[col] is None:\n test_data[i] = averages[col]\n else:\n test_data[i] = new_data[col]\n return model.predict_proba(test_data)[0][1]","def compare_with_historical_m3_forecast(configs: Configs, data_loader: DataLoader):\n\n # Historical forecasts\n granularity_week = [\n n.FIELD_YEARWEEK,\n n.FIELD_LEAD_SKU_ID,\n n.FIELD_CUSTOMER_GROUP,\n n.FIELD_PLANT_ID\n ]\n granularity_month = [n.FIELD_YEARMONTH] + granularity_week[1:]\n\n relevant_columns = [\n n.FIELD_YEARMONTH,\n n.FIELD_YEARWEEK,\n n.FIELD_LEAD_SKU_ID,\n n.FIELD_CUSTOMER_GROUP,\n n.FIELD_PLANT_ID,\n Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3,\n 'forecast_volume_m-3'\n ]\n data_loader.load_sell_in_forecast_accuracy_comparison_data(Evaluator.GROUND_TRUTH_APO_FORECAST_ACCURACY_REPORT_M3)\n\n historical_forecasts = data_loader.df_fc_acc.filter(relevant_columns)\n in_period = ((historical_forecasts[n.FIELD_YEARWEEK] >= configs.evaluation_start) &\n (historical_forecasts[n.FIELD_YEARWEEK] <= configs.evaluation_end))\n historical_forecasts = historical_forecasts[in_period]\n\n # Group to get one value per SKU, CPG, Plant, Week\n historical_forecasts = (historical_forecasts\n .groupby(granularity_week)\n .agg({n.FIELD_YEARMONTH: 'first',\n Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3: 'sum',\n 'forecast_volume_m-3': 'sum'})\n .reset_index()\n )\n\n # Predictions\n apo_week_month_list = (historical_forecasts[[n.FIELD_YEARMONTH, n.FIELD_YEARWEEK]]\n .sort_values([n.FIELD_YEARMONTH, n.FIELD_YEARWEEK])\n .drop_duplicates(n.FIELD_YEARWEEK))\n apo_week_month_list = build_m3_target_predictions_table(\n apo_week_month_list, configs.evaluation_start, configs.evaluation_end)\n\n # list contains all (SKU, CPG, Plant) combinations that appear at least once in a given month\n # TODO - cross join to get all existing (material, plant, cpg) combinations\n df_predictions_list = historical_forecasts[granularity_month].drop_duplicates()\n df_predictions_list = df_predictions_list.merge(apo_week_month_list, on=n.FIELD_YEARMONTH, how='left')\n predictions = predict_multiple_horizons(df_predictions_list, configs, data_loader)\n\n # scope historical forecasts table to keep exactly the same weeks as the predicted ones (before aggregating)\n historical_forecasts = historical_forecasts.merge(\n predictions.rename(columns={'date_to_predict': n.FIELD_YEARWEEK}).filter(granularity_week).drop_duplicates(),\n on=granularity_week, how='inner'\n )\n\n # Comparison ML forecast vs historical forecast\n # predictions_output = pd.read_csv('predictions_m3.csv')\n aggs = {Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3: 'sum', 'forecast_volume_m-3': 'sum'}\n historical_forecasts_month = historical_forecasts.groupby(granularity_month).agg(aggs).reset_index()\n predictions_month = predictions.groupby(granularity_month)[n.FIELD_PREDICTION].sum().reset_index()\n forecasts_apo_vs_ml_m3 = historical_forecasts_month.merge(\n predictions_month,\n on=granularity_month, how='left'\n )\n\n # Add is_on trade information\n tmp = data_loader.df_cpg_trade_channel[[n.FIELD_CUSTOMER_GROUP, 'trade_type']]\n tmp['is_on_trade'] = (tmp['trade_type'] == 'is_on_trade')\n tmp = tmp[[n.FIELD_CUSTOMER_GROUP, 'is_on_trade']]\n forecasts_apo_vs_ml_m3 = forecasts_apo_vs_ml_m3.merge(tmp, on=n.FIELD_CUSTOMER_GROUP, how='left')\n\n forecasts_apo_vs_ml_m3 = forecasts_apo_vs_ml_m3[forecasts_apo_vs_ml_m3[n.FIELD_PLANT_ID] != 'F015']\n evaluator = Evaluator(\n data=data_loader,\n granularity=granularity_month,\n start_periods_eval=configs.evaluation_start,\n end_periods_eval=configs.evaluation_end,\n forecasting_horizon=3,\n granularity_eval='forecast_volume_m-3',\n is_sell_in_model=configs.is_sell_in_model,\n is_weekly_forecast=False\n )\n granularities = replace_week_granularity_by_month(configs.granularity_evaluator.values())\n evaluator.report(forecasts_apo_vs_ml_m3, granularities)\n evaluator.detailed_report(forecasts_apo_vs_ml_m3, granularities)","def predict():\n data = request.json\n\n if data:\n predict = bool(data[\"predict\"])\n\n if predict:\n if predictor.pred_dict[\"model\"] == 0:\n # ARIMA\n arima_forecast = predictor.get_prediction_arima()\n plots.arima_df = arima_forecast\n elif predictor.pred_dict[\"model\"] == 1:\n # Prophet\n prophet_forecast = predictor.get_prediction_prophet()\n plots.prophet_df = prophet_forecast\n elif predictor.pred_dict[\"model\"] == 2:\n # LSTM\n lstm_forecast = predictor.get_prediction_bidirectlstm()\n plots.lstm_df = lstm_forecast\n else:\n pass\n\n return 'Non tam praeclarum est scire latine, quam turpe nescire'","def predictions(dataframe):\n # Select Features\n features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']]\n\n # Add UNIT to features using dummy variables\n dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\n features = features.join(dummy_units)\n\n # Values\n values = dataframe['ENTRIESn_hourly']\n m = len(values)\n\n features, mu, sigma = normalize_features(features)\n features['ones'] = np.ones(m) # Add a column of 1s (y intercept)\n\n # Convert features and values to numpy arrays\n features_array = np.array(features)\n values_array = np.array(values)\n\n # Set values for alpha, number of iterations.\n alpha = 0.1 # please feel free to change this value\n num_iterations = 75 # please feel free to change this value\n\n # Initialize theta, perform gradient descent\n theta_gradient_descent = np.zeros(len(features.columns))\n theta_gradient_descent, cost_history = gradient_descent(features_array,\n values_array,\n theta_gradient_descent,\n alpha,\n num_iterations)\n\n plot = None\n # -------------------------------------------------\n # Uncomment the next line to see your cost history\n # -------------------------------------------------\n # plot = plot_cost_history(alpha, cost_history)\n #\n # Please note, there is a possibility that plotting\n # this in addition to your calculation will exceed\n # the 30 second limit on the compute servers.\n\n predicted = np.dot(features_array, theta_gradient_descent)\n return predicted, plot","def train_promotion_prediction_model(input_data, input_features, output_features, cat_columns, model,\r\n learning_rate, max_depth, num_leaves, n_iter, n_estimators,\r\n train_size, test_months_exclusion, cat_var_exclusion,\r\n remove_outliers, impact_stores_outlier=None, promo_duration_outlier=None,\r\n discount_depths_outlier=None, min_samples=3):\r\n\r\n # convert input data format\r\n # for cols in input data that is not in the cat cols list, convert to numeric\r\n # for col in list(input_data.columns):\r\n # if col not in list(cat_columns):\r\n input_data['perc_inc_qty'] = pd.to_numeric(input_data['perc_inc_qty'])\r\n input_data['in_leaflet_flag'] = pd.to_numeric(input_data['in_leaflet_flag'])\r\n input_data['in_gondola_flag'] = pd.to_numeric(input_data['in_gondola_flag'])\r\n input_data['duration_wks'] = pd.to_numeric(input_data['duration_wks'])\r\n\r\n # subtract 2 in duration_wks\r\n input_data[\"duration_wks\"] = input_data[\"duration_wks\"].apply(lambda x: x - 2)\r\n\r\n # Check no. of input sample data rows\r\n logger.info(\"Input sample data includes {b} samples...\".format(b=input_data.shape[0]))\r\n\r\n # Lets remove data within the test exclusion months list\r\n if 'campaign_start_month' in list(input_data.columns) and test_months_exclusion is not None:\r\n outliers = input_data[input_data.campaign_start_month.isin(test_months_exclusion)]\r\n logger.info(\r\n \"Removing sample data where campaign start months is in {a}, {b} sample data points removed...\".format(\r\n a=test_months_exclusion,\r\n b=outliers.shape[0]))\r\n input_data = input_data[~input_data['campaign_start_month'].isin(test_months_exclusion)]\r\n\r\n # Lets remove data where store count is below a certain value\r\n if 'no_impacted_stores' in list(input_data.columns) and impact_stores_outlier is not None:\r\n outliers = input_data[input_data['no_impacted_stores'] < impact_stores_outlier]\r\n logger.info(\"Removing sample data where impacted stores < {a}, {b} sample data points removed...\".format(\r\n a=impact_stores_outlier,\r\n b=outliers.shape[0]))\r\n input_data = input_data[input_data['no_impacted_stores'] >= impact_stores_outlier]\r\n\r\n # Lets remove data where duration is above a certain value\r\n if 'duration_days' in list(input_data.columns) and promo_duration_outlier is not None:\r\n outliers = input_data[input_data['duration_days'] > promo_duration_outlier]\r\n logger.info(\"Removing sample data where promotion duration > {a}, {b} sample data points removed...\".format(\r\n a=promo_duration_outlier,\r\n b=outliers.shape[0]))\r\n input_data = input_data[input_data['duration_days'] <= promo_duration_outlier]\r\n\r\n # Lets remove data where discount depth is not in specified list\r\n if 'discount_depth' in list(input_data.columns) and discount_depths_outlier is not None:\r\n outliers = input_data[~input_data.discount_depth.isin(discount_depths_outlier)]\r\n logger.info(\"Removing sample data where discount depth is not in {a}, {b} sample data points removed...\".format(\r\n a=discount_depths_outlier,\r\n b=outliers.shape[0]))\r\n input_data = input_data[input_data.discount_depth.isin(discount_depths_outlier)]\r\n\r\n if remove_outliers:\r\n logger.info(\"Removing outliers from sample data...\")\r\n\r\n # outlier removal based on negative values and where the baseline is smaller than 100\r\n outliers = input_data[(input_data[output_features[0]] <= 0) & (input_data['total_baseline_qty'] < 100)]\r\n logger.info(\r\n \"Removing all negative values from {a} and where the baseline is smaller than 100, \"\r\n \"{b} sample data points removed...\".format(a=output_features[0], b=outliers.shape[0]))\r\n\r\n input_data = input_data[(input_data[output_features[0]] > 0) |\r\n (input_data[output_features[0]] <= 0) & (input_data['total_baseline_qty'] > 100)]\r\n\r\n # zero values which are negative\r\n outliers = input_data[input_data[output_features[0]] < 0]\r\n logger.info(\r\n \"Zeroing all negative values from {a}, \"\r\n \"{b} sample data points removed...\".format(a=output_features[0], b=outliers.shape[0]))\r\n\r\n input_data.loc[input_data[output_features[0]] < 0, output_features[0]] = 0\r\n\r\n # outlier removal based on too high % uplift - set value to 1000%\r\n if 'perc_inc_qty' in list(input_data.columns):\r\n outliers = input_data[input_data['perc_inc_qty'] >= 10]\r\n logger.info(\r\n \"Removing sample data where % qty uplift is greater than 1000%, {b} sample data points removed...\".format(\r\n b=outliers.shape[0]))\r\n input_data = input_data[input_data['perc_inc_qty'] < 10]\r\n\r\n # outlier removal based on quantile in target variable\r\n q = input_data[output_features[0]].quantile(0.95)\r\n\r\n outliers = input_data[input_data[output_features[0]] >= q]\r\n logger.info(\"Based on 95% quantiles, {} sample data points removed...\".format(outliers.shape[0]))\r\n\r\n input_data = input_data[input_data[output_features[0]] < q]\r\n\r\n # Filter on only the input features\r\n total_features = input_features + output_features\r\n input_data = input_data[total_features]\r\n\r\n # Check absent values\r\n null_value_stats_x = input_data[input_features].isnull().sum(axis=0)\r\n logger.info(\"Null values for input features include:\\n{}\".format(null_value_stats_x[null_value_stats_x != 0]))\r\n\r\n null_value_stats_y = input_data[output_features].isnull().sum(axis=0)\r\n logger.info(\"Null values for target variable include:\\n{}\".format(null_value_stats_y[null_value_stats_y != 0]))\r\n\r\n # Throw error if any values are null in y\r\n if input_data[output_features].isnull().values.any():\r\n logger.error(\"Null values found in target data...\")\r\n raise ValueError('Null values found in target data!')\r\n\r\n # Fill remaining absent values in X with -999\r\n input_data.fillna(-999, inplace=True)\r\n\r\n # Describe the dataset\r\n logger.info(\"Summary statistics for numeric features in input data are...\")\r\n logger.info(\"{}\".format(input_data.describe()))\r\n\r\n # Check data types\r\n X = input_data[input_features]\r\n\r\n # If the output feature is uplift %, multiply by 100\r\n if 'p_cal_perc_inc_sale_qty' in output_features:\r\n logger.info(\"Scaling target variable {a} by 100...\".format(a=output_features[0]))\r\n y = input_data[output_features]*100\r\n\r\n else:\r\n y = input_data[output_features]\r\n\r\n logger.info(\"Input dataset data types include:\\n{}\".format(X.dtypes))\r\n logger.info(\"Target variable data types include:\\n{}\".format(y.dtypes))\r\n\r\n # Lets split the data into training and validation sets\r\n X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=train_size, random_state=42)\r\n logger.info(\"Training dataset includes {} samples...\".format(X_train.shape[0]))\r\n logger.info(\"Test dataset includes {} samples...\".format(X_validation.shape[0]))\r\n\r\n # create a mapping dictionary (to be used for models which require int categorical cols)\r\n map_dict = {}\r\n\r\n if model == 'CatBoost':\r\n\r\n # Obtain categorical feature index\r\n cat_features_index = [X.columns.get_loc(c) for c in cat_columns if c in X]\r\n\r\n # initialise CatBoost regressor\r\n train_model = CatBoostRegressor(iterations=700,\r\n learning_rate=learning_rate,\r\n depth=max_depth,\r\n eval_metric='RMSE',\r\n random_seed=42,\r\n bagging_temperature=0.2,\r\n od_type='Iter',\r\n metric_period=75,\r\n od_wait=100)\r\n\r\n # Fit the model - catboost does not require us to specify integers for cat features\r\n train_model.fit(X_train, y_train,\r\n eval_set=(X_validation, y_validation),\r\n cat_features=cat_features_index,\r\n use_best_model=True)\r\n\r\n pred = train_model.predict(X_validation)\r\n\r\n elif model == 'lightgbm':\r\n\r\n # For lightgbm, we need to convert our categorical features to int\r\n # Loop through categorical cols\r\n for col in cat_columns:\r\n if col in list(X.columns):\r\n # get unique values\r\n unique_vals = X[col].unique()\r\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\r\n\r\n # map them for the train and test data sets\r\n X_train = X_train.copy()\r\n X_train[col] = X_train[col].map(unique_vals_dict)\r\n X_validation = X_validation.copy()\r\n X_validation[col] = X_validation[col].map(unique_vals_dict)\r\n\r\n # store the mapping for later use\r\n map_dict[col] = unique_vals_dict\r\n\r\n # LightGBM dataset formatting (with categorical variables)\r\n if cat_var_exclusion:\r\n lgtrain = lgb.Dataset(X_train, y_train,\r\n feature_name=input_features)\r\n lgvalid = lgb.Dataset(X_validation, y_validation,\r\n feature_name=input_features)\r\n else:\r\n cat_col = [col for col in cat_columns if col in list(X.columns)]\r\n\r\n lgtrain = lgb.Dataset(X_train, y_train,\r\n feature_name=input_features,\r\n categorical_feature=cat_col)\r\n lgvalid = lgb.Dataset(X_validation, y_validation,\r\n feature_name=input_features,\r\n categorical_feature=cat_col)\r\n\r\n params = {\r\n 'objective': 'regression',\r\n 'metric': 'rmse',\r\n 'num_leaves': num_leaves,\r\n 'max_depth': max_depth,\r\n 'learning_rate': learning_rate,\r\n 'feature_fraction': 0.8,\r\n 'bagging_fraction': 0.8,\r\n 'bagging_freq': 1,\r\n 'boosting_type': 'gbdt',\r\n 'verbosity': -1\r\n }\r\n\r\n train_model = lgb.train(\r\n params,\r\n lgtrain,\r\n num_boost_round=n_iter,\r\n valid_sets=[lgtrain, lgvalid],\r\n valid_names=[\"train\", \"valid\"],\r\n early_stopping_rounds=1000,\r\n verbose_eval=500\r\n )\r\n\r\n pred = train_model.predict(X_validation)\r\n\r\n elif model == 'xgboost':\r\n\r\n # For xgboost, we need to convert our categorical features to int\r\n # There are 3 approaches - one-hot encode, label encode and binary encode\r\n\r\n # Here, for simplicity, we are using label encoders\r\n # Loop through categorical cols\r\n for col in cat_columns:\r\n if col in list(X.columns):\r\n # get unique values\r\n unique_vals = X[col].unique()\r\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\r\n\r\n # map them for the train and test data sets\r\n X_train = X_train.copy()\r\n X_train[col] = X_train[col].map(unique_vals_dict)\r\n X_validation = X_validation.copy()\r\n X_validation[col] = X_validation[col].map(unique_vals_dict)\r\n\r\n # store the mapping for later use\r\n map_dict[col] = unique_vals_dict\r\n\r\n train_model = xgb.XGBRegressor(objective='reg:linear',\r\n colsample_bytree=0.3,\r\n learning_rate=learning_rate,\r\n max_depth=max_depth,\r\n alpha=10,\r\n n_estimators=n_estimators,\r\n verbosity=2)\r\n\r\n train_model.fit(X_train, y_train)\r\n\r\n pred = train_model.predict(X_validation)\r\n\r\n elif model == 'regression':\r\n\r\n # Use linear regression only if subcategory and brand name and segment is included in the list\r\n # Compute linear regression coefficients for the following combinations\r\n # 1) Subcategory and brand\r\n # 2) Segment\r\n # 3) Subcategory\r\n\r\n # Primary regressor variable includes discount_depth_rank\r\n\r\n # Compute coefficients for the remaining fields in the input data set\r\n # Output the R^2, p_value, and stdev for each combination\r\n # Follow a hierarchy when applying the model to each sku\r\n # If the subcategory and brand the sku sits in has an R^2, p_value and stdev smaller/ larger than a given threshold,\r\n # use, the segment model and likewise, when the segment model has an R^2, p_value and stdev smaller/ larger than a given\r\n # threshold, use the subcategory model\r\n\r\n # We will only thus be able to predict values for segments/ categories and subcategories where there has been a\r\n # promotion in the past\r\n if ('category' not in list(input_data[input_features].columns)) or \\\r\n ('subcategory' not in list(input_data[input_features].columns)) or \\\r\n ('segment' not in list(input_data[input_features].columns)) or \\\r\n ('sku_root_id' not in list(input_data[input_features].columns)) or \\\r\n ('promo_mechanic_dd' not in list(input_data[input_features].columns)):\r\n logger.error(\r\n \"Currently performing a linear regression per subcategory and/ or brand and/ or segment with discount depth rank \"\r\n \"as the primary regressor. However subcategory and brand name and segment and discount depth rank is not defined as \"\r\n \"an input variable!\")\r\n raise ValueError('Subcategory and/ or brand name and/ or segment and discount depth rank is not defined as an input variable')\r\n\r\n # For simplicity, use all data to train the model and compute the R2, stdev, intercept and coefficient\r\n logger.info(\"For regression, both train and test datasets will be used to train the model...\")\r\n logger.info(\"Combined sample dataset includes {} samples...\".format(input_data.shape[0]))\r\n\r\n # Loop through each combination and compute the regression\r\n combination = {('sku_root_id', 'promo_mechanic_dd'): 1, ('segment', 'promo_mechanic_dd'): 2,\r\n ('subcategory', 'promo_mechanic_dd'): 3, ('category', 'promo_mechanic_dd'): 4}\r\n all_combination = ('sku_root_id', 'segment', 'subcategory', 'category', 'promo_mechanic_dd')\r\n\r\n # Convert all categorical variables into numeric for regression\r\n input_data_train = input_data.copy()\r\n for col in cat_columns:\r\n if col in list(input_data.columns) and col not in all_combination:\r\n # get unique values\r\n unique_vals = input_data[col].unique()\r\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\r\n\r\n # map the input dataset\r\n input_data_train[col] = input_data_train[col].map(unique_vals_dict)\r\n\r\n # store the mapping for later use\r\n map_dict[col] = unique_vals_dict\r\n\r\n # Create a dataframe to store the results\r\n train_model_all = pd.DataFrame(columns=['rank', 'agg_fields', 'key', 'model', 'no_data_points', 'outlier_data_points',\r\n 'r2', 'rmse', 'mean_error', 'mae', 'mape', 'f_prob', 'coeff_c0', 'coeff_duration',\r\n 'coeff_in_gondola', 'coeff_in_leaflet', 'p_coeff_c0', 'p_coeff_duration',\r\n 'p_coeff_in_gondola', 'p_coeff_in_leaflet', 'VIF_coeff_c0', 'VIF_coeff_duration',\r\n 'VIF_coeff_in_gondola', 'VIF_coeff_in_leaflet'])\r\n\r\n # Create a dataframe to store the validation set to compute overall metrics\r\n valid_model = pd.DataFrame()\r\n filtered_model = pd.DataFrame()\r\n\r\n for agg_list in combination.keys():\r\n\r\n # Training model for combination\r\n logger.info(\"Training linear regression model for {a}...\".format(a=agg_list))\r\n\r\n # get unique values\r\n unique_df = input_data_train.drop_duplicates(list(agg_list))[list(agg_list)].reset_index(drop=True)\r\n logger.info(\"There are {a} unique {b} in the data...\".format(a=unique_df.shape[0], b=agg_list))\r\n\r\n # group by agg_list\r\n input_data_model = input_data_train.groupby(list(agg_list))\r\n\r\n # Select the list of input attributes not in agg_list\r\n training_list = list(set(list(input_data_train[input_features].columns)) - set(all_combination))\r\n logger.debug(\"Training features include {}\".format(training_list))\r\n\r\n for key, group in input_data_model:\r\n\r\n # Convert key to tuple if not\r\n if not isinstance(key, tuple):\r\n key_t = (key,)\r\n else:\r\n key_t = key\r\n\r\n # Train the model for each group\r\n logger.info(\"Training linear regression model for {a}...\".format(a=key_t))\r\n logger.info(\"There are {a} data samples...\".format(a=group.shape[0]))\r\n\r\n n_data_points = group.shape[0]\r\n\r\n # Lets remove all outlier data points with high z scores\r\n # q_group = group[output_features[0]].quantile(0.95)\r\n #\r\n # outliers = group[group[output_features[0]] >= q_group]\r\n # logger.info(\"Removing outlier data points...\")\r\n # logger.info(\"Based on 95% quantiles, {} sample data points removed...\".format(outliers.shape[0]))\r\n #\r\n # outlier_data_points = outliers.shape[0]\r\n #\r\n # group = group[group[output_features[0]] < q_group]\r\n outlier_data_points = 0\r\n\r\n # If there is less than 3 sample data points, then skip\r\n if group.shape[0] < 2:\r\n logger.info(\"Too few data sample needed for training...\")\r\n logger.info(\"Skipping to next group....\")\r\n\r\n train_model_dict = {'rank': combination[agg_list],\r\n 'agg_fields': agg_list,\r\n 'key': key_t,\r\n 'model': None,\r\n 'no_data_points': group.shape[0],\r\n 'outlier_data_points': outlier_data_points,\r\n 'r2': None,\r\n 'rmse': None,\r\n 'mean_error': None,\r\n 'mae': None,\r\n 'mape': None,\r\n 'f_prob': None,\r\n 'coeff_c0': None,\r\n 'coeff_duration': None,\r\n 'coeff_in_gondola': None,\r\n 'coeff_in_leaflet': None,\r\n 'p_coeff_c0': None,\r\n 'p_coeff_duration': None,\r\n 'p_coeff_in_gondola':None,\r\n 'p_coeff_in_leaflet':None,\r\n 'VIF_coeff_c0':None,\r\n 'VIF_coeff_duration':None,\r\n 'VIF_coeff_in_gondola':None,\r\n 'VIF_coeff_in_leaflet':None\r\n }\r\n\r\n # add train model to dataframe\r\n train_model_all = train_model_all.append(train_model_dict, ignore_index=True)\r\n continue\r\n\r\n # Append group to validation dataset\r\n v_group = group[list(all_combination)].copy()\r\n valid_model = valid_model.append(v_group.drop_duplicates())\r\n filtered_model = filtered_model.append(group)\r\n n_data_points_used = group.shape[0]\r\n\r\n t_list = training_list+output_features\r\n group = group[t_list]\r\n\r\n # plot the relationship\r\n # plotScatter(group, \"in_gondola_flag\", output_features[0], \"duration_wks\")\r\n\r\n X_reg = group[training_list]\r\n\r\n # force to add constant if a constant values is already supplied\r\n # this will ensure consistency in output format\r\n X_reg = sm.add_constant(X_reg, has_constant='add')\r\n y_reg = group[output_features]\r\n\r\n # Train robust linear regression model\r\n\r\n reg_model = sm.OLS(y_reg, X_reg).fit()\r\n logger.info(\"Completed model training...\")\r\n\r\n # Log the model results\r\n logger.debug(\"Model results...\")\r\n logger.debug(\"\\n{}\".format(reg_model.summary()))\r\n\r\n mape = np.divide(reg_model.resid.values,\r\n y_reg[output_features[0]].values)\r\n mape[mape == np.inf] = 0\r\n mape[mape == -np.inf] = 0\r\n mape = np.median(np.abs(mape))\r\n\r\n # Compute variance inflation factors\r\n VIF = [variance_inflation_factor(X_reg.values, i) for i in range(X_reg.shape[1])]\r\n\r\n train_model_dict = {'rank': combination[agg_list],\r\n 'agg_fields': agg_list,\r\n 'key': key_t,\r\n 'model': reg_model,\r\n 'no_data_points': n_data_points_used,\r\n 'outlier_data_points': outlier_data_points,\r\n 'r2': reg_model.rsquared,\r\n 'rmse': np.sqrt(np.mean(np.square(reg_model.resid.values))),\r\n 'mean_error': np.mean(reg_model.resid.values),\r\n 'mae': np.median(np.abs(reg_model.resid.values)),\r\n 'mape': mape,\r\n 'f_prob': reg_model.f_pvalue,\r\n 'coeff_c0': reg_model.params['const'],\r\n 'coeff_duration': reg_model.params['duration_wks'],\r\n 'coeff_in_gondola': reg_model.params['in_gondola_flag'],\r\n 'coeff_in_leaflet': reg_model.params['in_leaflet_flag'],\r\n 'p_coeff_c0': reg_model.pvalues['const'],\r\n 'p_coeff_duration': reg_model.pvalues['duration_wks'],\r\n 'p_coeff_in_gondola':reg_model.pvalues['in_gondola_flag'],\r\n 'p_coeff_in_leaflet':reg_model.pvalues['in_leaflet_flag'],\r\n 'VIF_coeff_c0':VIF[0],\r\n 'VIF_coeff_duration':VIF[3],\r\n 'VIF_coeff_in_gondola':VIF[2],\r\n 'VIF_coeff_in_leaflet':VIF[1]}\r\n\r\n # add train model to dataframe\r\n train_model_all = train_model_all.append(train_model_dict, ignore_index=True)\r\n\r\n\r\n if model in ('catboost', 'lightgbm', 'xgboost'):\r\n\r\n # Evaluate the model\r\n rmse = np.sqrt(mean_squared_error(y_validation, pred))\r\n logger.info(\"RMSE: {}\".format(rmse))\r\n\r\n mean_error = np.mean(y_validation[output_features[0]].values - pred)\r\n logger.info(\"Mean Error: {}\".format(mean_error))\r\n\r\n mae = np.median(np.absolute(y_validation[output_features[0]].values - pred))\r\n logger.info(\"MAE: {}\".format(mae))\r\n\r\n mape = np.divide(y_validation[output_features[0]].values - pred, y_validation[output_features[0]].values)\r\n mape[mape == np.inf] = 0\r\n mape[mape == -np.inf] = 0\r\n mape = np.median(np.abs(mape))\r\n logger.info(\"MAPE: {}%\".format(mape * 100))\r\n\r\n val_std = np.std(y_validation[output_features[0]].values)\r\n logger.info(\"Benchmark STD: {}\".format(val_std))\r\n\r\n val_mean = np.mean(y_validation[output_features[0]].values)\r\n logger.info(\"Benchmark Mean Error: {}\".format(val_mean))\r\n\r\n val_mae = np.mean(np.absolute(y_validation[output_features[0]].values))\r\n logger.info(\"Benchmark MAE: {}\".format(val_mae))\r\n\r\n logger.info(\"Benchmark MAPE: -100%\")\r\n\r\n # plot the results\r\n plothist(y_validation, pred)\r\n\r\n # plot the feature importance\r\n plotImp(model, train_model, X, num=20)\r\n\r\n join_fields = None\r\n filtered_model = None\r\n\r\n\r\n elif model in ('regression'):\r\n\r\n # save the full model\r\n train_model_all.to_csv(\"train_model_all.csv\", encoding='utf-8', index=False)\r\n\r\n # get the unique values in the validation model\r\n valid_model = valid_model.drop_duplicates(list(all_combination))[list(all_combination)].reset_index(drop=True)\r\n valid_model['p_id'] = valid_model[list(all_combination)].apply(tuple, axis=1)\r\n\r\n # For each line in valid_model, find the corresponding rows in train model where key is in p_id\r\n train_model = pd.DataFrame()\r\n logger.info(\"Computing best model for each {a}\".format(a=all_combination))\r\n for index, row in valid_model.iterrows():\r\n\r\n # logging progress\r\n logger.info(\"{a} out of {b} permutations complete...\".format(a=index, b=valid_model.shape[0]))\r\n\r\n # find all the rows in train model that satisfy the criterion\r\n train_model_all['valid'] = train_model_all.apply(lambda x: set(x.key).issubset(row.p_id), axis=1)\r\n\r\n # filter on the rows that are true\r\n valid_rows = train_model_all[train_model_all['valid'] == True]\r\n\r\n # # Using the rank condition, lets filter on only the model which has the most favorable property\r\n # # R2 threshold > 0.2 and\r\n # if len(valid_rows[(valid_rows['r2'] >= 0.2)]) >= 1:\r\n # valid_rows = valid_rows[valid_rows['r2'] >= 0.2]\r\n\r\n # valid_rows = valid_rows.sort_values(by='mape', ascending=True).head(1)\r\n\r\n # Using the rank condition, lets filter where the # data samples is > min_data samples, then sort by rank\r\n # and take the bottom row\r\n if len(valid_rows[(valid_rows['no_data_points'] >= min_samples)]) >= 1:\r\n valid_rows = valid_rows[valid_rows['no_data_points'] >= min_samples]\r\n valid_rows['valid_dp'] = True\r\n else:\r\n valid_rows['valid_dp'] = False\r\n\r\n valid_rows = valid_rows.sort_values(by='rank', ascending=True).head(1)\r\n\r\n # Include the valid model all combinations cols\r\n valid_rows.reset_index(drop=True, inplace=True)\r\n row_df = row.to_frame().T\r\n row_df.reset_index(drop=True, inplace=True)\r\n\r\n valid_rows = pd.concat([valid_rows, row_df], axis=1)\r\n\r\n train_model = train_model.append(valid_rows)\r\n\r\n # Compute aggregate statistics\r\n rmse = train_model['rmse'].mean()\r\n logger.info(\"RMSE: {}\".format(rmse))\r\n\r\n mean_error = train_model['mean_error'].mean()\r\n logger.info(\"Mean Error: {}\".format(mean_error))\r\n\r\n mae = train_model['mae'].median()\r\n logger.info(\"MAE: {}\".format(mae))\r\n\r\n mape = train_model['mape'].median()\r\n logger.info(\"MAPE: {}%\".format(mape * 100))\r\n\r\n mae_std = train_model['mae'].std()\r\n logger.info(\"MAE_std: {}\".format(mae_std))\r\n\r\n mape_std = train_model['mape'].std()\r\n logger.info(\"MAPE_std: {}%\".format(mape_std * 100))\r\n\r\n R2_avg = train_model['r2'].mean()\r\n logger.info(\"R^2_avg: {}\".format(R2_avg))\r\n\r\n join_fields = list(all_combination)\r\n\r\n return train_model, map_dict, mae, mape, join_fields, filtered_model","def df_monthly_anomaly(data_store, hemi, month):\n df = sit.monthly(hemisphere=hemi, data_store=data_store, month_num=month)\n\n # scale to millions of sq km\n df['extent'] = df.total_extent_km2 / 1e6\n\n # percentage difference between the climatological mean and the extent for a\n # given month\n df['anomaly'] = sit.monthly_percent_anomaly(df.extent, nt.DEFAULT_CLIMATOLOGY_YEARS)\n\n # trendline for anomaly\n df['anomaly_trend'] = sit.trend(df.anomaly)\n\n df = df[['extent', 'anomaly', 'anomaly_trend']]\n\n return df","def predict():\n ### YOUR CODE GOES HERE\n\n predictions = list(np.random.randint(low=0, high=1, size=2073))\n ### YOUR CODE ENDS HERE\n print(predictions) # Should be a dictionary of forecasts\n # i.e. {\"id1\" : forecast, \"id2\": forecast, ...}\n return predictions","def prob():\n # Get decision score for our example that came with the request\n data = flask.request.json\n #data[]\n year = data[\"year\"]\n Month = data[\"month\"]\n Day = data[\"day\"]\n species = data[\"species\"]\n lat = data[\"lattitude\"]\n lng = data[\"longitude\"]\n\n # find the closest trap to the clicked location\n closest_trap = find_closest_trap(lat,lng,trap_stations)\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\n \n # add 0 in front of month\n month = \"0\"+ Month if len(Month) == 1 else Month\n day = \"0\"+ Day if len(Day) == 1 else Day\n date = year + \"-\" + month + \"-\" + day\n trap[\"date\"] = date\n\n # convert datetime to str to match differing formats\n weather.station = weather.station.astype(str)\n trap.station = trap.station.astype(int).astype(str)\n weather.date = weather.date.astype(str)\n\n # merge trap & weather dataframes\n merged = trap.merge(weather,on=[\"date\",\"station\"],how=\"inner\")\n\n # convert back datetime\n merged.date = pd.to_datetime(merged.date)\n\n # extract features from datetime and dewpoint\n merged['month'] = month\n merged['year'] = year\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\n # add risk feature if month is between june and sept (peak virus season)\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\n\n # Reorder columns & Assign to X\n merged2 = merged.drop([\"date\", \"year\", \"trap\",\"species\"],1)\n cols = [\"month\",\"week\",\"longitude\",\"latitude\",\"tmax\",\"tmin\",\"tavg\",\"depart\",\"dewpoint\",\n \"wetbulb\",\"heat\",\"cool\",\"sunrise\",\"sunset\",\"preciptotal\",\"resultspeed\",\"avgspeed\",\n \"month\",\"dry\",\"risk\"]\n X = merged2[cols]\n \n # predict probability\n pred = loaded_model.predict_proba(X)\n prob = round(pred[0][1]*100,2)\n return flask.jsonify(prob)","def forecast_plot(df, predictions):\n df['Forecast'] = np.nan\n\n last_date = df.iloc[-1].name\n last_unix = last_date.timestamp()\n one_day = 86400 # sec in day\n next_unix = last_unix + one_day\n\n for i in predictions:\n next_date = datetime.datetime.fromtimestamp(next_unix)\n next_unix += one_day\n df.loc[next_date] = [np.nan for _ in range(len(df.columns) - 1)] + [i]\n\n plt.plot(df['Close'])\n plt.plot(df['Forecast'])\n plt.legend(bbox_to_anchor=(1.01, 1))\n plt.xlabel('Time(Yr-M)')\n plt.ylabel('Value(USD)')\n plt.show()","def forecast(self, stock):\n # Load the trained model\n model_handler = ModelHandler()\n model = model_handler.load_json_model(stock)\n\n # Importing the training set\n dataset = pd.read_csv(stock.csv_name)\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\n\n # Create the test dataset\n dataset_test = dataset[len(dataset) - 30:]\n real_stock_price = dataset_test.iloc[:, 1:2].values\n dataset = dataset['Open']\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\n inputs = inputs.reshape(-1, 1)\n\n # Feature Scaling\n sc = MinMaxScaler(feature_range=(0, 1))\n inputs = sc.fit_transform(inputs)\n\n x_test = []\n x_test.append(inputs[0:60, 0])\n predicted_values = []\n for i in range(1, 31):\n x_test_np = np.array(x_test)\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\n new_data = model.predict(x_test_np)\n predicted_values.append(new_data[0])\n x_test[0] = np.delete(x_test[0], 0)\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\n\n predicted_values = sc.inverse_transform(predicted_values)\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\n plt.gcf().autofmt_xdate()\n plt.title(f'{stock.ticker} Stock Price Prediction')\n plt.xlabel('Time')\n plt.ylabel(f'{stock.ticker} Stock Price')\n plt.legend()\n plt.show()","def predict(self, requested_day):\n\n df = load_data()\n\n # preprocess\n df = preprocess(df)\n df = filter_by_country(df, self.country_code)\n\n # separate cases from data\n dates, Y = separate(df)\n\n # normalize Y\n Y = normalize(Y)\n\n # apply look_back and generate needed samples\n X, _ = apply_lookback(Y, look_back=self.look_back)\n\n # reshape to fit the model\n X = reshape(X)\n dates = reshape(dates)\n dates = dates[self.look_back:]\n\n # unite samples (X) with dates\n # a date of a sample corresponds to the future prediction value (Y)\n united_samples = unite_dates_samples(dates.reshape(-1, 1),\n X.reshape(-1, self.look_back))\n\n last_day = requested_day\n predicted = 0\n\n for step in range(self.look_forward):\n sample, last_day = get_sample(united_samples, last_day)\n sample = sample.reshape(1, 1, self.look_back)\n\n predicted = self.model.predict(sample.astype(np.float32))\n\n united_samples, last_day = append_sample(united_samples, predicted,\n self.look_back,\n last_day)\n\n # date of the prediction\n last_day = change_date(last_day, delta_days=-1)\n\n # chosen starting date\n start_avail_day = change_date(last_day,\n delta_days=-self.look_back)\n\n # convert to a real number of COVID-19 cases\n predicted = denormalize(predicted)[0, 0]\n predicted = int(predicted)\n\n prediction_info = {\n 'prediction_date': last_day,\n 'starting_date': start_avail_day,\n 'prediction_new_cases': predicted\n }\n\n return prediction_info, united_samples","def do_forecasting(self, df_forecast, mon_names, available_target=False, name_target='yield'):\n data = df_forecast.as_matrix(columns=self.vars_features) # convert dataframe column to matrix\n predicted = self.model.predict(data)\n\n if available_target:\n expected = df_forecast.as_matrix(columns=[name_target]).ravel()\n if not self.classify: # REGRESSION\n # Compute stats\n results = compute_stats.ols(predicted.tolist(), expected.tolist())\n bias = compute_stats.bias(predicted, expected)\n rmse = compute_stats.rmse(predicted, expected)\n mae = compute_stats.mae(predicted, expected)\n\n # Plot!\n plot.plot_regression_scatter(expected, np.asarray(predicted),\n annotate=r'$r^{2}$ ' + '{:0.2f}'.format(results.rsquared) + '\\n' +\n 'peak NDVI date: ' + self.time_peak_ndvi.strftime('%b %d'),\n xlabel='Expected yield',\n ylabel='Predicted yield',\n title=mon_names + ' ' + str(int(df_forecast[self.season].unique()[0])),\n fname=self.task + '_' + '_'.join([mon_names]) + '_' + self.crop,\n out_path=self.path_out_dir)\n\n # global expected vs predicted\n if self.debug:\n # any non-existing index will add row\n self.df_global.loc[len(self.df_global)] = [np.nanmean(expected), np.nanmean(predicted), mon_names,\n self.forecast_yr]\n\n return predicted, {'RMSE': rmse, 'MAE': mae, r'$r^{2}$': results.rsquared, 'Bias': bias}\n else: # CLASSIFICATION\n # Convert from crop condition class (e.g. 4) to string (e.g. exceptional)\n expected, predicted = compute_stats.remove_nans(expected, predicted)\n cm = confusion_matrix(expected, predicted, labels=self.dict_cc.keys()).T\n\n # Compute and plot class probabilities\n proba_cc = self.model.predict_proba(data)\n df_proba = pd.DataFrame(proba_cc, columns=self.dict_cc.values())\n plot.plot_class_probabilities(df_proba, fname='proba_' + '_'.join([mon_names]) + '_' + self.crop,\n out_path=self.path_out_dir)\n\n # Plot confusion matrix\n plot.plot_confusion_matrix(cm, normalized=False, fname='cm_' + '_'.join([mon_names]) + '_' + self.crop,\n xlabel='True class', ylabel='Predicted class', ticks=self.dict_cc.values(),\n out_path=self.path_out_dir)\n\n # Normalize and plot confusion matrix\n cm_normalized = normalize(cm.astype(float), axis=1, norm='l1')\n plot.plot_confusion_matrix(cm_normalized, fname='norm_cm_' + '_'.join([mon_names]) + '_' + self.crop,\n xlabel='True class', ylabel='Predicted class', normalized=True,\n ticks=self.dict_cc.values(), out_path=self.path_out_dir)\n\n score_accuracy = accuracy_score(expected, predicted) * 100.0\n score_precision = precision_score(expected, predicted, average='weighted') * 100.0\n return predicted, {'Accuracy': score_accuracy, 'Precision': score_precision}\n else:\n return predicted, {'RMSE': np.nan, 'MAE': np.nan, r'$r^{2}$': np.nan, 'Bias': np.nan,\n 'Nash-Sutcliff': np.nan}","def prediction(home, away, stats, model):\n\n # Define categorical variables and initiate year variable.\n categorical = ['buildUpPlaySpeedClass_home', 'buildUpPlayDribblingClass_home',\n 'buildUpPlayPassingClass_home', 'buildUpPlayPositioningClass_home', \n 'chanceCreationPassingClass_home', 'chanceCreationCrossingClass_home', \n 'chanceCreationShootingClass_home', 'chanceCreationPositioningClass_home',\n 'defencePressureClass_home', 'defenceAggressionClass_home', \n 'defenceTeamWidthClass_home', 'defenceDefenderLineClass_home', \n 'team_long_name_home', 'buildUpPlaySpeedClass_away',\n 'buildUpPlayDribblingClass_away', 'buildUpPlayPassingClass_away', \n 'buildUpPlayPositioningClass_away', 'chanceCreationPassingClass_away', \n 'chanceCreationCrossingClass_away', 'chanceCreationShootingClass_away',\n 'chanceCreationPositioningClass_away', 'defencePressureClass_away', \n 'defenceAggressionClass_away', 'defenceTeamWidthClass_away', \n 'defenceDefenderLineClass_away', 'team_long_name_away']\n year = datetime.datetime.now().year\n\n # Create dummy variables.\n stats = pd.get_dummies(stats, columns=categorical)\n\n # Map team attributes and playing styles for home and away teams.\n test = stats[(stats['team_long_name_home_' + home.replace(' ', '')] == 1)\n & (stats['team_long_name_away_' + away.replace(' ', '')] == 1)]\n test['season_year'] = year\n\n # Make prediction from the loaded logistic regression model.\n predict = model.predict_proba(test)[0]\n\n return predict","def forc_model(self):\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\n for add in self.X.address1.unique():\n add_mask = self.X.address1 == add\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\n top_index = foo.index[0]\n clust = int(foo.cluster.values[0])\n # get values from last visit for store\n base_input = foo[self.model_mask_cols].values[0]\n base_actual = self.y[top_index]\n lag2_val = base_input[lag1_loc]\n lag1_val = base_actual\n\n for i in range(1, self.num_periods + 1):\n model = self.model_list[clust]\n inputs = base_input\n inputs[lag1_loc] = lag1_val\n inputs[lag2_loc] = lag2_val\n \n pred = model.predict(inputs.reshape(1, -1))\n self._update_cust_table(add, i, pred)\n \n lag2_val = lag1_val\n lag1_val = pred","async def _forecast_single(\n self,\n model: Prophet\n ) -> pd.DataFrame:\n future = model.make_future_dataframe(self._periods, 'H', False)\n return model.predict(future)","def train_predict(train_file_arg):\n\n # Load command line arguments\n train_file = train_file_arg\n parameter_file = \"prophet/lstm/trainingConfig.json\"\n\n # Load training parameters\n params = json.loads(open(parameter_file).read())\n\n # Load time series dataset, and split it into train and test\n x_train, y_train, x_test, _, x_test_raw, _,\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\n\n # Build RNN (LSTM) model\n lstm_layer = [1, params[\"window_size\"], params[\"hidden_unit\"], 1]\n model = buildModel.rnn_lstm(lstm_layer, params)\n\n # Train RNN (LSTM) model with train set\n model.fit(\n x_train,\n y_train,\n batch_size=params[\"batch_size\"],\n epochs=params[\"epochs\"],\n validation_split=params[\"validation_split\"])\n\n # Check the model against test set\n predicted = buildModel.predict_next_timestamp(model, x_test)\n predicted_raw = []\n for i in range(len(x_test_raw)):\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\n\n # Predict next time stamp\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\n print(\"The next time stamp forecasting is: {}\".format(next_timestamp_raw))\n\n # Add 5 minutes for a new timestamp of predictions\n last_datetime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(last_datetime_epoch))\n last_datetime = datetime.strptime(last_datetime, \"%Y-%m-%d %H:%M:%S\")\n new_datetime = last_datetime + timedelta(seconds=300)\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\n new_datetime = new_datetime.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Concatenate datetime and price forecast\n new_entry = \"coin_name,\" + str(new_datetime_epoch) + \",\" \\\n + str(next_timestamp_raw) + \",coin_supply\" + \",coin_mc\" + \"\\n\"\n\n # Write to CSV file of new prediction\n fd = open(train_file, \"a\")\n fd.write(new_entry)\n fd.close()\n\n # Return new prediction\n return [new_datetime, str(next_timestamp_raw)]","def _predict(self):\n bull_wr = self.bull_win_rate\n self.df = self.df.assign(bull_wr=bull_wr)","def naive_forecasting(x_test, y_test):\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred","def compute(self, today, asset_ids, out, high):\n start_month = today.month\n current_month = start_month - 1 if start_month - 1 > 0 else 12\n prev_month = current_month - 1 if current_month - 1 > 0 else 12\n idx = -1\n date_idx = today - 1\n month_idx = date_idx.month\n\n current_end_month_idx = 0\n current_start_month_idx = 0\n\n while idx > -MonthlyHigh.window_length:\n if month_idx == current_month:\n if current_end_month_idx == 0:\n current_end_month_idx = idx\n if month_idx == prev_month:\n if current_start_month_idx == 0:\n current_start_month_idx = idx + 1\n\n date_idx = date_idx - 1\n month_idx = date_idx.month\n idx = idx - 1\n\n if current_end_month_idx == -1:\n current_month_high = high[current_start_month_idx:, :].max(\n axis=0)\n # current_month_low = low[current_start_month_idx:, :].min(axis=0)\n else:\n current_month_high = high[current_start_month_idx:current_end_month_idx + 1, :].max(\n axis=0)\n\n # current_month_low = low[current_start_month_idx:current_end_month_idx + 1, :].min(axis=0)\n out[:] = current_month_high","def arima(X):\n \n sq_preds = status_quo_model(X)\n X[\"id\"] = X.index\n # get it to long form\n longX = pd.melt(X, id_vars=['id'], value_vars=list(X.columns.values)[:-1])\n # change the default column name to a proper one\n longX['year'] = longX['variable']\n longX.drop(columns = 'variable', inplace=True)\n # sort by id and year so that we can fit individual time series models for each indicator\n longX = longX.sort_values(['id', 'year'])\n # group\n grouped = longX.groupby('id')['value']\n # for each group (a country-indicator combination)\n forecasts = list()\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\")\n for k, g in grouped:\n g = g[-5:]\n gi = g.interpolate(method = 'linear', limit = 50, limit_direction = 'backward')\n model = ARIMA(gi, order=(2,1,0))\n try:\n results = model.fit(disp = 0)\n forecasts.append(results.forecast()[0][0])\n except (ValueError, numpy.linalg.linalg.LinAlgError) as e:\n forecasts.append(sq_preds[k])\n return forecasts","def __init__(self,\n model_configuration,\n app_id=None,\n predict_interval=PREDICT_INTERVAL,\n metric=None):\n super(MidtermArmaPredictor, self).__init__(model_configuration, app_id,\n predict_interval, metric)","def predict(self, instances):\r\n raise NotImplementedError","def predict():\n\n\n json_payload = request.json\n #LOG.info(f\"JSON payload: %s\" %json_payload)\n inference_payload = pd.DataFrame(json_payload)\n #LOG.info(\"inference payload DataFrame: %s\" %inference_payload)\n scaled_payload = scale(inference_payload)\n prediction = list(clf.predict(scaled_payload))\n return jsonify({'prediction': prediction})","def predict(request, route_id, stop_id, planned_arrival, day_of_week, month,\n departure_time_seconds, t_minus_1118):\n\n stop_lat_lon_dict = '/home/student/analytics/ml_dictionaries/db_stop_latlon.json'\n\n cluster_dict = '/home/student/analytics/ml_dictionaries/stops_to_cluster_300m_radius.json'\n\n labels_dir = '/home/student/analytics/models_label_encodings/'\n\n models_dir = '/home/student/analytics/models/'\n\n # this call updates the weather attributes to the current time and / or day\n # implement front-end check to queries to JSON response out of range\n\n time = Time()\n time.find_date(departure_time_seconds)\n time.check_day(day_of_week)\n\n print(time.day_of_week, departure_time_seconds)\n weather.update_rain_temp(time.day_of_week, departure_time_seconds)\n\n # this instantiates our formatter object with prediction inputs\n\n formatter = FormatInput(route_id,\n stop_id,\n planned_arrival,\n weather.rain,\n weather.temp,\n time.day_of_week,\n month,\n t_minus_1118,\n time.date,\n stop_lat_lon_dict,\n cluster_dict)\n\n # this adds and transforms features in preparation for prediction\n\n formatter.derive_features()\n\n # this label encodes the appropriate categorical variables\n\n formatter.label_encode(labels_dir)\n\n # this creates an input vector attribute to pass to the predictor\n formatter.create_vector()\n\n # instantiate a predictor object\n\n predictor = Predict()\n\n # generate a prediction for a line, given a vector which is passed\n\n result = predictor.predict(models_dir, formatter.line, formatter.vector)\n\n # return the prediction\n\n return HttpResponse(result)","def makePrediction(toPredict,candidateList):\n\n print(\"Predicting for: \" + toPredict['title'])\n\n if PREDICT_RATING:\n actualRating = float(toPredict['actual_imdb_rating'])\n predictedRating = round(predictRating(toPredict, candidateList),1)\n percentDiff = round((actualRating - predictedRating) / actualRating * 100, 2)\n print(\"Predicted rating: \" + str(predictedRating) + \" Actual Rating: \" + str(actualRating))\n print(\"Difference in rating prediction: \" + str(percentDiff) + \"%\")\n else:\n predictedRating = round(predictRating(toPredict, candidateList), 1) # using rating to prune unrelated movies.\n actualRevenue = int(toPredict['actual_revenue'])\n predictedRevenue = int(predictRevenue(toPredict,candidateList,predictedRating) * float(toPredict['budget'])) #PredictRevenue method return mean RoR\n percentDiff = round((actualRevenue - predictedRevenue) / actualRevenue * 100, 2)\n print(str(\"Predicted revenue: \" + str(predictedRevenue) + \" Actual Revenue: \" + str(actualRevenue)))\n print(\"Difference in revenue prediction: \" + str(percentDiff) + \"%\")\n\n #Tabulate how close the prediction was based on percent difference\n if abs(percentDiff) <= 5:\n withinFive.append(0)\n\n if abs(percentDiff) <= 10:\n withinTen.append(0)\n\n if abs(percentDiff) <= 15:\n withinFifteen.append(0)\n\n if abs(percentDiff) <= 20:\n withinTwenty.append(0)\n\n if abs(percentDiff) <= 30:\n withinThirty.append(0)","def make_predictions(df, model):\n predictions = pd.DataFrame({\"move\": model.predict(df.drop(\"move\", axis=1))})\n predictions[\"move\"] = predictions[\"move\"].apply(lambda x: round(x))\n return pd.concat([df[[\"brand\", \"store\", \"week\"]].reset_index(drop=True), predictions], axis=1)","def predict_ensemble(test_df):\n instances = [wongnai_predictor._dataset_reader.text_to_instance(word_tokenize(review)) \n for review in list(test_df.review)]\n model_paths = glob('output_*/model.tar.gz')\n all_predicted_labels = []\n for model_path in model_paths:\n archive = load_archive(model_path) # load trained model\n wongnai_predictor = Predictor.from_archive(archive, 'wongnai_predictor')\n predicted_labels = [int(wongnai_predictor.predict_instance(instance)['predicted_label']) \n for instance in instances]\n all_predicted_labels.append(predicted_labels)\n all_predicted_labels = np.array(all_predicted_labels)\n predicted_labels_vote = mode(np.array(all_predicted_labels).T, axis=-1).mode.ravel()\n test_df['rating'] = predicted_labels_vote\n return test_df.drop('review', axis=1)","def predict(data: pd.DataFrame) -> pd.DataFrame:\n return pd.DataFrame(data={\"prediction\": trained_model.predict(data)})","def make_ensemble_prediction(trn_features, models, test_df):\n test_id: pd.Series = test_df.iloc[:, 0]\n test_features: pd.DataFrame = test_df.iloc[:, 1:]\n\n test_features = pd.concat([trn_features, test_features])\n\n clean_test_features, test_log = clean_features(test_features)\n clean_test_features = clean_test_features.iloc[trn_features.shape[0]:, :]\n\n predictions = np.empty([0, trn_features.shape[0] - 1])\n for model in models:\n prediction = np.array([model.predict(clean_test_features)])\n predictions = np.vstack([predictions, prediction])\n\n prediction = np.mean(predictions, axis=0)\n\n # print(predictions, prediction)\n result_df = pd.DataFrame(columns=[\"Id\", \"SalePrice\"])\n\n for i in range(test_id.shape[0]):\n result_df = result_df.append(pd.DataFrame([[int(test_id.iloc[i]), prediction[i]]], columns=[\"Id\", \"SalePrice\"]))\n\n print(result_df)\n result_df.to_csv('../data/result.csv', index=False)","def test_monthly_resolution_hindcast(monthly_initialized, monthly_obs):\n monthly_initialized.lead.attrs[\"units\"] = \"months\"\n assert compute_hindcast(monthly_initialized, monthly_obs).all()","def rolling_arima(train, test, endog, order, exog=None, dates=None, freq=None, missing='none'):\n history = list(train)\n predictions = []\n for row in test:\n model = sm.tsa.ARIMA(endog=history, order=order, exog=exog, dates=dates, freq=freq, missing=missing)\n model_fit = model.fit(disp=0)\n output = model_fit.forecast()\n yhat = output[0][0]\n predictions.append(yhat)\n history.append(row)\n\n return predictions","def build_predictions() -> List[str]:\n\n with open(os.path.join(UTILS_PATH, STREETS_FOR_PREDICTIONS_FILE)) as f:\n streets_for_predictions = f.read().split('\\n')[:-1]\n\n street_to_id = {street: ind for ind, street in enumerate(streets_for_predictions)}\n\n yesterday = datetime.today() - timedelta(days=7) # shift for 7 days\n last_month = set([(yesterday - timedelta(days=x)).strftime(DAY_FORMAT) for x in range(30)])\n\n with open(os.path.join(UTILS_PATH, CURRENT_FILENAMES_FILE), \"r\") as f:\n FILENAMES = json.load(f)\n\n filename = FILENAMES[CRASHES_AFTER_ETL]\n conn = sql.connect(f\"{METABASE_DATA_PATH}{filename}.db\")\n query = f\"{SELECT_DATETIME_STREETS_QUERY} '{filename}'\"\n df = pd.read_sql_query(query, conn)\n\n df[DATETIME] = df[DATETIME].apply(lambda x: x.split(\"T\")[0])\n df = df[df[STREET_NAME].isin(streets_for_predictions)]\n df = df.reset_index(drop=True)\n\n df = df[df[DATETIME].isin(last_month)]\n df[DATETIME] = pd.to_datetime(df[DATETIME])\n df = df.reset_index(drop=True)\n\n uniq_dates = sorted(set(df[DATETIME]))\n date_to_id = {date: idd for idd, date in enumerate(uniq_dates)}\n\n X = np.zeros((len(street_to_id), len(date_to_id)), dtype=np.uint32)\n\n for row in df.iterrows():\n info = row[1]\n date = info[DATETIME]\n street = info[STREET_NAME]\n\n date_id = date_to_id[date]\n street_id = street_to_id[street]\n\n X[street_id, date_id] += 1\n\n model = CatBoostRegressor()\n model.load_model(os.path.join(UTILS_PATH, PREDICTOR_FILE))\n\n y_pred = model.predict(X)\n top_10_streets_inds = np.argsort(y_pred)[::-1][:10]\n top_10_streets = [streets_for_predictions[ind] for ind in top_10_streets_inds]\n\n logging.info(\"Predictions successfully made.\")\n\n return top_10_streets","def test_03_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n \n self.assertTrue(os.path.exists(log_file))","def rank_pre_extract(self, mention_data, predictions):\n mdata = pd.DataFrame(mention_data)\n\n\n\n\n pass","def predict(self, y_hat_df):\n check_is_fitted(self, 'fitted_models_')\n\n y_hat_df = deepcopy(y_hat_df[['unique_id', 'ds']])\n\n forecasts = []\n uids = []\n dss = []\n name_models = []\n for ts, name_model in product(y_hat_df.groupby('unique_id'), self.models.keys()):\n uid, df = ts\n h = len(df)\n model = self.fitted_models_.loc[(uid, name_model)]\n model = model.item()\n y_hat = dask.delayed(model.predict)(h)\n forecasts.append(y_hat)\n uids.append(np.repeat(uid, h))\n dss.append(df['ds'])\n name_models.append(np.repeat(name_model, h))\n\n forecasts = dask.delayed(forecasts).compute(scheduler=self.scheduler)\n forecasts = zip(uids, dss, name_models, forecasts)\n\n forecasts_df = []\n for uid, ds, name_model, forecast in forecasts:\n dict_df = {'unique_id': uid,\n 'ds': ds,\n 'model': name_model,\n 'forecast': forecast}\n df = dask.delayed(pd.DataFrame.from_dict)(dict_df)\n forecasts_df.append(df)\n\n forecasts = dask.delayed(forecasts_df).compute()\n forecasts = pd.concat(forecasts)\n\n forecasts = forecasts.set_index(['unique_id', 'ds', 'model']).unstack()\n forecasts = forecasts.droplevel(0, 1).reset_index()\n forecasts.columns.name = ''\n\n\n return forecasts","def predict(self, df):\n results = [] \n _ds = pdfds.DataFrameDataset(df, self.fields) \n _iter = BucketIterator(_ds, batch_size=16, sort_key=lambda x: len(x.text),\n train=False, sort=True, sort_within_batch=True)\n self.odel.eval()\n with torch.no_grad():\n for (labels, text), _ in _iter:\n labels = labels.type(torch.LongTensor)\n text = text.type(torch.LongTensor)\n _, output = self.model(text, labels)\n sm = torch.nn.Softmax(dim=1)\n results.extend( sm(output).tolist()[1] )\n return results","def fit_and_predict(df, \n outcome: str='deaths', \n method: str='exponential', \n mode: str='predict_future', \n target_day: np.ndarray=np.array([1]),\n output_key: str=None,\n demographic_vars=[],\n verbose: bool=False):\n \n assert mode == 'predict_future' or mode == 'eval_mode', 'unknown mode'\n if output_key is None:\n output_key = f'predicted_{outcome}_{method}_{target_day[-1]}'\n if len(demographic_vars) > 0:\n output_key += '_demographics'\n if method == 'AR':\n print('currently deprecated')\n raise NotImplementedError\n loss, model, best_window = naive_autoreg_baselines.train_and_evaluate_model(train_df,test_df)\n return naive_autoreg_baselines.make_predictions(test_df,model,best_window)\n \n elif method == 'exponential':\n preds = exponential_modeling.exponential_fit(df[outcome].values, \n mode=mode, \n target_day=target_day)\n \n \n df[output_key] = preds\n #del test_df['predicted_deaths_exponential']\n\n return df\n \n elif method == 'linear':\n preds = exponential_modeling.linear_fit(df[outcome].values, \n mode=mode, \n target_day=target_day)\n \n \n df[output_key] = preds\n #del test_df['predicted_deaths_exponential']\n\n return df \n \n elif method == 'shared_exponential':\n # Fit a poisson GLM with shared parameters across counties. Input to the poisson GLM is demographic_vars and log(previous_days_deaths+1)\n cur_day_predictions = exponential_modeling.fit_and_predict_shared_exponential(df,mode,outcome=outcome,demographic_vars=demographic_vars,target_day=target_day)\n #if len(demographic_vars) > 0:\n # output_key += '_demographics'\n # import IPython\n # IPython.embed()\n df[output_key] = cur_day_predictions\n return df\n \n elif method == 'ensemble':\n print('please use fit_and_predict_ensemble instead')\n \n \n else:\n print('Unknown method')\n raise ValueError","def model_predict(df, model, encoders):\n\n data_enc = process_data(df, encoders, process_target=False)\n\n headers = ['probability']\n predictions = pd.DataFrame(model.predict(data_enc), columns=headers)\n\n return predictions","def make_predictions(data_dict):\n new_data = pd.DataFrame(columns=[\"location\", \"date\",\n \"logistic_prediction\",\n \"logistic_logarithmic_prediction\",\n \"logistic_polynomial_prediction\"])\n for country in data_dict.keys():\n data = data_dict[country][\"data\"]\n x_data = np.array(list(range(500)))\n model1 = LogisticRegressionModel(data_dict[country][\"data\"])\n y_pred1 = model1.predict(x_data.reshape(-1, 1))\n model2 = LogisticLogarithmicRegressionModel(data)\n y_pred2 = model2.predict(x_data.reshape(-1, 1))\n model3 = LogisticPolynomialRegressionModel(data)\n y_pred3 = model3.predict(x_data.reshape(-1, 1))\n model4 = PolynomialRegressionModel(data)\n y_pred4 = model4.predict(x_data.reshape(-1, 1))\n data = np.swapaxes(np.array([x_data, y_pred1, y_pred2,\n y_pred3, y_pred4]), 0, 1)\n tmp_data = pd.DataFrame(data, columns=[\"date\",\n \"logistic_prediction\",\n \"logistic_logarithmic_prediction\",\n \"logistic_polynomial_prediction\",\n \"polynomial_prediction\"])\n tmp_data[\"location\"] = country\n new_data = new_data.append(tmp_data)\n return new_data","def prophet_train(data): \n \n model = Prophet(interval_width=.95, changepoint_prior_scale=6, yearly_seasonality=True, \n seasonality_prior_scale=1, weekly_seasonality=False, daily_seasonality=False)\n model.add_seasonality(name='monthly', period=120, fourier_order=4)\n \n model.fit(data)\n \n future_dates = model.make_future_dataframe(periods=12, freq='MS')\n forecast = model.predict(future_dates)\n forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n results = forecast[forecast['ds']>='2018-01-01']\n return(results)","def predict(model, up_to_days=DEFAULT_UP_TO_DAYS, start_time=None, end_time=None, plot=False):\n if start_time is None and end_time is None:\n df_future = model.make_future_dataframe(periods=24 * up_to_days, freq='H')\n start_future = df_future.iloc[-1, :]['ds'] - datetime.timedelta(days=up_to_days)\n df_future = df_future[df_future['ds'] >= start_future]\n elif start_time is not None and end_time is not None:\n indices = pd.date_range(start_time, end_time, freq='H')\n assert len(indices) > 0, \"Indices should not be empty\"\n df_future = pd.DataFrame(columns=['ds', 'y'])\n df_future['ds'] = indices\n else:\n raise ValueError(\"Either up_to_days or start_time and end_time must be set appropriately.\")\n df_forecast = model.predict(df_future)\n if plot:\n model.plot(df_forecast)\n model.plot_components(df_forecast)\n return df_forecast","def predict(\n self, horizon: int, predict_fn: Callable = predictions_flow, **predict_fn_kwargs\n ) -> dd.DataFrame:\n model_future = self.client.scatter(self.model.model_, broadcast=True)\n predictions_futures = self.client.map(\n predict_fn,\n self.ts,\n model=model_future,\n horizon=horizon,\n **predict_fn_kwargs,\n )\n meta = self.client.submit(lambda x: x.head(), predictions_futures[0]).result()\n return dd.from_delayed(\n predictions_futures, meta=meta, divisions=self.data_divisions\n )","def forecast_temperature(y, results, end_date, **kwargs):\n \n # Forecast\n forecast = results.get_prediction(start=(y.index.max() + timedelta(1)),\n end=end_date,\n **kwargs)\n y_forecast = forecast.predicted_mean\n forecast_ci = forecast.conf_int()\n\n # Graph forecast\n ax = y.plot(label='observed')\n y_forecast.plot(ax=ax, label='forecasted', alpha=.7, figsize=(15, 8))\n ax.fill_between(forecast_ci.index,\n forecast_ci.iloc[:, 0],\n forecast_ci.iloc[:, 1], color='k', alpha=.2)\n plt.title('Forecasted Average Monthly Temperature')\n ax.set_xlabel('Date')\n ax.set_ylabel('Temperature')\n plt.legend()\n plt.show()\n \n # Generate forecast table\n forecast_table = forecast.summary_frame()\n forecast_table.index.rename('Date',inplace=True)\n forecast_table = round(forecast_table, 2)\n \n return forecast_table","def predict(self, n: int) -> TimeSeries:\n\n if (not self._fit_called):\n raise_log(Exception('fit() must be called before predict()'), logger)","def _predict(self, X: TimeSeriesInstances, y=None) -> np.ndarray:\n raise NotImplementedError","def predict(self):\n for column in self.data_to_predict.columns:\n if column not in list(self.selected_features_):\n self.data_to_predict.drop(column, axis=1, inplace=True)\n for column in list(self.selected_features_):\n if column not in self.data_to_predict.columns:\n self.data_to_predict.loc[:, column] = 0\n self.predictions = self.model.predict(\n self.data_to_predict[self.selected_features_])","def predict(self, data, current_timestamp, number_of_iterations=1):\n\n if self._topology is None:\n logger.warning('Not ready for prediction - safer to run train first')\n logger.info('Cromulon Oracle prediction on {}.'.format(current_timestamp))\n\n predict_y_list = []\n for i in range(number_of_iterations):\n predict_y, symbols, target_timestamps = self.predict_classification(data, current_timestamp)\n predict_y_list.append(predict_y)\n\n predict_y_stack = np.stack(predict_y_list)\n average_predict_y = np.mean(predict_y_stack, axis=0)\n\n means, conf_low, conf_high = self._data_transformation.inverse_transform_multi_predict_y(average_predict_y, symbols)\n self.log_validity_of_predictions(means, conf_low, conf_high)\n\n means_pd = pd.DataFrame(data=means, columns=symbols, index=target_timestamps)\n conf_low_pd = pd.DataFrame(data=conf_low, columns=symbols, index=target_timestamps)\n conf_high_pd = pd.DataFrame(data=conf_high, columns=symbols, index=target_timestamps)\n\n means_pd, conf_low_pd, conf_high_pd = self.filter_predictions(means_pd, conf_low_pd, conf_high_pd)\n\n return OraclePrediction(means_pd, conf_low_pd, conf_high_pd, current_timestamp)","def predict_AR(test, history, window, coeff):\n predictions = list()\n for t in range(len(test)):\n length = len(history)\n lag = [history[i] for i in range(length - window, length)] # keep the last window values\n yhat = coeff[0]\n for d in range(window): # make the prediction for the autoregressive model\n yhat += coeff[d + 1] * lag[window - d - 1]\n obs = test[t] # store the true value of the test set to use for future predictions\n predictions.append(yhat)\n history.append(obs) # and update the history\n return pd.Series(predictions)","def predict(self, model_collection, date, test_mode=False, verbose=False):\n if verbose:\n print(date)\n #get predictions, metrics\n model_predictions = model_collection.predict(date, verbose)\n if(test_mode):\n model_collection.get_voter_metrics()\n \n if verbose:\n print(\"Model Predictions: \", model_predictions)\n \n predictions = {}\n for i,model in enumerate(model_collection.models):\n if(test_mode):\n predictions[model.title] = (model_predictions[i], model.metrics['acc'], model.metrics['balance'], model.metrics['False Positive Rate'])\n else:\n predictions[model.title] = (model_predictions[i], model.metrics['acc'])\n\n \n if verbose:\n print(\"Updated Predictions in Voter\", predictions)\n \n if self.voting_type == \"accuracy\":\n\n sum_voting = 0\n for pred in predictions.items():\n pred = pred[1]\n sum_voting += pred[0]*pred[1]\n sum_voting += pred[0]*pred[1]\n sum_voting = sum_voting/len(predictions)\n if sum_voting > 0.4:\n prediction = 1\n else:\n prediction = 0\n\n elif self.voting_type == 'Penrose':\n total_votes =0\n votes_toPass= 0\n votes_toReject =0\n for title, metrics in predictions.items():\n pred = metrics[0]\n acc = metrics[1] \n balance = metrics[2] \n FPR = metrics[3] \n\n if pred ==0 :\n vote_to = 'reject'\n else:\n vote_to = 'Pass'\n\n multiplier = (acc)* math.sqrt(balance*FPR)\n votes = int(multiplier)\n\n if vote_to == 'reject':\n votes_toReject += votes\n elif vote_to == 'Pass':\n votes_toPass += votes\n total_votes += votes\n \n ratio = votes_toPass/total_votes\n if ratio > .5:\n prediction = 1\n else:\n prediction = 0\n\n else:\n prediction = 'you have not selected a valid voting method'\n \n if verbose:\n print(\"Voting Output \", prediction, \" for model with accuracy \", model_collection.accuracy)\n \n return (prediction, model_collection.accuracy)","def Empirical_Bayes_Estimator(df_train, df_test):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # define the prior weights (simple average, with intercept equal zero)\n beta_0 = np.append(np.array(0), np.full(K, fill_value=1/K, dtype=float))\n\n # design matrix (intercept + individual forecasts)\n F = df_train.iloc[:, 1:].values\n F = np.insert(F, 0, 1, axis=1)\n\n # define y (observed values)\n y = df_train.iloc[:, 0].values\n\n # OLS weights\n beta_hat = np.dot(np.linalg.inv(np.dot(np.transpose(F), F)),\n np.dot(np.transpose(F), y))\n\n # sigma\n sigma_hat_sq = np.dot(\n np.transpose(y - np.dot(F, beta_hat)),\n y - np.dot(F, beta_hat)\n ) / T\n\n # tau\n num = np.dot(np.transpose(beta_hat - beta_0), beta_hat - beta_0)\n denum = np.trace(np.linalg.inv(np.dot(np.transpose(F), F)))\n tau_hat_sq = num / denum - sigma_hat_sq\n\n # combining weights\n shrinkage = 1 - sigma_hat_sq / (sigma_hat_sq + tau_hat_sq)\n beta_1_hat = beta_0 + shrinkage*(beta_hat - beta_0)\n\n # predictions\n df_pred = pd.DataFrame(\n {\"Empirical Bayes Estimator\":\n beta_1_hat[0] + np.dot(df_test, beta_1_hat[1:])},\n index=df_test.index)\n\n return df_pred","def predict(model, dataframe, features, target_name='score', inplace=True):\n if inplace is False:\n dataframe = dataframe.copy()\n dataframe[target_name] = model.predict(dataframe[features])\n return dataframe","def forecast(self, model, idx_rows=None, replace=True):\n if idx_rows is None:\n idx_rows = range(self.X.shape[0])\n\n forecastedY = model.predict(self.X[idx_rows, :])\n if forecastedY.ndim == 1:\n forecastedY = forecastedY[:, None]\n\n # ravel forecasts and, if replace=True, input new values to self.forecasts vector\n idx_frc = self.add_forecasts(forecastedY, idx_rows, replace)\n\n return forecastedY, idx_frc","def generate_hourly_forecast(num_pred_hours, consumption, model, scaler, lag):\r\n # allocate prediction frame\r\n preds_scaled = np.zeros(num_pred_hours)\r\n \r\n # initial X is last lag values from the cold start\r\n X = scaler.transform(consumption.values.reshape(-1, 1))[-lag:]\r\n \r\n # forecast\r\n for i in range(num_pred_hours):\r\n # predict scaled value for next time step\r\n yhat = model1.predict(X.reshape(1, 1, lag), batch_size=1)[0][0]\r\n preds_scaled[i] = yhat\r\n \r\n # update X to be latest data plus prediction\r\n X = pd.Series(X.ravel()).shift(-1).fillna(yhat).values\r\n\r\n # revert scale back to original range\r\n hourly_preds = scaler.inverse_transform(preds_scaled.reshape(-1, 1)).ravel()\r\n return hourly_preds","def makePredictions(self, data, batchSize=1):\n ds = tf.data.Dataset.from_tensor_slices(data)\n ds = ds.window(self.lookBack, shift=self.forecast, drop_remainder=True)\n ds = ds.flat_map(lambda w: w.batch(self.lookBack))\n ds = ds.batch(batchSize).prefetch(1)\n prediction = self.model.predict(ds)\n return prediction","def prediction_data(median_split,mean_split,std_split,degrees_split,weight_split,export_file):\n DATA_TEST_PATH = '../data/test.csv' # Download train data and supply path here \n print('\\nIMPORTING TESTING DATA :',end=\" \")\n y_test, tX_test, ids_test = load_csv_data(DATA_TEST_PATH)\n print('DONE')\n \n #5.a. Splitting the testing data\n print('SPLITTING TESTING DATA :',end=\" \")\n y_test_split,tx_test_split,id_test_split = split_dataset(y_test,tX_test,ids_test) \n print('DONE') \n #5.b. prediction on each model\n y_pred = list()\n \n for split,(y_test_s,tx_test_s,id_test_s) in enumerate(zip(y_test_split,tx_test_split,id_test_split)): \n print('PREDICTION FOR TESTING DATA SPLIT NUMBER',split)\n \n #Formatting to the correct datatype\n y_test_s = np.squeeze(y_test_s)\n tx_test_s = np.squeeze(tx_test_s)\n id_test_s = np.squeeze(id_test_s)\n print('Size of the vectors',y_test_s.shape,tx_test_s.shape) \n #Formatting the data themselves\n print('Counting NaN',end='. ')\n tx_test_s = count_NaN(tx_test_s)\n print('Sanitizing',end = ' . ')\n tx_test_s,median_vec = sanitize_NaN(tx_test_s,median_split[split])\n print('Standardizing',end = ' .')\n tx_test_s,mean_te,std_te = standardize(tx_test_s,mean_split[split],std_split[split])\n print('Building polynomial basis') \n tx_test_s = build_poly(tx_test_s, degrees_split[split])\n \n #Prediction\n y_pred.append(predict_labels(np.array(weight_split[split]), np.array(tx_test_s))) \n \n print('MERGING TESTING DATA',end=\"\")\n y_pred_merged, ids_merged = merge_dataset(y_pred,id_test_split)\n print('DONE')\n \n OUTPUT_PATH = 'results/output_sanitized_normalization_'+export_file+'.csv' \n print('EXPORTING TESTING DATA WITH PREDICTIONS :',end=\" \")\n \n create_csv_submission(ids_merged, y_pred_merged, OUTPUT_PATH)\n print('DONE')","def predict(\n prophet_model: Prophet,\n df: Optional[pd.DataFrame] = None,\n vectorized: bool = False,\n) -> pd.DataFrame:\n if prophet_model.history is None:\n raise Exception(\"Model has not been fit.\")\n\n if df is None:\n df = prophet_model.history.copy()\n else:\n if df.shape[0] == 0:\n raise ValueError(\"Dataframe has no rows.\")\n df = prophet_model.setup_dataframe(df)\n\n df[\"trend\"] = prophet_model.predict_trend(df)\n # TODO: the seasoanl part will be computed twice when we need to compute uncertainty. Remove this part if possible.\n seasonal_components = prophet_model.predict_seasonal_components(df)\n\n if prophet_model.uncertainty_samples:\n intervals = predict_uncertainty(prophet_model, df, vectorized)\n\n else:\n intervals = None\n\n # Drop columns except ds, cap, floor, and trend\n cols = [\"ds\", \"trend\"]\n if \"cap\" in df:\n cols.append(\"cap\")\n if prophet_model.logistic_floor:\n cols.append(\"floor\")\n # Add in forecast components\n df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)\n df2[\"yhat\"] = (\n df2[\"trend\"] * (1 + df2[\"multiplicative_terms\"]) + df2[\"additive_terms\"]\n )\n return df2","def date_prediction(config):\n if config['functionality'] == 'best_flights':\n departure_flight_date = date(config['departure_flight']['departure_date'][0],\n config['departure_flight']['departure_date'][1],\n config['departure_flight']['departure_date'][2])\n return_flight_date = date(config['return_flight']['departure_date'][0],\n config['return_flight']['departure_date'][1],\n config['return_flight']['departure_date'][2])\n div = config['prediction_period_days'] / 7\n dates_search = []\n for x in range(0, div + 1):\n dates_search.append(\n [(departure_flight_date + datetime.timedelta(days=x * 7)),\n (return_flight_date + datetime.timedelta(days=x * 7))])\n for i in dates_search:\n i[0] = str(i[0])\n year, month, day = i[0].split(\"-\")\n i[0] = \"%s/%s/%s\" % (day, month, year)\n i[1] = str(i[1])\n year, month, day = i[1].split(\"-\")\n i[1] = \"%s/%s/%s\" % (day, month, year)\n return dates_search\n elif config['functionality'] == 'flight_trends':\n departure_flight_date = date(\n config['departure_flight']['departure_date'][0],\n config['departure_flight']['departure_date'][1],\n config['departure_flight']['departure_date'][2])\n return_flight_date = date(config['return_flight']['departure_date'][0],\n config['return_flight']['departure_date'][1],\n config['return_flight']['departure_date'][2])\n dates_search = []\n for x in range(0, config['prediction_period_days']):\n dates_search.append(\n [(departure_flight_date + datetime.timedelta(days=x)),\n (return_flight_date + datetime.timedelta(days=x))])\n for i in dates_search:\n i[0] = str(i[0])\n year, month, day = i[0].split(\"-\")\n i[0] = \"%s/%s/%s\" % (day, month, year)\n i[1] = str(i[1])\n year, month, day = i[1].split(\"-\")\n i[1] = \"%s/%s/%s\" % (day, month, year)\n return dates_search","def sts_predict_canteen_values(full_df, prediction_df, future=True):\n\n df = full_df.copy()\n df = df.filter([\"Canteen\"])\n test_period = prediction_df.shape[0]\n\n # The days between last row in dataset and today needs to be calculated\n if future is True:\n # Finding days between end date of dataset and today\n date_today = datetime.now()\n end_date = df.index[-1]\n future_test_period = test_period + (date_today - end_date).days - 1\n\n # The whole dataframe is now the train set\n train = df\n # Creating a new dataframe containing all dates between end date and today + future days from prediction_df\n date_df = pd.DataFrame()\n date_df[\"date\"] = pd.date_range(\n (end_date + timedelta(1)).date(),\n periods=future_test_period,\n freq=\"D\",\n )\n date_df.index = pd.to_datetime(date_df.pop(\"date\"))\n # Setting the new df as test set\n test = date_df\n else:\n # If future = False, the test period is the last part of the full_df, train is everything up until this point\n train = df.iloc[:-test_period]\n test = df.iloc[-test_period:]\n\n # Make prediction\n _, predictions = prediction(train, test)\n return predictions.iloc[-test_period:]"],"string":"[\n \"def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\\\\n columns=['month_year', 'predicted_evictions'])\\n\\n now = datetime.datetime.now()\\n\\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\\n model.fit()\\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\\n\\n predicted = y_hat.Eviction_Notice.values.tolist()\\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\\n temp_df['month_year']=months_list\\n\\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\\n predictions_df = predictions_df[1:]\\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\\n\\n return predictions_df, y_hat\",\n \"def arimax_by_month_train_test(model_data,future_data,months_ahead):\\n transformed_df = arima_by_zip_data_transform(model_data)\\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\\n\\n y_train = transformed_df\\n\\n\\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]\\n\\n now = datetime.datetime.now()\\n this_month = pd.to_datetime(str(now.year)+ '-'+ str(now.month) + '-'+str(1))\\n y_test=y_test[(y_test.Month_Year>this_month)&(y_test.Month_Year<(this_month+pd.offsets.MonthBegin(months_ahead+1)))].sort_values('Month_Year')\\n months_list = np.asarray(y_test.Month_Year)\\n\\n y_test = y_test.set_index(['Month_Year'],inplace=False)\\n\\n\\n for i,row in y_train.Eviction_Notice.iteritems():\\n if row>(y_train.Eviction_Notice.mean()+ y_train.Eviction_Notice.std()*3):\\n y_train.Eviction_Notice.iloc[i]=y_train.Eviction_Notice.mean()\\n\\n y_train = y_train[['Month_Year','Eviction_Notice','CASANF0URN']].set_index(['Month_Year'],inplace=False)\\n\\n return y_train, y_test,months_list\",\n \"def get_predictions(year, month):\\n \\n start_date = str(year)+\\\"-\\\"+str(month)+\\\"-01\\\"\\n end_date = str(year)+\\\"-\\\"+str(month)+\\\"-\\\"+str(monthrange(year, month)[1])\\n\\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\\\"%Y-%m-%d\\\").tolist()\\n\\n # predictfunction \\n # do predictions\\n pred_arr = []\\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\\n \\n try:\\n predictions = load_predictions(file_name)\\n predictions = predictions.round()\\n except:\\n print(\\\"An exception occurred\\\")\\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\\n \\n \\n for index,row in predictions.iterrows():\\n \\n pred_mail = 0\\n pred_counter = 0\\n pred_tel = 0\\n \\n # check predictions dataframe for 'Datum'\\n if 'Datum' in predictions.columns:\\n date = row['Datum']\\n else:\\n break;\\n\\n # check predictions dataframe for 'Mail'\\n if 'Mail' in predictions.columns:\\n pred_mail = row['Mail']\\n\\n # check predictions dataframe for 'Schalter'\\n if 'Schalter' in predictions.columns:\\n pred_counter = row['Schalter']\\n\\n # check predictions dataframe for 'Tel'\\n if 'Tel' in predictions.columns:\\n pred_tel = row['Tel']\\n \\n \\n pred_dict = {'date': date, \\n 'predictions':{'mail' : pred_mail, \\n 'tel' : pred_tel, \\n 'counter' : pred_counter\\n }\\n }\\n\\n pred_arr.append(pred_dict)\\n\\n print(pred_arr) \\n \\n return pred_arr\",\n \"def top_down_forecast_data_processing(original_df,predictions_by_month):\\n transformed_df=original_df.groupby('Month_Year').sum().reset_index()\\\\\\n [['Month_Year','Eviction_Notice']]\\n\\n percentage_of_month_df = pd.merge(original_df[['Month_Year','Address_Zipcode',\\\\\\n 'Eviction_Notice']],transformed_df,how = 'left',left_on='Month_Year',\\\\\\n right_on='Month_Year',suffixes=('','_for_month'))\\n\\n percentage_of_month_df.dropna(inplace=True)\\n\\n percentage_of_month_df['perc_of_month']=\\\\\\n percentage_of_month_df['Eviction_Notice']/percentage_of_month_df['Eviction_Notice_for_month']\\n\\n group_by_zip_df = percentage_of_month_df.groupby('Address_Zipcode').mean().reset_index()[['Address_Zipcode','perc_of_month']]\\n\\n return group_by_zip_df\",\n \"def predict_sales() -> str:\\r\\n # HTML tables and list that are filled depending on the user input.\\r\\n html_gr_month_table = app.config[\\\"table_monthly_growth\\\"]\\r\\n html_gr_week_table = app.config[\\\"table_weekly_growth\\\"]\\r\\n html_csv_list = \\\"\\\"\\r\\n # Empty filename and path to sales forecast graph; also dep. on user input.\\r\\n graph_filename = \\\"\\\"\\r\\n graph_filepath = \\\"'File has not been created yet!'\\\"\\r\\n # Contains HTML form data inputted by the user submitted using POST.\\r\\n response = request.form\\r\\n num_months_input = response.get(\\\"num_months_input\\\")\\r\\n csv_filename_input = response.get(\\\"csv_filename_input\\\")\\r\\n # True if user inputted num of past months for prediction and uploaded CSV.\\r\\n if num_months_input is not None and csv_filename_input is not None:\\r\\n # Loads data from CSV into dataframe & removes rows with missing values\\r\\n file_name = csv_filename_input\\r\\n dataframe = load_csv_clean_to_dataframe(file_name)\\r\\n try:\\r\\n # Replaces index numbers of df with datetimes to get a time series.\\r\\n dataframe = convert_to_time_series(dataframe)\\r\\n except KeyError as error:\\r\\n error_message = (\\\"The following column name is missing \\\"\\r\\n \\\"in the data: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Calculates Quantity ordered averages for every past week.\\r\\n column_to_avg = \\\"Quantity ordered\\\"\\r\\n average_by = \\\"W\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\\r\\n # Calculates Quantity ordered averages for every past month.\\r\\n average_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_month = average_data(dataframe, column_to_avg,\\r\\n average_by)\\r\\n # Calculates past Quantity ordered average growth rates.\\r\\n growth_rates_per_week = past_avgs_per_week.pct_change()\\r\\n growth_rates_per_month = past_avgs_per_month.pct_change()\\r\\n # Creates HTML table containing past monthly sales growth rates.\\r\\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\\r\\n app.config[\\\"table_monthly_growth\\\"] = html_gr_month_table\\r\\n # Creates HTML table containing past weekly sales growth rates.\\r\\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\\r\\n app.config[\\\"table_weekly_growth\\\"] = html_gr_week_table\\r\\n # Filters dataframe for beer types.\\r\\n dunkel = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Dunkel\\\"]\\r\\n pilsner = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Pilsner\\\"]\\r\\n red_helles = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Red Helles\\\"]\\r\\n # Monthly basis;\\r\\n # calculates dunkel's Quantity ordered sums for each month.\\r\\n column_to_sum = \\\"Quantity ordered\\\"\\r\\n sum_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\\r\\n # Calculates pilsner's Quantity ordered sums for each month.\\r\\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\\r\\n # Calculates red_helles' Quantity ordered sums for each month.\\r\\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\\r\\n all_order_sum_month = {\\\"dunkers\\\": dunk_order_sum_per_month,\\r\\n \\\"pilsner\\\": pils_order_sum_per_month,\\r\\n \\\"red_helles\\\": redh_order_sum_per_month}\\r\\n # Clears monthly sales forecasts from the dictionary.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"] = {}\\r\\n # Hyperparameters (hp) for seasonal ARIMA model.\\r\\n order = app.config[\\\"order\\\"]\\r\\n # Sets num_months_in. as num of past periods to be considered by model.\\r\\n app.config[\\\"seasonal_order\\\"][3] = int(num_months_input)\\r\\n seasonal_order = app.config[\\\"seasonal_order\\\"]\\r\\n # Fits ARIMA model and forecasts monthly sales for each beer type.\\r\\n try:\\r\\n for beer_type in all_order_sum_month:\\r\\n model = define_and_fit_model(all_order_sum_month[beer_type],\\r\\n False, order, seasonal_order)\\r\\n # Forecasts 12 months into the future with the fitted model.\\r\\n forecast = model.get_forecast(steps=12)\\r\\n # Stores monthly sales forecasts in dictionary by beer_type.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"][beer_type] = forecast\\r\\n # Plots actual and predicted num of beers sold and saves plot.\\r\\n plot_size = (15, 10)\\r\\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\\r\\n plot_size, beer_type)\\r\\n except ValueError as error:\\r\\n error_message = (\\\"Wrong hyperparameters are set, the model \\\"\\r\\n + \\\"can not be fitted: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Gets filepath of this module and thus filepath to graph (png file).\\r\\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\\r\\n graph_filename = \\\"monthly_forecast_and_past.png\\\"\\r\\n try:\\r\\n # Opens plotted graph.\\r\\n img = Image.open(graph_filename)\\r\\n img.show()\\r\\n except (FileNotFoundError, PermissionError) as error:\\r\\n app.config[\\\"logger\\\"].error(error)\\r\\n # Finds names of all uploaded CSV files in folder where this module is.\\r\\n all_uploaded_csv = []\\r\\n for csv_file in glob.glob(\\\"*.csv\\\"):\\r\\n all_uploaded_csv.append(csv_file)\\r\\n # Creates HTML drop-down list containing all uploaded CSV filenames.\\r\\n html_csv_list = update_csv_list(all_uploaded_csv)\\r\\n return (\\\"\\\"\\\"<style>\\r\\n h1, h2, h3 {\\r\\n font-family: arial, sans-serif;\\r\\n }\\r\\n table {\\r\\n font-family: arial, sans-serif;\\r\\n border-collapse: collapse;\\r\\n width: 100%;\\r\\n }\\r\\n td, th {\\r\\n border: 1px solid #dddddd;\\r\\n text-align: left;\\r\\n padding: 8px;\\r\\n }\\r\\n tr:nth-child(even) {\\r\\n background-color: #dddddd;\\r\\n }\\r\\n </style>\\r\\n <h2>Predict sales</h2>\\r\\n <form action=\\\"/predict_sales\\\" method=\\\"POST\\\">\\r\\n Please select an uploaded CSV file that should be used for the \\r\\n sales forecast (if the list is empty, no CSV file has been \\r\\n uploaded yet; to upload a file, please go back to the \\r\\n tracking screen).<br>\\r\\n <select name=\\\"csv_filename_input\\\">\\\"\\\"\\\"\\r\\n + html_csv_list\\r\\n + \\\"\\\"\\\"</select><br><br>\\r\\n Please enter how many <b>months</b> of past data should be\\r\\n used for the sales forecast:<br>\\r\\n (Note: If the number of past periods is higher than the number\\r\\n of periods in the past sales data, the nonexistent periods are\\r\\n recorded as 0 sales, which, if too many periods are 0,\\r\\n makes the prediction zero.\\r\\n <br>\\r\\n <input type=\\\"number\\\" name=\\\"num_months_input\\\" min=\\\"2\\\"\\r\\n required=\\\"required\\\">\\r\\n <br><br>\\r\\n <input type=\\\"submit\\\" value=\\\"Predict sales\\\">\\r\\n </form>\\r\\n In case the graph is not displayed, please open the file \\\"\\\"\\\"\\r\\n + graph_filename + \\\" manually using the path: \\\"\\r\\n + graph_filepath\\r\\n + \\\"\\\"\\\"<form action=\\\"/\\\" method=\\\"POST\\\">\\r\\n <input type=\\\"hidden\\\">\\r\\n <br>\\r\\n <input type=\\\"submit\\\" value=\\\"Go back to tracking screen\\\">\\r\\n </form> \\r\\n <h3>Average sales growth rates of the past months</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Monthly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_month_table + \\\"</table>\\\"\\r\\n + \\\"\\\"\\\"<h3>Average sales growth rates of the past weeks</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Weekly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_week_table + \\\"</table>\\\")\",\n \"def _forecast(model, predict_steps):\\n\\n pred_uc = model.get_forecast(steps=predict_steps)\\n\\n # Produce the forecasted tables\\n predicted_mean_df = pred_uc.predicted_mean.reset_index()\\n predicted_mean_df.columns = ['Date', 'Predicted_Mean']\\n return predicted_mean_df\",\n \"def test_model_avg_3_weeks_bt(df_y_train, df_y_predict, df_y_ground_truth_scaled, predict_period, n_samples_week, target):\\n\\n # Use average of last 3 weeks (for same time stamps) as predicted values\\n df_hist = pd.concat([df_y_train, df_y_ground_truth_scaled], 0)\\n df_hist_1week = df_hist.shift(n_samples_week)\\n df_hist_2week = df_hist.shift(2*n_samples_week)\\n df_hist_3week = df_hist.shift(3*n_samples_week)\\n df_hist_all = pd.concat([df_hist_1week, df_hist_2week, df_hist_3week], 1)\\n df_hist_all = df_hist_all[df_hist_all.index.isin(df_y_predict.index)]\\n \\n if target == \\\"count\\\":\\n # Average\\n prediction = df_hist_all.mean(axis = 1)\\n elif target == \\\"level\\\":\\n # Majority class\\n prediction = df_hist_all.mode(axis = 1).iloc[:, 0]\\n \\n return prediction\",\n \"def forecast_ARIMA(train, test, p, d, q, model_selection, prod_num):\\n model = ARIMA(train['STU'].dropna(), order=(p, d, q))\\n model_fit = model.fit(disp=0)\\n if model_selection:\\n forecast, st_errs, conf_int = model_fit.forecast(len(test), alpha=0.05)\\n else:\\n predictions_range = pd.date_range('2019-04-01', '2019-09-29', freq=\\\"W-MON\\\").to_series()\\n\\n df_predictions = pd.DataFrame(predictions_range, columns=[\\\"Week\\\"])\\n\\n forecast, st_errs, conf_int = model_fit.forecast(26, alpha=0.05)\\n df_predictions['Sales_Prediction'] = forecast\\n df_predictions['Week'] = pd.to_datetime(df_predictions[\\\"Week\\\"])\\n df_predictions.set_index(\\\"Week\\\", inplace=True)\\n\\n plt.figure(figsize=(12, 5))\\n plt.plot(train['STU'], label='training')\\n plt.plot(df_predictions['Sales_Prediction'], label='forecast')\\n plt.title('Forecast vs Actuals')\\n plt.legend(loc='upper left', fontsize=8)\\n plt.savefig(\\\"Product\\\" + f\\\"_{prod_num}\\\" + \\\".pdf\\\")\\n if model_selection:\\n MSE = mean_squared_error(test, forecast)\\n return MSE\\n return df_predictions\",\n \"def predict(self, next_days):\\n last_date = self.series.index[-1]\\n for time in range(next_days):\\n row = {}\\n for c in self.countries:\\n history = self.series[c]\\n series = history.astype(float)\\n model = ARIMA(series, order=(2,1,0))\\n model_fit = model.fit()\\n output = model_fit.forecast()\\n row[c] = output[0][0]\\n last_date = last_date + pd.DateOffset(1)\\n self.series.loc[last_date] = row\",\n \"def generate_admission_prediction():\\n query = \\\"SELECT * FROM admissions_data\\\"\\n result = general_db_func.query_database(query)\\n admission_data = pd.DataFrame(result)\\n admission_data.rename(columns={0: \\\"year\\\", 1: \\\"university\\\", 2: \\\"admissions\\\"}, inplace=True)\\n admission_data = admission_data.sort_values(by=[\\\"university\\\", \\\"year\\\"], ascending=True)\\n admission_data = admission_data.groupby(\\\"university\\\")\\n admission_data = [admission_data.get_group(x) for x in admission_data.groups]\\n\\n for uni in admission_data:\\n uni_name = list(uni[\\\"university\\\"])[0]\\n uni = uni.drop(columns=[\\\"university\\\"])\\n uni.set_index(uni.year, inplace=True)\\n\\n historic_points = generate_admission_year_points()\\n historic_admissions = fill_none_admissions(uni[\\\"admissions\\\"].tolist())\\n\\n historic_data = (historic_points, historic_admissions)\\n\\n try:\\n # Generate autoregressive model\\n model = AR(uni[\\\"admissions\\\"])\\n model_fit = model.fit()\\n\\n # predict next 2 years\\n prediction_points = generate_admission_year_points(future=True)\\n predicted_admissions = model_fit.predict(len(uni[\\\"year\\\"]), len(uni[\\\"year\\\"])+1).to_list()\\n predicted_data = (prediction_points, predicted_admissions)\\n insert_predicted_admission(uni_name, historic_data, predicted_data)\\n except ValueError as e:\\n print(e)\",\n \"def feature_engineer_ts(self, month=12):\\n st_data_dt = self.get_st_data_dt()\\n end_data_dt = self.get_end_data_dt()\\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n population = self.get_population()\\n is_raw_partition = self.get_is_raw_partition()\\n# Lag 2 months\\n all_data = []\\n# join past is_raw columns\\n for d in date_list:\\n \\n population_partition = population[population['ft_data_dt'] == d] \\n old_date = d - relativedelta(months=month)\\n y = old_date.year\\n m = old_date.month\\n day = calendar.monthrange(y, m)[1]\\n old_date = date(y, m, day)\\n old_date = max(old_date, st_data_dt)\\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n date_list_join.reverse()\\n for index, date_join in enumerate(date_list_join):\\n if date_join.strftime(\\\"%Y-%m-%d\\\") not in is_raw_partition.keys():\\n continue\\n \\n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\\\"%Y-%m-%d\\\")]\\n \\n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\\n new_col = [c+'_'+str(index+1) for c in rename_col]\\n name_dict = dict(list(zip(rename_col, new_col)))\\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\\n all_data.append(population_partition)\\n ts_df = pd.concat(all_data)\\n threshold_null = len(ts_df.columns) - 4\\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\\n \\n def sum_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_sum_'+str(duration)+'mth'\\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def mean_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_avg_'+str(duration)+'mth'\\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def std_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_std_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def med_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_med_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def min_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_min_'+str(duration)+'mth'\\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def max_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_max_'+str(duration)+'mth'\\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q1_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q1_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q3_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q3_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def last_ts(self, df, feature):\\n ft_name = feature+ '_last'\\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\\n return tmp_df\\n \\n ts_duration = [1, 3, 6, 9, 12]\\n feature_list = self.get_is_raw_col()\\n df = ts_df[['idd', 'ft_data_dt']]\\n# Time Series Features\\n for duration in ts_duration:\\n for col in feature_list:\\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\\n df = pd.concat([df\\\\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\\\\n , med_ts(self, ts_df, col_list, col, duration)\\\\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\\\\n , min_ts(self, ts_df, col_list, col, duration)\\\\\\n , max_ts(self, ts_df, col_list, col, duration)]\\n , axis=1)\\n self.set_all_data(df)\",\n \"def generate_time_series_df(eviction_df):\\n evictions_by_month = get_counts_by_month(eviction_df, \\\"month\\\", \\\"total-eviction-filings\\\")\\n timeseries_df = evictions_by_month\\n return timeseries_df\",\n \"def predict(self):\\n # format data\\n df = self.normalize(self.daily)\\n x = df.index.astype(np.int64).values.reshape(-1, 1)\\n y = self.normalize(df[['Adj Close']]).values\\n\\n # format time\\n one_day_time = 86400000000000\\n x_tomorrow = x[-1] + one_day_time\\n x_incl_tomorrow = np.append(x, [x_tomorrow], axis=0)\\n dates = pd.to_datetime(x_incl_tomorrow.reshape(-1))\\n\\n # average the predictions\\n lin_reg = self.linear_regression(x, y, x_tomorrow, x_incl_tomorrow, dates)\\n knn = self.knn(x, y, x_tomorrow, x_incl_tomorrow, dates)\\n tomorrow_norm = [(lin_reg + knn) / 2]\\n today_norm = [df['Adj Close'][-1]]\\n tomorrow = round((tomorrow_norm[0] * self.daily['Adj Close'][0]), 2)\\n today = self.daily['Adj Close'][-1]\\n percent_gain = round((((tomorrow / today) - 1) * 100), 2)\\n percent_gain_int = abs(int(round(percent_gain, 0)))\\n\\n if percent_gain > 0:\\n self.debug += '\\\\nExpected price gain: {} %, buys + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\\n self.buys += percent_gain_int\\n else:\\n self.debug += '\\\\nExpected price gain: {} %, sells + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\\n self.sells += percent_gain_int\\n\\n # plots dotted line connecting stock today with tomorrow's prediction\\n predicting_line = np.append(today_norm, tomorrow_norm, axis=0)\\n\\n if self.will_plot:\\n self.ax.plot(dates[-2:], predicting_line, color='cyan', dashes=([1, 1, 1, 1]))\\n self.ax.plot(pd.to_datetime(x_tomorrow), tomorrow_norm, marker='o', markersize=3, color=\\\"cyan\\\")\",\n \"def model_predict(country,year,month,day,all_models=None,test=False):\\r\\n\\r\\n ## start timer for runtime\\r\\n time_start = time.time()\\r\\n\\r\\n ## load model if needed\\r\\n if not all_models:\\r\\n all_data,all_models = model_load(training=False)\\r\\n \\r\\n ## input checks\\r\\n if country not in all_models.keys():\\r\\n #log\\r\\n raise Exception(\\\"ERROR (model_predict) - model for country '{}' could not be found\\\".format(country))\\r\\n\\r\\n for d in [year,month,day]:\\r\\n if re.search(\\\"\\\\D\\\",d):\\r\\n #log\\r\\n raise Exception(\\\"ERROR (model_predict) - invalid year, month or day\\\")\\r\\n \\r\\n ## load data\\r\\n model = all_models[country]\\r\\n data = all_data[country]\\r\\n\\r\\n ## check date\\r\\n target_date = \\\"{}-{}-{}\\\".format(year,str(month).zfill(2),str(day).zfill(2))\\r\\n print(target_date)\\r\\n\\r\\n if target_date not in data['dates']:\\r\\n raise Exception(\\\"ERROR (model_predict) - date {} not in range {}-{}\\\".format(target_date,\\r\\n data['dates'][0],\\r\\n data['dates'][-1]))\\r\\n date_indx = np.where(data['dates'] == target_date)[0][0]\\r\\n query = data['X'].iloc[[date_indx]]\\r\\n \\r\\n ## sainty check\\r\\n if data['dates'].shape[0] != data['X'].shape[0]:\\r\\n #log\\r\\n raise Exception(\\\"ERROR (model_predict) - dimensions mismatch\\\")\\r\\n\\r\\n ## make prediction and gather data for log entry\\r\\n y_pred = model.predict(query)\\r\\n y_proba = None\\r\\n if 'predict_proba' in dir(model) and 'probability' in dir(model):\\r\\n if model.probability == True:\\r\\n y_proba = model.predict_proba(query)\\r\\n\\r\\n\\r\\n m, s = divmod(time.time()-time_start, 60)\\r\\n h, m = divmod(m, 60)\\r\\n runtime = \\\"%03d:%02d:%02d\\\"%(h, m, s)\\r\\n\\r\\n ## update predict log\\r\\n update_predict_log('Prediction completed for country {0} and target date {1}. Total runtime {2}'.format(country,target_date,runtime))\\r\\n return({'y_pred':y_pred,'y_proba':y_proba})\",\n \"def predict(self, **kwargs):\\n\\n logging.debug(\\n \\\"Call predict() with parameters. \\\"\\n \\\"Forecast 1 step only, kwargs:{kwargs}\\\".format(kwargs=kwargs)\\n )\\n\\n X_test = self.df[-self.step :][self.feature_names]\\n X_test = self.scaler.transform(X_test)\\n y_predict = self.model.predict(X_test)\\n poly_now = self.y_train_season_obj[-1]\\n first_occ = np.where(self.y_train_season_obj == poly_now)\\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\\n now = self.df[\\\"y\\\"][-self.step :]\\n return (now - poly_now) - y_predict + polynext\",\n \"def run():\\n\\n df = read_input() # the parameters\\n df = add_time_period(df) # a feature\\n df = is_holiday(df) # a feature\\n df = scale_continous(df) # continous feature transformation\\n df = encode_dummy(df) # categorical feature transformation\\n df = order_columns(df) # ordering model inputs\\n model = load_model() # the multiple linear regression model\\n prediction = int(model.predict(df)) # form a prediction\\n return prediction # return the prediction\",\n \"def mlp_forecast(model_data, x_filename):\\n # extract model and tree from model data\\n model = model_data[0]\\n window = model_data[1]\\n # grab test data from file\\n x = fio.read_from_file(x_filename)\\n x = x.to_numpy()\\n # predict values\\n y_hat = model.predict(x)\\n # interpolate predicted values to real size\\n y_hat = mlp_output_mapper(y_hat, window)\\n return y_hat\",\n \"def test_03_predict(self):\\n country = 'all'\\n year = 2018\\n month = 2\\n n_next = 20\\n query_data = {'country': 'all',\\n 'year': 2018,\\n 'month': 2,\\n 'n_next': 20\\n }\\n\\n request_json = {'query':query_data,'mode':'test'}\\n\\n r = requests.post('http://localhost:{}/predict'.format(port),json=request_json)\\n response = literal_eval(r.text)\\n\\n self.assertTrue(len(response['y_pred'])==n_next)\\n self.assertTrue(len(response['y_lower'])==n_next)\\n self.assertTrue(len(response['y_upper'])==n_next)\\n\\n for p in response['y_pred']:\\n self.assertTrue(isinstance(p,numbers.Number))\\n\\n for p in response['y_lower']:\\n self.assertTrue(isinstance(p,numbers.Number))\\n\\n for p in response['y_upper']:\\n self.assertTrue(isinstance(p,numbers.Number))\",\n \"def arima_model(self, run_id):\\n # Retrieve data for modelling\\n measures = self.daily_avg(run_id)\\n\\n # don't try to compute if there aren't any measures\\n if measures is None:\\n return pd.DataFrame()\\n\\n # Take past 7-day average of exogenous predictors to use for\\n # future prediction\\n exog_future_predictors = pd.concat(\\n [measures.iloc[-7:, :].mean(axis=0).to_frame().T]*7,\\n ignore_index=True\\n )\\n\\n try:\\n # Find optimal order for model\\n params = arma_order_select_ic(measures['flow'], ic='aic')\\n try:\\n # Build and fit model\\n mod = ARIMA(measures['flow'],\\n order=(params.aic_min_order[0], 0,\\n params.aic_min_order[1]),\\n exog=measures[['temp', 'precip']]).fit()\\n\\n prediction = pd.DataFrame(\\n [mod.forecast(steps=7,\\n exog=exog_future_predictors[\\n ['temp', 'precip']],\\n alpha=0.05)[0]]).T\\n except Exception:\\n # If model doesn't converge, return \\\"prediction\\\"\\n # of most recent day\\n prediction = pd.concat([measures.iloc[-1, :].to_frame().T]*7,\\n ignore_index=True)['flow']\\n except ValueError:\\n # If order fitting doesn't converge, return \\\"prediction\\\"\\n # of most recent day\\n prediction = pd.concat([measures.iloc[-1, :].to_frame().T]*7,\\n ignore_index=True)['flow']\\n\\n # Add dates and return past 21 days for plotting\\n prediction_dates = [measures.index[-2] +\\n datetime.timedelta(days=x) for x in range(0, 7)]\\n prediction.index = prediction_dates\\n past = measures['flow'][-22:-1]\\n prediction = pd.concat([past[:-1], prediction], axis=0)\\n\\n return prediction\",\n \"def forecast(\\n input_values: np.ndarray, future_dates: List, model: Sequential, scaler\\n) -> pd.DataFrame:\\n if scaler:\\n future_values = scaler.inverse_transform(\\n model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\\n )\\n else:\\n future_values = model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\\n\\n df_future = pd.DataFrame(\\n future_values, index=future_dates, columns=[\\\"Predicted Price\\\"]\\n )\\n return df_future\",\n \"def predict_Keras():\\n #read future games\\n future_mstr = pd.read_csv(FG_filename)\\n #extract features and prepare X for predictions\\n features = future_mstr.columns[6:]\\n X_new = future_mstr[features]\\n #load saved model\\n loaded_model = load_model(K_filename)\\n # make predictions on the future games\\n y_new = loaded_model.predict(X_new)\\n # round predictions\\n y_new = [round(x[0]) for x in y_new]\\n # find prediction probabilities on the future games\\n y_new_proba = loaded_model.predict_proba(X_new)\\n # grab the future games and make a copy so you can add the predictions side by side\\n prediction_mstr_w_preds = future_mstr.copy()\\n # add the predictions and probabilities\\n prediction_mstr_w_preds['Prediction'] = y_new\\n prediction_mstr_w_preds['Proba'] = y_new_proba\\n # keep only the columns you want to see\\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Prediction', 'Proba']\\n prediction_final = prediction_mstr_w_preds[cols]\\n \\n return prediction_final\",\n \"def predictions_for_df(df, inferrer):\\n working_df = df.copy()\\n working_df['predictions'] = inferrer.get_activations(\\n working_df.sequence.values).tolist()\\n return working_df\",\n \"def test_04_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n\\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\\n self.assertEqual(y_pred,logged_y_pred)\",\n \"def _predict(cls, model, is_log_transformed,\\n raw_actual, interpolated_actual,\\n training_end=None, seasonal_feature_scoring=None, pred_date=None, order_of_diff=None,\\n training_tail=None, ext_training_features=None, pred_len=None, freq=None,\\n include_holidays_exog=None):\\n\\n import numpy as np\\n import pandas as pd\\n import scipy.stats as st\\n from numpy.linalg import LinAlgError\\n import math\\n\\n alpha = cls._sig_level\\n alpha_extreme = cls._sig_level_extreme\\n\\n include_holidays_exog = include_holidays_exog if ext_training_features else 0\\n\\n index = pd.date_range(start=training_end, end=pred_date, freq=freq)[1:] # Holidays are always daily.\\n\\n de_obj = DataExploration()\\n pred_exog = de_obj._get_exog_data(pred_date, pred_date, index) if include_holidays_exog else None\\n\\n if pred_exog is not None and set(pred_exog.columns.values) != set(ext_training_features):\\n missing_col_list = list(set(ext_training_features) - set(pred_exog.columns.values))\\n common_cols = list(set(ext_training_features).intersection(set(pred_exog.columns.values)))\\n temp_df = pred_exog[common_cols]\\n missing_feat_df = pd.DataFrame(np.zeros([len(pred_exog), len(missing_col_list)]),\\n columns=missing_col_list, index=pred_exog.index.values)\\n pred_exog = pd.concat([temp_df, missing_feat_df], axis=1)\\n pred_exog = pred_exog[ext_training_features]\\n\\n freq = \\\"1\\\" + freq if not any(char.isdigit() for char in freq) else freq\\n\\n forecast_ndays = int((pred_date - pd.Timestamp(training_end)) / pd.Timedelta(freq))\\n model_freshness = forecast_ndays / float(pred_len)\\n\\n try:\\n if forecast_ndays > pred_len:\\n raise ValueError('Current trained model object expired')\\n\\n float_min = 1e-10\\n\\n # set exogenous (holiday) variables for input data\\n if include_holidays_exog:\\n pred_exog = pred_exog.loc[pd.Timestamp(training_end) + pd.Timedelta(freq): pred_date]\\n else:\\n pred_exog = None\\n\\n if seasonal_feature_scoring:\\n if not include_holidays_exog:\\n pred_exog = seasonal_feature_scoring[:forecast_ndays]\\n else:\\n pred_exog['fourier_feature'] = seasonal_feature_scoring[:forecast_ndays]\\n\\n forecast = list(model.forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog))\\n interpolated_training_data = list(zip(*training_tail))[1]\\n\\n for order in list(reversed(range(order_of_diff))):\\n training_data_diff = np.diff(interpolated_training_data,\\n order) if order > 0 else interpolated_training_data\\n\\n forecast_diff_mean = [training_data_diff[-1]]\\n forecast_diff_ci = []\\n\\n for i in range(forecast_ndays):\\n forecast_diff_mean.append(forecast_diff_mean[-1] + forecast[0][i])\\n forecast_diff_ci.append([forecast_diff_mean[-1] -\\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i]),\\n forecast_diff_mean[-1] +\\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i])])\\n forecast[0] = forecast_diff_mean[1:]\\n forecast[2] = forecast_diff_ci\\n\\n if is_log_transformed:\\n transformed_back_forecast = np.exp(forecast[0][-1] + ((forecast[1][-1] ** 2) / 2.0)) - 1\\n transformed_back_std_err = np.sqrt((np.exp(forecast[1][-1] ** 2) - 1) * (np.exp((2 * forecast[0][-1]) +\\n (forecast[1][\\n -1] ** 2))))\\n transformed_back_CILower = transformed_back_forecast - \\\\\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\\\\n if transformed_back_std_err != 0 else transformed_back_forecast\\n transformed_back_CIUpper = transformed_back_forecast + \\\\\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\\\\n if transformed_back_std_err != 0 else transformed_back_forecast\\n transformed_back_interpolated_actual = float(np.exp(interpolated_actual) - 1)\\n if np.sum(np.isnan(forecast[0][-1])) or np.isnan(forecast[1][-1]):\\n raise ValueError('Predicted null value')\\n\\n if is_log_transformed:\\n zscore = (transformed_back_interpolated_actual -\\n transformed_back_forecast) / max(float(transformed_back_std_err), float_min)\\n\\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\\n if math.isnan(anomaly_probability) or math.isnan(transformed_back_CILower) \\\\\\n or math.isnan(transformed_back_CIUpper):\\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN under log transform')\\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\\n\\n result = {'Success': True,\\n 'IsLogTransformed': is_log_transformed,\\n 'LogTransformedAdjustedActual': interpolated_actual,\\n 'LogTransformedPrediction': float(forecast[0][-1]),\\n 'LogTransformedStdErr': float(forecast[1][-1]),\\n 'LogTransformedCILower': float(forecast[2][-1][0]),\\n 'LogTransformedCIUpper': float(forecast[2][-1][1]),\\n 'AdjustedActual': transformed_back_interpolated_actual,\\n 'Prediction': float(transformed_back_forecast) if not float(\\n transformed_back_forecast) == float('inf') else 0.0,\\n 'StdErr': float(transformed_back_std_err) if not float(\\n transformed_back_std_err) == float('inf') else 0.0,\\n 'CILower': float(transformed_back_CILower) if not float(\\n transformed_back_CILower) == float('-inf') else 0.0,\\n 'CIUpper': float(transformed_back_CIUpper) if not float(\\n transformed_back_CIUpper) == float('inf') else 0.0,\\n 'ConfLevel': float(1.0 - alpha) * 100,\\n 'ExogenousHolidays': include_holidays_exog,\\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\\n 'ModelFreshness': model_freshness}\\n\\n else:\\n zscore = (interpolated_actual - forecast[0][-1]) / max(float(forecast[1][-1]), float_min)\\n\\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\\n if math.isnan(anomaly_probability) or math.isnan(forecast[2][-1][0]) or math.isnan(forecast[2][-1][1]):\\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN')\\n\\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\\n\\n result = {'Success': True,\\n 'IsLogTransformed': is_log_transformed,\\n 'AdjustedActual': interpolated_actual,\\n 'Prediction': float(forecast[0][-1]) if not float(\\n forecast[0][-1]) == float('inf') else 0.0,\\n 'StdErr': float(forecast[1][-1]) if not float(\\n forecast[1][-1]) == float('inf') else 0.0,\\n 'CILower': float(forecast[2][-1][0]) if not float(\\n forecast[2][-1][0]) == float('-inf') else 0.0,\\n 'CIUpper': float(forecast[2][-1][1]) if not float(\\n forecast[2][-1][1]) == float('inf') else 0.0,\\n 'ConfLevel': float(1.0 - alpha) * 100,\\n 'ExogenousHolidays': include_holidays_exog,\\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\\n 'ModelFreshness': model_freshness}\\n\\n except (LinAlgError, ValueError, LADStructuralError) as e:\\n result = {'Success': False,\\n 'AdjustedActual': interpolated_actual,\\n 'ErrorMessage': str(e)}\\n\\n return result\",\n \"def predict():\\r\\n global model_pred\\r\\n if request.method == 'POST':\\r\\n date = int(request.form['x'])\\r\\n pred_args =[date]\\r\\n pred_args_arr=np.array(pred_args)\\r\\n pred_features=pred_args_arr.reshape(1,-1)\\r\\n model_reg=open(\\\"classical_decomposition_regression_model\\\", \\\"rb\\\")\\r\\n ml_model=joblib.load(model_reg)\\r\\n model_pred=ml_model.predict(pred_features)\\r\\n model_pred=round(float(model_pred), 2)\\r\\n\\r\\n return render_template('predict.html', predictions=model_pred)\",\n \"def make_predictions(df):\\n t_labels = get_labels(\\\"labels_pca\\\")\\n # clean data\\n df = clean_data(df)\\n # engineer data\\n df = engineer_features(df)\\n # predict\\n with open(\\\"model.pkl\\\",\\\"r\\\") as mdl:\\n model = pickle.load(mdl)\\n mdl.close()\\n predictions = model.predict(df[t_labels])\\n return predictions\",\n \"def get_prediction(amazon_id, df):\\n\\n # convert values to pd DataFrame\\n df, predict_window = process_data(df)\\n\\n #check if model for the product exists and use it, otherwise create new one\\n if(not os.path.exists(get_model_path(amazon_id))):\\n\\n #process data\\n dataset_train, dataset_test = split_dataset(df, predict_window)\\n\\n #transform data\\n dataset_train, dataset_test = transform_data(dataset_train, dataset_test)\\n\\n #scale data\\n x_train, y_train, x_test, y_test = reshape_datasets(dataset_train, dataset_test, predict_window)\\n\\n #create model\\n model = get_model(x_train)\\n\\n #fit data \\n model.fit(x_train, y_train, epochs=500, batch_size=512)\\n\\n #save model \\n model.save(get_model_path(amazon_id))\\n\\n #clear the session\\n Clear.clear_session()\\n \\n if os.path.exists(get_model_path(amazon_id)):\\n\\n #upload model\\n model = load_model(get_model_path(amazon_id)) \\n\\n #run scaler\\n scaler.fit(df)\\n\\n #get inputs for prediction\\n inputs = df[len(df) - predict_window:]\\n\\n #transform inputs\\n inputs = scaler.transform(inputs)\\n\\n # Slide the window forward by one, so the last predicted value now becomes the head of \\n # the new window and predict the next, slide again, and so on\\n for i in range(predict_window):\\n x_predict = []\\n\\n x_predict.append(inputs[i:i + predict_window,0])\\n x_predict = np.array(x_predict)\\n x_predict = np.reshape(x_predict, (x_predict.shape[0], x_predict.shape[1],1))\\n nextPrice = model.predict(x_predict)\\n \\n inputs = np.append(inputs, nextPrice, axis=0)\\n\\n #inverse transformation we did\\n predictions = scaler.inverse_transform(inputs[predict_window:])\\n\\n #convert numpy array to python list\\n python_list = get_python_list(predictions)\\n\\n #clear the session after creating or loading model\\n Clear.clear_session()\\n\\n return python_list\",\n \"def ml_pred_post(ticker=None, stock_last_day=None, train_pred_df=None, val_pred_df=None, test_pred_df=None, \\nX_features=None, y_features=None, n_past_days=90, n_future_days=5, loss='mean_squared_error', lr=0.01, epochs=12, batch_size=32, RMSE_train_pred=None, RMSE_val_pred=None):\\n\\n try:\\n train_pred_df_dict = train_pred_df.to_dict(orient='records')\\n val_pred_df_dict = val_pred_df.to_dict(orient='records')\\n test_pred_df_dict = test_pred_df.to_dict(orient='records')\\n \\n n_X_features = len(X_features)\\n n_y_features = len(y_features)\\n\\n pred_post_to_upload = {\\n 'Stock': ticker,\\n 'Datetime': stock_last_day,\\n 'X_features': X_features,\\n 'y_features': y_features,\\n 'n_past_days': n_past_days,\\n 'n_future_days': n_future_days,\\n 'ML Model': {\\n 'model': 'LSTM',\\n 'parameters': {\\n 'layers': {'LSTM_1 units': 64,\\n 'LSTM_1 input_shape': (n_past_days, n_X_features),\\n 'LSTM_2 units': 32,\\n 'Dropout': 0.2,\\n 'Dense units': n_future_days*n_y_features\\n },\\n 'compile': {\\n 'optimizer': 'Adam',\\n 'loss': str(loss),\\n 'lr': lr\\n },\\n 'fit': {\\n 'epochs': epochs,\\n 'batch_size': batch_size\\n } \\n },\\n\\n },\\n 'RMSE_train_pred': float(RMSE_train_pred),\\n 'RMSE_val_pred': float(RMSE_val_pred),\\n 'train_pred': train_pred_df_dict,\\n 'val_pred': val_pred_df_dict,\\n 'test_pred': test_pred_df_dict\\n }\\n return pred_post_to_upload\\n\\n except Exception as e:\\n print('Something went wrong...')\\n print(f'str(e) = {str(e)}')\\n # print(f'repr(e) = {repr(e)}')\\n\\n # return pred_post_to_upload\",\n \"def __predict(self, predict_start_date, predict_end_date)->dict:\\n X, y = self.__get_data(self.universe, self.factor_pool, self.ret_label, predict_start_date, predict_end_date)\\n res_dict = self.model.predict(X)\\n return res_dict\",\n \"def predict(self, forecastyear, scenario=None):\\n\\n start_time = time()\\n\\n results = []\\n for m in self.model:\\n\\n # \\\"TARGET\\\" -> ISO-3 country code of destination country\\n R = {}\\n R['target'] = m['target']\\n\\n # Forecast year in the input query\\n R['year'] = forecastyear\\n\\n # Scenario in the input query\\n R['scenario'] = scenario if scenario else {}\\n\\n Xv = self.__adjust_features(m['feature'], scenario)\\n # logger.info(\\\"Return adjust features in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n R['forecast'] = m['clf'].predict(Xv)[0]\\n # logger.info(\\\"Return forecast predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n # Explanation of point forecast using SHAP values\\n # R['explanation'] = self.__get_explanations(m['explainer'], Xv, m['Xt'].columns)\\n\\n # Confidence intervals are now one-sided\\n # R['CI'] = [0.0, m['CI'].predict(Xv)[0]]\\n # logger.info(\\\"Return CI predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n results.append(R)\\n\\n logger.debug(\\\"Return predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n return results\",\n \"def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\\n df.dropna(inplace=True)\\n ## day and month\\n df['Date'] = pd.to_datetime(df['Date'])\\n df['Month'] = df['Date'].dt.month\\n df['dayowk'] = df['Date'].dt.dayofweek\\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\\n \\n ##Previos n-day pct_changes\\n df['1day_pct'] = df['Adj Close'].pct_change()\\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\\n \\n ##Cumulative sum of 1day_pct\\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\\n \\n ##EWMA of 7, 50 and 200 days\\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\\n ## Golden Cross vs Death Cross etc.\\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n \\n ##RSI and MACD\\n df = RSI(df,14)\\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\\n \\n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\\n \\n ##Classification target\\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\\n \\n ## IS the stock Overbought or Oversold based on RSI?\\n df['RSI_overbought'] = df['RSI']>70\\n df['RSI_oversold'] = df['RSI']<30\\n \\n \\n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\\n# df = df.dropna()\\n \\n #df = df.reset_index(drop=True)\\n \\n ## Calculating how large the previos hot and cold streaks were\\n f = 0\\n df['prev_hot_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==1:\\n f += 1\\n if df['target'][i+1] ==0:\\n df['prev_hot_streak'][i+1] = f\\n f = 0\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_hot_streak'][i]==0:\\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\\n \\n \\n df['prev_cold_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==0:\\n f += 1\\n if df['target'][i+1] ==1:\\n df['prev_cold_streak'][i+1] = f\\n f = 0\\n\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_cold_streak'][i]==0:\\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\\n \\n ## Calculating current hot and cold streaks\\n df['current_hot_streak'] = np.zeros(df.shape[0])\\n df['current_cold_streak'] = np.zeros(df.shape[0])\\n fhot=0\\n fcold=0\\n for i in range(df.shape[0]):\\n if df['target'][i]==1:\\n fhot += 1\\n fcold = 0\\n df['current_hot_streak'][i] = fhot\\n elif df['target'][i]==0:\\n fcold += 1\\n fhot = 0\\n df['current_cold_streak'][i] = fcold\\n \\n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\\n \\n ## Combinations of previos streaks\\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\\n \\n ##Calculating days since max\\n current_max = df['Adj Close'][0]\\n df['days_from_max'] = np.zeros(df.shape[0])\\n df['pct_from_max'] = np.zeros(df.shape[0])\\n #print('blah')\\n for i in range(1,df.shape[0]):\\n if df['Adj Close'][i] > current_max:\\n current_max = df['Adj Close'][i]\\n # print(current_max)\\n else:\\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\\n #print(df['days_from_max'][i])\\n \\n \\n \\n df.dropna(inplace=True)\\n df = df.reset_index(drop=True)\\n return df\",\n \"def get_pred_score(file_name, kpi, start_train, end_train, periods=365):\\n # set names for columns in order\\n names = ['date', 'handle_time', 'handle_time_forecast',\\n 'volume', 'volume_forecast']\\n df = pd.read_csv('./data/'+file_name, names=names, header=1, index_col=0)\\n\\n df['date'] = pd.to_datetime(df['date'])\\n df.index = df['date']\\n\\n # create column for aht\\n df['aht'] = df['handle_time']/df['volume']\\n df['aht_forecast'] = df['handle_time_forecast']/df['volume_forecast']\\n\\n # df = df[(df['aht'] > 200) & (df['aht'] < 1500)]\\n\\n df1 = df.copy()\\n\\n # create business unit name\\n bu = file_name[:6]\\n\\n # # remove outliers if it helps the model\\n # df = remove_outliers(df, bu)\\n\\n # remove holidays\\n df = df[~df.index.isin(h.iloc[:,0].tolist())]\\n\\n # remove weekends\\n df = df[~df.index.weekday.isin([5,6])]\\n\\n # # plot data in a notebook\\n # plot data\\n # plot_time_vol(df)\\n\\n # create training\\n df = create_training_data(df, kpi, start_train, end_train)\\n\\n \\n # display(df.head())\\n m,future = create_forecast(df, periods=int(periods))\\n forecast = m.predict(future)\\n\\n # # plot forecast in a notebook\\n # plot_forecast(forecast)\\n\\n # set forecast beginning and end date\\n f = forecast.copy()\\n end_test = df1.index.max()\\n f = f[(forecast['ds'] > end_train) & (f['ds'] <= end_test)]\\n\\n # create validation dataset\\n df2 = df1.copy()\\n df2['ds'] = pd.to_datetime(df2.index.date) \\n\\n # set start date of validation data equal to June 1st, 2020 or any other date\\n df2 = df2[df2['ds'] > end_train]\\n\\n # remove weekends and holidays from data\\n df2 = df2[~df2.index.isin(h.iloc[:,0].tolist())]\\n df2 = df2[~df2.index.weekday.isin([5,6])]\\n os.makedirs('./preds/'+kpi, exist_ok=True)\\n\\n future_forecast=forecast[['ds', 'yhat_lower', 'yhat', 'yhat_upper']][forecast['ds']>datetime.datetime.now()]\\n\\n f[['ds', 'yhat_lower', 'yhat', 'yhat_upper']].to_csv('./preds/'+kpi+'/'+bu+'.csv')\\n forecast.to_csv('./preds/analysis/'+kpi+'_'+bu+'.csv')\\n\\n # Validate test data, it must match for scoring\\n mae = evaluate_model(f,df2, kpi, metric='mae')\\n\\n mae.update({'kpi': kpi, 'start_train':start_train, 'end_train': end_train, 'end_test': end_test}) \\n\\n curr = pd.read_csv('scores/'+kpi+'_score.csv',index_col=0)\\n new = pd.DataFrame(mae, index=[bu])\\n\\n new.to_csv('./scores/current/'+bu+'_'+kpi+'.csv')\\n \\n if bu not in curr.index:\\n curr = pd.concat([curr, new], 0)\\n # create log everytime a score is superceded\\n elif new.loc[bu,'prophet'] < curr.loc[bu,'prophet']:\\n data = pd.concat([curr.loc[[bu],:],new.loc[[bu],:]],0)\\n data.to_csv('./scores/logs/'+bu+'_'+kpi+'_'+datetime.datetime.now()\\n .strftime(\\\"%b %d %Y %H:%M:%S\\\").replace(' ', '_')+'.csv')\\n curr.update(new)\\n curr.to_csv('./scores/'+kpi+'_score.csv')\\n\\n return 'error with metric'\\n print('\\\\ntraining',file_name, 'on', kpi, 'start train:',start_train, \\n 'end train:', end_train, 'end_test:', )\",\n \"def predict_XGB():\\n #read future games\\n future_mstr = pd.read_csv(FG_filename)\\n #extract features and prepare X for predictions\\n features = future_mstr.columns[6:]\\n X_new = future_mstr[features]\\n #load saved model\\n xgb = joblib.load(XG_filename)\\n #make predictions\\n XG_preds = xgb.predict(X_new)\\n future_mstr['Predictions'] = XG_preds\\n #return the relevant prediction information\\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Predictions']\\n prediction_mstr_all = future_mstr[cols]\\n \\n return prediction_mstr_all\",\n \"def predict_progression(final):\\n #Import the dataset for prediction\\n dataset = read_excel('SampleProgressionData.xlsx', sheet_name=0)\\n \\\"\\\"\\\"\\n Split the data into a training and a testing set\\n \\\"\\\"\\\"\\n train_features = dataset.iloc[0:197,:-1]\\n train_targets = dataset.iloc[0:197,-1]\\n i=0\\n df_cluster_data_col = {}\\n Dict_progression = {} \\n Dict_rec_progression = {} \\n while i < 5:\\n # Number of employees in this cluster\\n array_n = final[final.cluster == i][\\\"EmployeeID\\\"].unique()\\n len(array_n)\\n print(\\\"cluster \\\", i )\\n print(\\\"Employees: \\\", array_n)\\n # List of employees\\n final[final.cluster == i]\\n cluster_data_n=final[final.cluster == i]\\n cluster_data_n=cluster_data_n[0:len(array_n)]\\n # cluster_data_n\\n df_cluster_data_n=pd.DataFrame(cluster_data_n, columns = ['EmployeeID', 'MajorityBand','CurrentBand','NumberYears','Progression']) \\n df_cluster_data_n['MajorityBand']=df_cluster_data_n['CurrentBand'].max()\\n df_cluster_data_n.fillna(0, inplace=True)\\n dataset_cluster_n = pd.DataFrame(df_cluster_data_n,columns = ['EmployeeID','MajorityBand', 'CurrentBand','NumberYears','Progression'])\\n test_features = dataset_cluster_n.iloc[:,1:4]\\n test_targets = dataset_cluster_n.iloc[:,-1]\\n \\\"\\\"\\\"\\n Train the model\\n \\\"\\\"\\\"\\n tree = DecisionTreeClassifier(criterion = 'entropy').fit(train_features,train_targets)\\n \\\"\\\"\\\"\\n Predict the classes of new, unseen data\\n \\\"\\\"\\\"\\n prediction = tree.predict(test_features)\\n \\\"\\\"\\\"\\n Check the accuracy\\n \\\"\\\"\\\"\\n print(\\\"The prediction accuracy is: \\\",tree.score(test_features,test_targets)*100,\\\"%\\\")\\n df_cluster_data_n['Progression']=list(prediction)\\n strmax = df_cluster_data_n['CurrentBand'].max()\\n print(\\\"strmax \\\", strmax)\\n print(\\\"prediction \\\", prediction)\\n print('---------------------------------------------------------------------')\\n print(\\\"PREDICTION FOR BAND \\\", strmax)\\n print('---------------------------------------------------------------------')\\n df_cluster_data_n = df_cluster_data_n.loc[df_cluster_data_n[\\\"Progression\\\"]==1]\\n df_cluster_data_empid=pd.DataFrame(df_cluster_data_n, columns = ['EmployeeID'])\\n df_cluster_data_col[i]=df_cluster_data_empid\\n Dict_progression[strmax]=list(df_cluster_data_empid['EmployeeID'].unique())\\n print(\\\"Dict_progression \\\", Dict_progression)\\n i=i+1\\n # Show the next promotion band and not the cluster highest band for recommendation to employees\\n list_promotion = Dict_progression[strmax]\\n for emp_band_promo in list_promotion:\\n array = df_cluster_data_n.loc[df_cluster_data_n['EmployeeID'] == emp_band_promo]['CurrentBand'].unique()\\n promotion_band = array[0]+1 \\n list_emp_band = []\\n if promotion_band in Dict_rec_progression:\\n list_emp_band = Dict_rec_progression[promotion_band]\\n list_emp_band.append(emp_band_promo) \\n list_emp_band.sort()\\n Dict_rec_progression[promotion_band] = list_emp_band\\n print(\\\"Dict_progression\\\", Dict_progression)\\n print(\\\"Dict_rec_progression\\\", Dict_rec_progression) \\n return Dict_rec_progression\",\n \"def alspostprocess(data, prediction, features, user_features, movie_features, n_features=10):\\r\\n \\r\\n\\r\\n data['ALS'] = prediction[data.loc[:, 'userID']-1, data.loc[:, 'movieID']-1]\\r\\n features.append('ALS')\\r\\n \\r\\n total_features = len(movie_features)\\r\\n if n_features>total_features:\\r\\n n_features = total_features\\r\\n \\r\\n for i in range(n_features):\\r\\n data[\\\"UserFeature{}\\\".format(i)] = user_features[data.loc[:, 'userID']-1, i]\\r\\n features.append(\\\"UserFeature{}\\\".format(i))\\r\\n data[\\\"MovieFeature{}\\\".format(i)] = movie_features[i, data.loc[:, 'movieID']-1]\\r\\n features.append(\\\"MovieFeature{}\\\".format(i))\\r\\n return data, features\",\n \"def predict(self, trained_model, prediction_datetime):\\n return trained_model.predict()\",\n \"def depart_arrive_stats_by_month(flights):\\n\\n return ...\",\n \"def predict(self,X_test):\\r\\n self.X_test_data=X_test.reset_index(drop=True)\\r\\n \\r\\n temp=pd.DataFrame()\\r\\n count=0\\r\\n for each_model in self.fitted_model:\\r\\n count=count+1\\r\\n \\r\\n temp_str='model_'+str(count)+'_predictions'\\r\\n temp[temp_str]=each_model.predict(self.X_test_data)\\r\\n \\r\\n temp_str='model_'+str(count)+'_probablities'\\r\\n out = each_model.predict_proba(self.X_test_data)\\r\\n temp[temp_str]=[max(each) for each in out]\\r\\n \\r\\n \\\"\\\"\\\"self.outcomes=temp_df\\r\\n return self.out()\\\"\\\"\\\"\\r\\n \\r\\n \\r\\n \\r\\n \\\"\\\"\\\"columns list form all the predictions and probabalities individually\\\"\\\"\\\"\\r\\n pred_list=[x for x in temp.columns if x.split('_')[-1]=='predictions']\\r\\n prob_list=[x for x in temp.columns if x.split('_')[-1]=='probablities']\\r\\n \\r\\n \\\"\\\"\\\"getting max probablity column name out of alla the probablity\\\"\\\"\\\"\\r\\n \\r\\n max_prob=temp[prob_list].max(axis=1)\\r\\n max_prob_col_name=temp[prob_list].idxmax(axis=1)\\r\\n \\r\\n \\\"\\\"\\\"getting final prediction column name for each instance using max probabaility column name\\\"\\\"\\\"\\r\\n final_pred_col=[]\\r\\n for every in max_prob_col_name:\\r\\n final_pred_col.append('_'.join(every.split('_')[:-1])+'_predictions')\\r\\n \\r\\n \\\"\\\"\\\"final_pred=[]\\r\\n for each in range(len(final_pred_col)):\\r\\n final_pred.append(temp[pred_list].loc[each][final_pred_col[i]])\\\"\\\"\\\"\\r\\n \\r\\n final_pred=[]\\r\\n for each in range(len(final_pred_col)):\\r\\n final_pred.append(temp[pred_list].loc[each][final_pred_col[each]])\\r\\n \\r\\n \\r\\n \\r\\n self.final_preditions=pd.DataFrame(final_pred,columns=['prediction'])\\r\\n self.final_probablaties=pd.DataFrame(max_prob,columns=['probablity'])\\r\\n \\r\\n \\r\\n return (pd.concat([self.X_test_data, self.final_preditions,self.final_probablaties], axis=1, sort=True))\",\n \"def predict(df,x):\\n ml,g,sl2,sm2,sd2= x\\n _,in_dic = rl.build_data_strucutre(df,'length_box_um',1)\\n pred_mat = rl.prediction_total(ml,g,sl2,sm2,in_dic['reind_v'],in_dic['dat_v'],in_dic['s'],in_dic['S'],in_dic['dt'],in_dic['lane_ID_v'],in_dic['val_v'],1,sd2,nproc=10)\\n df = rl.merge_df_pred(df,pred_mat)\\n return df\",\n \"def anomaly_detect(data):\\n reconstruction_dict = {}\\n for location, dataframe in data.items():\\n model_dir_path = os.path.join(THIS_DIR, './models')\\n print(location)\\n samples = len(dataframe.index.codes[0])\\n timesteps = len(dataframe.columns.levels[1])\\n np_data_o1 = dataframe[['occupancy1']].values\\n np_data_o2 = dataframe[['occupancy2']].values\\n np_data_diff = np.abs(np_data_o1 - np_data_o2)\\n scaler = MinMaxScaler()\\n np_data_o1 = scaler.fit_transform(np_data_o1)\\n np_data_o2 = scaler.fit_transform(np_data_o2)\\n np_data_diff = scaler.fit_transform(np_data_diff)\\n np_data = np.stack((np_data_o1, np_data_o2, np_data_diff), axis=-1)\\n print(str(np_data.shape[0]) + ' weeks processing') \\n\\n ae = LstmAutoEncoder()\\n\\n # fit the data and save model into model_dir_path\\n ae.fit(np_data[:, :, :], model_dir_path=model_dir_path, std_dev_threshold=1.5)\\n\\n # load back the model saved in model_dir_path detect anomaly\\n ae.load_model(model_dir_path)\\n anomaly_information = ae.anomaly(np_data[:, :, :])\\n reconstruction_error = {}\\n if ae.threshold == 0.0:\\n continue\\n else:\\n reconstruction_error['threshold'] = ae.threshold\\n years = list(dataframe.index.get_level_values(0))\\n weeks = list(dataframe.index.get_level_values(1))\\n for idx, (is_anomaly, dist) in enumerate(anomaly_information):\\n reconstruction_error[str(years[idx]) + ', ' +str(weeks[idx])] = [is_anomaly, dist]\\n if is_anomaly == True:\\n print(location + ' year ' + str(years[idx]) + ', week ' + str(weeks[idx]) + ' is anomalous')\\n reconstruction_dict[location] = reconstruction_error\\n\\n return reconstruction_dict\",\n \"def store_predictions(df):\\n ts = df[df.columns[1]]\\n base = pd.DataFrame(ts)\\n preds = make_preds(ts, 'Predicted '+ df.columns[1])\\n base.index = df['year']\\n base = base.append(pd.DataFrame(preds), sort = True)\\n for col in df.columns[2:]:\\n ts = df[col]\\n temp = pd.DataFrame(ts)\\n preds = make_preds(ts, 'Predicted ' + col)\\n temp.index = df['year']\\n temp = temp.append(pd.DataFrame(preds), sort = True)\\n base = base.join(temp)\\n return base\",\n \"def recursive(model_dic_item, Std2=True, skip=60, leave=0): \\n\\n key, model_obj = model_dic_item\\n \\n # Use internal variable names\\n Xn = model_obj.Xn\\n monthR = model_obj.target\\n continuous = model_obj.continuous\\n h = model_obj.horizon\\n Std = model_obj.std\\n minH = model_obj.minH\\n minR = model_obj.minR\\n lagBM = model_obj.lagBM\\n\\n if lagBM != 0:\\n # Do not change original\\n Xn = Xn.copy(deep=True)\\n Xn.index = Xn.index + MonthEnd(lagBM)\\n \\n # Merge already copy the dfs => not change input df\\n data = pd.merge(Xn, model_obj.Y, how='left', left_index=True, \\n right_index=True)\\n idxR = Xn.shape[1] # Index of 'RM' column\\n\\n # Ret. in proper frequency, 'fR', (h=1 => no change)\\n if h != 1:\\n data['fR'] = changeFreq(data[monthR], new_frequency=h,\\n continuous=continuous, percent=False) \\n else:\\n data['fR'] = data[monthR]\\n \\n # Returns that will be realized over h periods starting *after* t:\\n # ex: t=Jan (end) => Ret(01/Feb - 31/Jan)\\n data['Future'] = data['fR'].shift(-h)\\n \\n # historical mean prediction (observable at t):\\n ###############################################\\n data['Ehist'] = data['fR'].expanding().mean()\\n # Errors historical:\\n data['eNested'] = data['Ehist'] - data['Future']\\n \\n ############################################################\\n # Forecasts from the model (larger) *at the end of month t*\\n ############################################################\\n data['Er'] = np.nan\\n # make one prediction w/ info until (and including) t\\n for t in range(skip, data.shape[0] - leave):\\n \\n # The last *position* is t!\\n subsample = data.iloc[: t + 1, :].copy()\\n lastDateIS = subsample.index[-1] # last date in subsample\\n \\n subsample['F'] = estim_F(subsample.iloc[:, :idxR], # the first t \\\"BMs\\\"\\n subsample['fR'],\\n continuous, h, Std, Std2, minH, minR,\\n IS=False, lagBM=0)\\n \\n # because conditions minR, etc => F=Nan often\\n if not subsample['F'].isnull().all():\\n results = sm.OLS(subsample['fR'].shift(-h), # .iloc[:-h]\\n sm.add_constant(subsample['F']), # .iloc[:-h]\\n missing='drop').fit()\\n # Save result in main df:\\n data['Er'].loc[lastDateIS] = (\\n results.params[0] \\n + results.params[1] * subsample['F'].loc[lastDateIS])\\n\\n # Prediction error (model)\\n # NOTE: I register the prediction error *at the prediction date*,\\n # not \\\"1y\\\" later, when the return is realized!\\n # KP place the prediction in t+1 always, even for 12m. \\n # This can be confusing to compare the results.\\n data['eLarger'] = data['Er'] - data['Future']\\n \\n # Return a tupple with the forecast DF and the key:\\n return data.filter(['Er', 'Ehist', 'eLarger', 'eNested', 'Future']\\n ).copy(), model_obj.key\",\n \"def predict(data, model, final_columns, category_features, averages):\\n new_data = extend_dummy_columns(data, final_columns, category_features)\\n # np array to use for our model.\\n test_data = np.ones(len(final_columns))\\n for i, col in enumerate(final_columns):\\n if new_data[col] is None:\\n test_data[i] = averages[col]\\n else:\\n test_data[i] = new_data[col]\\n return model.predict_proba(test_data)[0][1]\",\n \"def compare_with_historical_m3_forecast(configs: Configs, data_loader: DataLoader):\\n\\n # Historical forecasts\\n granularity_week = [\\n n.FIELD_YEARWEEK,\\n n.FIELD_LEAD_SKU_ID,\\n n.FIELD_CUSTOMER_GROUP,\\n n.FIELD_PLANT_ID\\n ]\\n granularity_month = [n.FIELD_YEARMONTH] + granularity_week[1:]\\n\\n relevant_columns = [\\n n.FIELD_YEARMONTH,\\n n.FIELD_YEARWEEK,\\n n.FIELD_LEAD_SKU_ID,\\n n.FIELD_CUSTOMER_GROUP,\\n n.FIELD_PLANT_ID,\\n Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3,\\n 'forecast_volume_m-3'\\n ]\\n data_loader.load_sell_in_forecast_accuracy_comparison_data(Evaluator.GROUND_TRUTH_APO_FORECAST_ACCURACY_REPORT_M3)\\n\\n historical_forecasts = data_loader.df_fc_acc.filter(relevant_columns)\\n in_period = ((historical_forecasts[n.FIELD_YEARWEEK] >= configs.evaluation_start) &\\n (historical_forecasts[n.FIELD_YEARWEEK] <= configs.evaluation_end))\\n historical_forecasts = historical_forecasts[in_period]\\n\\n # Group to get one value per SKU, CPG, Plant, Week\\n historical_forecasts = (historical_forecasts\\n .groupby(granularity_week)\\n .agg({n.FIELD_YEARMONTH: 'first',\\n Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3: 'sum',\\n 'forecast_volume_m-3': 'sum'})\\n .reset_index()\\n )\\n\\n # Predictions\\n apo_week_month_list = (historical_forecasts[[n.FIELD_YEARMONTH, n.FIELD_YEARWEEK]]\\n .sort_values([n.FIELD_YEARMONTH, n.FIELD_YEARWEEK])\\n .drop_duplicates(n.FIELD_YEARWEEK))\\n apo_week_month_list = build_m3_target_predictions_table(\\n apo_week_month_list, configs.evaluation_start, configs.evaluation_end)\\n\\n # list contains all (SKU, CPG, Plant) combinations that appear at least once in a given month\\n # TODO - cross join to get all existing (material, plant, cpg) combinations\\n df_predictions_list = historical_forecasts[granularity_month].drop_duplicates()\\n df_predictions_list = df_predictions_list.merge(apo_week_month_list, on=n.FIELD_YEARMONTH, how='left')\\n predictions = predict_multiple_horizons(df_predictions_list, configs, data_loader)\\n\\n # scope historical forecasts table to keep exactly the same weeks as the predicted ones (before aggregating)\\n historical_forecasts = historical_forecasts.merge(\\n predictions.rename(columns={'date_to_predict': n.FIELD_YEARWEEK}).filter(granularity_week).drop_duplicates(),\\n on=granularity_week, how='inner'\\n )\\n\\n # Comparison ML forecast vs historical forecast\\n # predictions_output = pd.read_csv('predictions_m3.csv')\\n aggs = {Evaluator.GROUND_TRUTH_COLUMN_APO_FINAL_FORECAST_M3: 'sum', 'forecast_volume_m-3': 'sum'}\\n historical_forecasts_month = historical_forecasts.groupby(granularity_month).agg(aggs).reset_index()\\n predictions_month = predictions.groupby(granularity_month)[n.FIELD_PREDICTION].sum().reset_index()\\n forecasts_apo_vs_ml_m3 = historical_forecasts_month.merge(\\n predictions_month,\\n on=granularity_month, how='left'\\n )\\n\\n # Add is_on trade information\\n tmp = data_loader.df_cpg_trade_channel[[n.FIELD_CUSTOMER_GROUP, 'trade_type']]\\n tmp['is_on_trade'] = (tmp['trade_type'] == 'is_on_trade')\\n tmp = tmp[[n.FIELD_CUSTOMER_GROUP, 'is_on_trade']]\\n forecasts_apo_vs_ml_m3 = forecasts_apo_vs_ml_m3.merge(tmp, on=n.FIELD_CUSTOMER_GROUP, how='left')\\n\\n forecasts_apo_vs_ml_m3 = forecasts_apo_vs_ml_m3[forecasts_apo_vs_ml_m3[n.FIELD_PLANT_ID] != 'F015']\\n evaluator = Evaluator(\\n data=data_loader,\\n granularity=granularity_month,\\n start_periods_eval=configs.evaluation_start,\\n end_periods_eval=configs.evaluation_end,\\n forecasting_horizon=3,\\n granularity_eval='forecast_volume_m-3',\\n is_sell_in_model=configs.is_sell_in_model,\\n is_weekly_forecast=False\\n )\\n granularities = replace_week_granularity_by_month(configs.granularity_evaluator.values())\\n evaluator.report(forecasts_apo_vs_ml_m3, granularities)\\n evaluator.detailed_report(forecasts_apo_vs_ml_m3, granularities)\",\n \"def predict():\\n data = request.json\\n\\n if data:\\n predict = bool(data[\\\"predict\\\"])\\n\\n if predict:\\n if predictor.pred_dict[\\\"model\\\"] == 0:\\n # ARIMA\\n arima_forecast = predictor.get_prediction_arima()\\n plots.arima_df = arima_forecast\\n elif predictor.pred_dict[\\\"model\\\"] == 1:\\n # Prophet\\n prophet_forecast = predictor.get_prediction_prophet()\\n plots.prophet_df = prophet_forecast\\n elif predictor.pred_dict[\\\"model\\\"] == 2:\\n # LSTM\\n lstm_forecast = predictor.get_prediction_bidirectlstm()\\n plots.lstm_df = lstm_forecast\\n else:\\n pass\\n\\n return 'Non tam praeclarum est scire latine, quam turpe nescire'\",\n \"def predictions(dataframe):\\n # Select Features\\n features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']]\\n\\n # Add UNIT to features using dummy variables\\n dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\\n features = features.join(dummy_units)\\n\\n # Values\\n values = dataframe['ENTRIESn_hourly']\\n m = len(values)\\n\\n features, mu, sigma = normalize_features(features)\\n features['ones'] = np.ones(m) # Add a column of 1s (y intercept)\\n\\n # Convert features and values to numpy arrays\\n features_array = np.array(features)\\n values_array = np.array(values)\\n\\n # Set values for alpha, number of iterations.\\n alpha = 0.1 # please feel free to change this value\\n num_iterations = 75 # please feel free to change this value\\n\\n # Initialize theta, perform gradient descent\\n theta_gradient_descent = np.zeros(len(features.columns))\\n theta_gradient_descent, cost_history = gradient_descent(features_array,\\n values_array,\\n theta_gradient_descent,\\n alpha,\\n num_iterations)\\n\\n plot = None\\n # -------------------------------------------------\\n # Uncomment the next line to see your cost history\\n # -------------------------------------------------\\n # plot = plot_cost_history(alpha, cost_history)\\n #\\n # Please note, there is a possibility that plotting\\n # this in addition to your calculation will exceed\\n # the 30 second limit on the compute servers.\\n\\n predicted = np.dot(features_array, theta_gradient_descent)\\n return predicted, plot\",\n \"def train_promotion_prediction_model(input_data, input_features, output_features, cat_columns, model,\\r\\n learning_rate, max_depth, num_leaves, n_iter, n_estimators,\\r\\n train_size, test_months_exclusion, cat_var_exclusion,\\r\\n remove_outliers, impact_stores_outlier=None, promo_duration_outlier=None,\\r\\n discount_depths_outlier=None, min_samples=3):\\r\\n\\r\\n # convert input data format\\r\\n # for cols in input data that is not in the cat cols list, convert to numeric\\r\\n # for col in list(input_data.columns):\\r\\n # if col not in list(cat_columns):\\r\\n input_data['perc_inc_qty'] = pd.to_numeric(input_data['perc_inc_qty'])\\r\\n input_data['in_leaflet_flag'] = pd.to_numeric(input_data['in_leaflet_flag'])\\r\\n input_data['in_gondola_flag'] = pd.to_numeric(input_data['in_gondola_flag'])\\r\\n input_data['duration_wks'] = pd.to_numeric(input_data['duration_wks'])\\r\\n\\r\\n # subtract 2 in duration_wks\\r\\n input_data[\\\"duration_wks\\\"] = input_data[\\\"duration_wks\\\"].apply(lambda x: x - 2)\\r\\n\\r\\n # Check no. of input sample data rows\\r\\n logger.info(\\\"Input sample data includes {b} samples...\\\".format(b=input_data.shape[0]))\\r\\n\\r\\n # Lets remove data within the test exclusion months list\\r\\n if 'campaign_start_month' in list(input_data.columns) and test_months_exclusion is not None:\\r\\n outliers = input_data[input_data.campaign_start_month.isin(test_months_exclusion)]\\r\\n logger.info(\\r\\n \\\"Removing sample data where campaign start months is in {a}, {b} sample data points removed...\\\".format(\\r\\n a=test_months_exclusion,\\r\\n b=outliers.shape[0]))\\r\\n input_data = input_data[~input_data['campaign_start_month'].isin(test_months_exclusion)]\\r\\n\\r\\n # Lets remove data where store count is below a certain value\\r\\n if 'no_impacted_stores' in list(input_data.columns) and impact_stores_outlier is not None:\\r\\n outliers = input_data[input_data['no_impacted_stores'] < impact_stores_outlier]\\r\\n logger.info(\\\"Removing sample data where impacted stores < {a}, {b} sample data points removed...\\\".format(\\r\\n a=impact_stores_outlier,\\r\\n b=outliers.shape[0]))\\r\\n input_data = input_data[input_data['no_impacted_stores'] >= impact_stores_outlier]\\r\\n\\r\\n # Lets remove data where duration is above a certain value\\r\\n if 'duration_days' in list(input_data.columns) and promo_duration_outlier is not None:\\r\\n outliers = input_data[input_data['duration_days'] > promo_duration_outlier]\\r\\n logger.info(\\\"Removing sample data where promotion duration > {a}, {b} sample data points removed...\\\".format(\\r\\n a=promo_duration_outlier,\\r\\n b=outliers.shape[0]))\\r\\n input_data = input_data[input_data['duration_days'] <= promo_duration_outlier]\\r\\n\\r\\n # Lets remove data where discount depth is not in specified list\\r\\n if 'discount_depth' in list(input_data.columns) and discount_depths_outlier is not None:\\r\\n outliers = input_data[~input_data.discount_depth.isin(discount_depths_outlier)]\\r\\n logger.info(\\\"Removing sample data where discount depth is not in {a}, {b} sample data points removed...\\\".format(\\r\\n a=discount_depths_outlier,\\r\\n b=outliers.shape[0]))\\r\\n input_data = input_data[input_data.discount_depth.isin(discount_depths_outlier)]\\r\\n\\r\\n if remove_outliers:\\r\\n logger.info(\\\"Removing outliers from sample data...\\\")\\r\\n\\r\\n # outlier removal based on negative values and where the baseline is smaller than 100\\r\\n outliers = input_data[(input_data[output_features[0]] <= 0) & (input_data['total_baseline_qty'] < 100)]\\r\\n logger.info(\\r\\n \\\"Removing all negative values from {a} and where the baseline is smaller than 100, \\\"\\r\\n \\\"{b} sample data points removed...\\\".format(a=output_features[0], b=outliers.shape[0]))\\r\\n\\r\\n input_data = input_data[(input_data[output_features[0]] > 0) |\\r\\n (input_data[output_features[0]] <= 0) & (input_data['total_baseline_qty'] > 100)]\\r\\n\\r\\n # zero values which are negative\\r\\n outliers = input_data[input_data[output_features[0]] < 0]\\r\\n logger.info(\\r\\n \\\"Zeroing all negative values from {a}, \\\"\\r\\n \\\"{b} sample data points removed...\\\".format(a=output_features[0], b=outliers.shape[0]))\\r\\n\\r\\n input_data.loc[input_data[output_features[0]] < 0, output_features[0]] = 0\\r\\n\\r\\n # outlier removal based on too high % uplift - set value to 1000%\\r\\n if 'perc_inc_qty' in list(input_data.columns):\\r\\n outliers = input_data[input_data['perc_inc_qty'] >= 10]\\r\\n logger.info(\\r\\n \\\"Removing sample data where % qty uplift is greater than 1000%, {b} sample data points removed...\\\".format(\\r\\n b=outliers.shape[0]))\\r\\n input_data = input_data[input_data['perc_inc_qty'] < 10]\\r\\n\\r\\n # outlier removal based on quantile in target variable\\r\\n q = input_data[output_features[0]].quantile(0.95)\\r\\n\\r\\n outliers = input_data[input_data[output_features[0]] >= q]\\r\\n logger.info(\\\"Based on 95% quantiles, {} sample data points removed...\\\".format(outliers.shape[0]))\\r\\n\\r\\n input_data = input_data[input_data[output_features[0]] < q]\\r\\n\\r\\n # Filter on only the input features\\r\\n total_features = input_features + output_features\\r\\n input_data = input_data[total_features]\\r\\n\\r\\n # Check absent values\\r\\n null_value_stats_x = input_data[input_features].isnull().sum(axis=0)\\r\\n logger.info(\\\"Null values for input features include:\\\\n{}\\\".format(null_value_stats_x[null_value_stats_x != 0]))\\r\\n\\r\\n null_value_stats_y = input_data[output_features].isnull().sum(axis=0)\\r\\n logger.info(\\\"Null values for target variable include:\\\\n{}\\\".format(null_value_stats_y[null_value_stats_y != 0]))\\r\\n\\r\\n # Throw error if any values are null in y\\r\\n if input_data[output_features].isnull().values.any():\\r\\n logger.error(\\\"Null values found in target data...\\\")\\r\\n raise ValueError('Null values found in target data!')\\r\\n\\r\\n # Fill remaining absent values in X with -999\\r\\n input_data.fillna(-999, inplace=True)\\r\\n\\r\\n # Describe the dataset\\r\\n logger.info(\\\"Summary statistics for numeric features in input data are...\\\")\\r\\n logger.info(\\\"{}\\\".format(input_data.describe()))\\r\\n\\r\\n # Check data types\\r\\n X = input_data[input_features]\\r\\n\\r\\n # If the output feature is uplift %, multiply by 100\\r\\n if 'p_cal_perc_inc_sale_qty' in output_features:\\r\\n logger.info(\\\"Scaling target variable {a} by 100...\\\".format(a=output_features[0]))\\r\\n y = input_data[output_features]*100\\r\\n\\r\\n else:\\r\\n y = input_data[output_features]\\r\\n\\r\\n logger.info(\\\"Input dataset data types include:\\\\n{}\\\".format(X.dtypes))\\r\\n logger.info(\\\"Target variable data types include:\\\\n{}\\\".format(y.dtypes))\\r\\n\\r\\n # Lets split the data into training and validation sets\\r\\n X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=train_size, random_state=42)\\r\\n logger.info(\\\"Training dataset includes {} samples...\\\".format(X_train.shape[0]))\\r\\n logger.info(\\\"Test dataset includes {} samples...\\\".format(X_validation.shape[0]))\\r\\n\\r\\n # create a mapping dictionary (to be used for models which require int categorical cols)\\r\\n map_dict = {}\\r\\n\\r\\n if model == 'CatBoost':\\r\\n\\r\\n # Obtain categorical feature index\\r\\n cat_features_index = [X.columns.get_loc(c) for c in cat_columns if c in X]\\r\\n\\r\\n # initialise CatBoost regressor\\r\\n train_model = CatBoostRegressor(iterations=700,\\r\\n learning_rate=learning_rate,\\r\\n depth=max_depth,\\r\\n eval_metric='RMSE',\\r\\n random_seed=42,\\r\\n bagging_temperature=0.2,\\r\\n od_type='Iter',\\r\\n metric_period=75,\\r\\n od_wait=100)\\r\\n\\r\\n # Fit the model - catboost does not require us to specify integers for cat features\\r\\n train_model.fit(X_train, y_train,\\r\\n eval_set=(X_validation, y_validation),\\r\\n cat_features=cat_features_index,\\r\\n use_best_model=True)\\r\\n\\r\\n pred = train_model.predict(X_validation)\\r\\n\\r\\n elif model == 'lightgbm':\\r\\n\\r\\n # For lightgbm, we need to convert our categorical features to int\\r\\n # Loop through categorical cols\\r\\n for col in cat_columns:\\r\\n if col in list(X.columns):\\r\\n # get unique values\\r\\n unique_vals = X[col].unique()\\r\\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\\r\\n\\r\\n # map them for the train and test data sets\\r\\n X_train = X_train.copy()\\r\\n X_train[col] = X_train[col].map(unique_vals_dict)\\r\\n X_validation = X_validation.copy()\\r\\n X_validation[col] = X_validation[col].map(unique_vals_dict)\\r\\n\\r\\n # store the mapping for later use\\r\\n map_dict[col] = unique_vals_dict\\r\\n\\r\\n # LightGBM dataset formatting (with categorical variables)\\r\\n if cat_var_exclusion:\\r\\n lgtrain = lgb.Dataset(X_train, y_train,\\r\\n feature_name=input_features)\\r\\n lgvalid = lgb.Dataset(X_validation, y_validation,\\r\\n feature_name=input_features)\\r\\n else:\\r\\n cat_col = [col for col in cat_columns if col in list(X.columns)]\\r\\n\\r\\n lgtrain = lgb.Dataset(X_train, y_train,\\r\\n feature_name=input_features,\\r\\n categorical_feature=cat_col)\\r\\n lgvalid = lgb.Dataset(X_validation, y_validation,\\r\\n feature_name=input_features,\\r\\n categorical_feature=cat_col)\\r\\n\\r\\n params = {\\r\\n 'objective': 'regression',\\r\\n 'metric': 'rmse',\\r\\n 'num_leaves': num_leaves,\\r\\n 'max_depth': max_depth,\\r\\n 'learning_rate': learning_rate,\\r\\n 'feature_fraction': 0.8,\\r\\n 'bagging_fraction': 0.8,\\r\\n 'bagging_freq': 1,\\r\\n 'boosting_type': 'gbdt',\\r\\n 'verbosity': -1\\r\\n }\\r\\n\\r\\n train_model = lgb.train(\\r\\n params,\\r\\n lgtrain,\\r\\n num_boost_round=n_iter,\\r\\n valid_sets=[lgtrain, lgvalid],\\r\\n valid_names=[\\\"train\\\", \\\"valid\\\"],\\r\\n early_stopping_rounds=1000,\\r\\n verbose_eval=500\\r\\n )\\r\\n\\r\\n pred = train_model.predict(X_validation)\\r\\n\\r\\n elif model == 'xgboost':\\r\\n\\r\\n # For xgboost, we need to convert our categorical features to int\\r\\n # There are 3 approaches - one-hot encode, label encode and binary encode\\r\\n\\r\\n # Here, for simplicity, we are using label encoders\\r\\n # Loop through categorical cols\\r\\n for col in cat_columns:\\r\\n if col in list(X.columns):\\r\\n # get unique values\\r\\n unique_vals = X[col].unique()\\r\\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\\r\\n\\r\\n # map them for the train and test data sets\\r\\n X_train = X_train.copy()\\r\\n X_train[col] = X_train[col].map(unique_vals_dict)\\r\\n X_validation = X_validation.copy()\\r\\n X_validation[col] = X_validation[col].map(unique_vals_dict)\\r\\n\\r\\n # store the mapping for later use\\r\\n map_dict[col] = unique_vals_dict\\r\\n\\r\\n train_model = xgb.XGBRegressor(objective='reg:linear',\\r\\n colsample_bytree=0.3,\\r\\n learning_rate=learning_rate,\\r\\n max_depth=max_depth,\\r\\n alpha=10,\\r\\n n_estimators=n_estimators,\\r\\n verbosity=2)\\r\\n\\r\\n train_model.fit(X_train, y_train)\\r\\n\\r\\n pred = train_model.predict(X_validation)\\r\\n\\r\\n elif model == 'regression':\\r\\n\\r\\n # Use linear regression only if subcategory and brand name and segment is included in the list\\r\\n # Compute linear regression coefficients for the following combinations\\r\\n # 1) Subcategory and brand\\r\\n # 2) Segment\\r\\n # 3) Subcategory\\r\\n\\r\\n # Primary regressor variable includes discount_depth_rank\\r\\n\\r\\n # Compute coefficients for the remaining fields in the input data set\\r\\n # Output the R^2, p_value, and stdev for each combination\\r\\n # Follow a hierarchy when applying the model to each sku\\r\\n # If the subcategory and brand the sku sits in has an R^2, p_value and stdev smaller/ larger than a given threshold,\\r\\n # use, the segment model and likewise, when the segment model has an R^2, p_value and stdev smaller/ larger than a given\\r\\n # threshold, use the subcategory model\\r\\n\\r\\n # We will only thus be able to predict values for segments/ categories and subcategories where there has been a\\r\\n # promotion in the past\\r\\n if ('category' not in list(input_data[input_features].columns)) or \\\\\\r\\n ('subcategory' not in list(input_data[input_features].columns)) or \\\\\\r\\n ('segment' not in list(input_data[input_features].columns)) or \\\\\\r\\n ('sku_root_id' not in list(input_data[input_features].columns)) or \\\\\\r\\n ('promo_mechanic_dd' not in list(input_data[input_features].columns)):\\r\\n logger.error(\\r\\n \\\"Currently performing a linear regression per subcategory and/ or brand and/ or segment with discount depth rank \\\"\\r\\n \\\"as the primary regressor. However subcategory and brand name and segment and discount depth rank is not defined as \\\"\\r\\n \\\"an input variable!\\\")\\r\\n raise ValueError('Subcategory and/ or brand name and/ or segment and discount depth rank is not defined as an input variable')\\r\\n\\r\\n # For simplicity, use all data to train the model and compute the R2, stdev, intercept and coefficient\\r\\n logger.info(\\\"For regression, both train and test datasets will be used to train the model...\\\")\\r\\n logger.info(\\\"Combined sample dataset includes {} samples...\\\".format(input_data.shape[0]))\\r\\n\\r\\n # Loop through each combination and compute the regression\\r\\n combination = {('sku_root_id', 'promo_mechanic_dd'): 1, ('segment', 'promo_mechanic_dd'): 2,\\r\\n ('subcategory', 'promo_mechanic_dd'): 3, ('category', 'promo_mechanic_dd'): 4}\\r\\n all_combination = ('sku_root_id', 'segment', 'subcategory', 'category', 'promo_mechanic_dd')\\r\\n\\r\\n # Convert all categorical variables into numeric for regression\\r\\n input_data_train = input_data.copy()\\r\\n for col in cat_columns:\\r\\n if col in list(input_data.columns) and col not in all_combination:\\r\\n # get unique values\\r\\n unique_vals = input_data[col].unique()\\r\\n unique_vals_dict = dict([(val, num) for num, val in enumerate(unique_vals)])\\r\\n\\r\\n # map the input dataset\\r\\n input_data_train[col] = input_data_train[col].map(unique_vals_dict)\\r\\n\\r\\n # store the mapping for later use\\r\\n map_dict[col] = unique_vals_dict\\r\\n\\r\\n # Create a dataframe to store the results\\r\\n train_model_all = pd.DataFrame(columns=['rank', 'agg_fields', 'key', 'model', 'no_data_points', 'outlier_data_points',\\r\\n 'r2', 'rmse', 'mean_error', 'mae', 'mape', 'f_prob', 'coeff_c0', 'coeff_duration',\\r\\n 'coeff_in_gondola', 'coeff_in_leaflet', 'p_coeff_c0', 'p_coeff_duration',\\r\\n 'p_coeff_in_gondola', 'p_coeff_in_leaflet', 'VIF_coeff_c0', 'VIF_coeff_duration',\\r\\n 'VIF_coeff_in_gondola', 'VIF_coeff_in_leaflet'])\\r\\n\\r\\n # Create a dataframe to store the validation set to compute overall metrics\\r\\n valid_model = pd.DataFrame()\\r\\n filtered_model = pd.DataFrame()\\r\\n\\r\\n for agg_list in combination.keys():\\r\\n\\r\\n # Training model for combination\\r\\n logger.info(\\\"Training linear regression model for {a}...\\\".format(a=agg_list))\\r\\n\\r\\n # get unique values\\r\\n unique_df = input_data_train.drop_duplicates(list(agg_list))[list(agg_list)].reset_index(drop=True)\\r\\n logger.info(\\\"There are {a} unique {b} in the data...\\\".format(a=unique_df.shape[0], b=agg_list))\\r\\n\\r\\n # group by agg_list\\r\\n input_data_model = input_data_train.groupby(list(agg_list))\\r\\n\\r\\n # Select the list of input attributes not in agg_list\\r\\n training_list = list(set(list(input_data_train[input_features].columns)) - set(all_combination))\\r\\n logger.debug(\\\"Training features include {}\\\".format(training_list))\\r\\n\\r\\n for key, group in input_data_model:\\r\\n\\r\\n # Convert key to tuple if not\\r\\n if not isinstance(key, tuple):\\r\\n key_t = (key,)\\r\\n else:\\r\\n key_t = key\\r\\n\\r\\n # Train the model for each group\\r\\n logger.info(\\\"Training linear regression model for {a}...\\\".format(a=key_t))\\r\\n logger.info(\\\"There are {a} data samples...\\\".format(a=group.shape[0]))\\r\\n\\r\\n n_data_points = group.shape[0]\\r\\n\\r\\n # Lets remove all outlier data points with high z scores\\r\\n # q_group = group[output_features[0]].quantile(0.95)\\r\\n #\\r\\n # outliers = group[group[output_features[0]] >= q_group]\\r\\n # logger.info(\\\"Removing outlier data points...\\\")\\r\\n # logger.info(\\\"Based on 95% quantiles, {} sample data points removed...\\\".format(outliers.shape[0]))\\r\\n #\\r\\n # outlier_data_points = outliers.shape[0]\\r\\n #\\r\\n # group = group[group[output_features[0]] < q_group]\\r\\n outlier_data_points = 0\\r\\n\\r\\n # If there is less than 3 sample data points, then skip\\r\\n if group.shape[0] < 2:\\r\\n logger.info(\\\"Too few data sample needed for training...\\\")\\r\\n logger.info(\\\"Skipping to next group....\\\")\\r\\n\\r\\n train_model_dict = {'rank': combination[agg_list],\\r\\n 'agg_fields': agg_list,\\r\\n 'key': key_t,\\r\\n 'model': None,\\r\\n 'no_data_points': group.shape[0],\\r\\n 'outlier_data_points': outlier_data_points,\\r\\n 'r2': None,\\r\\n 'rmse': None,\\r\\n 'mean_error': None,\\r\\n 'mae': None,\\r\\n 'mape': None,\\r\\n 'f_prob': None,\\r\\n 'coeff_c0': None,\\r\\n 'coeff_duration': None,\\r\\n 'coeff_in_gondola': None,\\r\\n 'coeff_in_leaflet': None,\\r\\n 'p_coeff_c0': None,\\r\\n 'p_coeff_duration': None,\\r\\n 'p_coeff_in_gondola':None,\\r\\n 'p_coeff_in_leaflet':None,\\r\\n 'VIF_coeff_c0':None,\\r\\n 'VIF_coeff_duration':None,\\r\\n 'VIF_coeff_in_gondola':None,\\r\\n 'VIF_coeff_in_leaflet':None\\r\\n }\\r\\n\\r\\n # add train model to dataframe\\r\\n train_model_all = train_model_all.append(train_model_dict, ignore_index=True)\\r\\n continue\\r\\n\\r\\n # Append group to validation dataset\\r\\n v_group = group[list(all_combination)].copy()\\r\\n valid_model = valid_model.append(v_group.drop_duplicates())\\r\\n filtered_model = filtered_model.append(group)\\r\\n n_data_points_used = group.shape[0]\\r\\n\\r\\n t_list = training_list+output_features\\r\\n group = group[t_list]\\r\\n\\r\\n # plot the relationship\\r\\n # plotScatter(group, \\\"in_gondola_flag\\\", output_features[0], \\\"duration_wks\\\")\\r\\n\\r\\n X_reg = group[training_list]\\r\\n\\r\\n # force to add constant if a constant values is already supplied\\r\\n # this will ensure consistency in output format\\r\\n X_reg = sm.add_constant(X_reg, has_constant='add')\\r\\n y_reg = group[output_features]\\r\\n\\r\\n # Train robust linear regression model\\r\\n\\r\\n reg_model = sm.OLS(y_reg, X_reg).fit()\\r\\n logger.info(\\\"Completed model training...\\\")\\r\\n\\r\\n # Log the model results\\r\\n logger.debug(\\\"Model results...\\\")\\r\\n logger.debug(\\\"\\\\n{}\\\".format(reg_model.summary()))\\r\\n\\r\\n mape = np.divide(reg_model.resid.values,\\r\\n y_reg[output_features[0]].values)\\r\\n mape[mape == np.inf] = 0\\r\\n mape[mape == -np.inf] = 0\\r\\n mape = np.median(np.abs(mape))\\r\\n\\r\\n # Compute variance inflation factors\\r\\n VIF = [variance_inflation_factor(X_reg.values, i) for i in range(X_reg.shape[1])]\\r\\n\\r\\n train_model_dict = {'rank': combination[agg_list],\\r\\n 'agg_fields': agg_list,\\r\\n 'key': key_t,\\r\\n 'model': reg_model,\\r\\n 'no_data_points': n_data_points_used,\\r\\n 'outlier_data_points': outlier_data_points,\\r\\n 'r2': reg_model.rsquared,\\r\\n 'rmse': np.sqrt(np.mean(np.square(reg_model.resid.values))),\\r\\n 'mean_error': np.mean(reg_model.resid.values),\\r\\n 'mae': np.median(np.abs(reg_model.resid.values)),\\r\\n 'mape': mape,\\r\\n 'f_prob': reg_model.f_pvalue,\\r\\n 'coeff_c0': reg_model.params['const'],\\r\\n 'coeff_duration': reg_model.params['duration_wks'],\\r\\n 'coeff_in_gondola': reg_model.params['in_gondola_flag'],\\r\\n 'coeff_in_leaflet': reg_model.params['in_leaflet_flag'],\\r\\n 'p_coeff_c0': reg_model.pvalues['const'],\\r\\n 'p_coeff_duration': reg_model.pvalues['duration_wks'],\\r\\n 'p_coeff_in_gondola':reg_model.pvalues['in_gondola_flag'],\\r\\n 'p_coeff_in_leaflet':reg_model.pvalues['in_leaflet_flag'],\\r\\n 'VIF_coeff_c0':VIF[0],\\r\\n 'VIF_coeff_duration':VIF[3],\\r\\n 'VIF_coeff_in_gondola':VIF[2],\\r\\n 'VIF_coeff_in_leaflet':VIF[1]}\\r\\n\\r\\n # add train model to dataframe\\r\\n train_model_all = train_model_all.append(train_model_dict, ignore_index=True)\\r\\n\\r\\n\\r\\n if model in ('catboost', 'lightgbm', 'xgboost'):\\r\\n\\r\\n # Evaluate the model\\r\\n rmse = np.sqrt(mean_squared_error(y_validation, pred))\\r\\n logger.info(\\\"RMSE: {}\\\".format(rmse))\\r\\n\\r\\n mean_error = np.mean(y_validation[output_features[0]].values - pred)\\r\\n logger.info(\\\"Mean Error: {}\\\".format(mean_error))\\r\\n\\r\\n mae = np.median(np.absolute(y_validation[output_features[0]].values - pred))\\r\\n logger.info(\\\"MAE: {}\\\".format(mae))\\r\\n\\r\\n mape = np.divide(y_validation[output_features[0]].values - pred, y_validation[output_features[0]].values)\\r\\n mape[mape == np.inf] = 0\\r\\n mape[mape == -np.inf] = 0\\r\\n mape = np.median(np.abs(mape))\\r\\n logger.info(\\\"MAPE: {}%\\\".format(mape * 100))\\r\\n\\r\\n val_std = np.std(y_validation[output_features[0]].values)\\r\\n logger.info(\\\"Benchmark STD: {}\\\".format(val_std))\\r\\n\\r\\n val_mean = np.mean(y_validation[output_features[0]].values)\\r\\n logger.info(\\\"Benchmark Mean Error: {}\\\".format(val_mean))\\r\\n\\r\\n val_mae = np.mean(np.absolute(y_validation[output_features[0]].values))\\r\\n logger.info(\\\"Benchmark MAE: {}\\\".format(val_mae))\\r\\n\\r\\n logger.info(\\\"Benchmark MAPE: -100%\\\")\\r\\n\\r\\n # plot the results\\r\\n plothist(y_validation, pred)\\r\\n\\r\\n # plot the feature importance\\r\\n plotImp(model, train_model, X, num=20)\\r\\n\\r\\n join_fields = None\\r\\n filtered_model = None\\r\\n\\r\\n\\r\\n elif model in ('regression'):\\r\\n\\r\\n # save the full model\\r\\n train_model_all.to_csv(\\\"train_model_all.csv\\\", encoding='utf-8', index=False)\\r\\n\\r\\n # get the unique values in the validation model\\r\\n valid_model = valid_model.drop_duplicates(list(all_combination))[list(all_combination)].reset_index(drop=True)\\r\\n valid_model['p_id'] = valid_model[list(all_combination)].apply(tuple, axis=1)\\r\\n\\r\\n # For each line in valid_model, find the corresponding rows in train model where key is in p_id\\r\\n train_model = pd.DataFrame()\\r\\n logger.info(\\\"Computing best model for each {a}\\\".format(a=all_combination))\\r\\n for index, row in valid_model.iterrows():\\r\\n\\r\\n # logging progress\\r\\n logger.info(\\\"{a} out of {b} permutations complete...\\\".format(a=index, b=valid_model.shape[0]))\\r\\n\\r\\n # find all the rows in train model that satisfy the criterion\\r\\n train_model_all['valid'] = train_model_all.apply(lambda x: set(x.key).issubset(row.p_id), axis=1)\\r\\n\\r\\n # filter on the rows that are true\\r\\n valid_rows = train_model_all[train_model_all['valid'] == True]\\r\\n\\r\\n # # Using the rank condition, lets filter on only the model which has the most favorable property\\r\\n # # R2 threshold > 0.2 and\\r\\n # if len(valid_rows[(valid_rows['r2'] >= 0.2)]) >= 1:\\r\\n # valid_rows = valid_rows[valid_rows['r2'] >= 0.2]\\r\\n\\r\\n # valid_rows = valid_rows.sort_values(by='mape', ascending=True).head(1)\\r\\n\\r\\n # Using the rank condition, lets filter where the # data samples is > min_data samples, then sort by rank\\r\\n # and take the bottom row\\r\\n if len(valid_rows[(valid_rows['no_data_points'] >= min_samples)]) >= 1:\\r\\n valid_rows = valid_rows[valid_rows['no_data_points'] >= min_samples]\\r\\n valid_rows['valid_dp'] = True\\r\\n else:\\r\\n valid_rows['valid_dp'] = False\\r\\n\\r\\n valid_rows = valid_rows.sort_values(by='rank', ascending=True).head(1)\\r\\n\\r\\n # Include the valid model all combinations cols\\r\\n valid_rows.reset_index(drop=True, inplace=True)\\r\\n row_df = row.to_frame().T\\r\\n row_df.reset_index(drop=True, inplace=True)\\r\\n\\r\\n valid_rows = pd.concat([valid_rows, row_df], axis=1)\\r\\n\\r\\n train_model = train_model.append(valid_rows)\\r\\n\\r\\n # Compute aggregate statistics\\r\\n rmse = train_model['rmse'].mean()\\r\\n logger.info(\\\"RMSE: {}\\\".format(rmse))\\r\\n\\r\\n mean_error = train_model['mean_error'].mean()\\r\\n logger.info(\\\"Mean Error: {}\\\".format(mean_error))\\r\\n\\r\\n mae = train_model['mae'].median()\\r\\n logger.info(\\\"MAE: {}\\\".format(mae))\\r\\n\\r\\n mape = train_model['mape'].median()\\r\\n logger.info(\\\"MAPE: {}%\\\".format(mape * 100))\\r\\n\\r\\n mae_std = train_model['mae'].std()\\r\\n logger.info(\\\"MAE_std: {}\\\".format(mae_std))\\r\\n\\r\\n mape_std = train_model['mape'].std()\\r\\n logger.info(\\\"MAPE_std: {}%\\\".format(mape_std * 100))\\r\\n\\r\\n R2_avg = train_model['r2'].mean()\\r\\n logger.info(\\\"R^2_avg: {}\\\".format(R2_avg))\\r\\n\\r\\n join_fields = list(all_combination)\\r\\n\\r\\n return train_model, map_dict, mae, mape, join_fields, filtered_model\",\n \"def df_monthly_anomaly(data_store, hemi, month):\\n df = sit.monthly(hemisphere=hemi, data_store=data_store, month_num=month)\\n\\n # scale to millions of sq km\\n df['extent'] = df.total_extent_km2 / 1e6\\n\\n # percentage difference between the climatological mean and the extent for a\\n # given month\\n df['anomaly'] = sit.monthly_percent_anomaly(df.extent, nt.DEFAULT_CLIMATOLOGY_YEARS)\\n\\n # trendline for anomaly\\n df['anomaly_trend'] = sit.trend(df.anomaly)\\n\\n df = df[['extent', 'anomaly', 'anomaly_trend']]\\n\\n return df\",\n \"def predict():\\n ### YOUR CODE GOES HERE\\n\\n predictions = list(np.random.randint(low=0, high=1, size=2073))\\n ### YOUR CODE ENDS HERE\\n print(predictions) # Should be a dictionary of forecasts\\n # i.e. {\\\"id1\\\" : forecast, \\\"id2\\\": forecast, ...}\\n return predictions\",\n \"def prob():\\n # Get decision score for our example that came with the request\\n data = flask.request.json\\n #data[]\\n year = data[\\\"year\\\"]\\n Month = data[\\\"month\\\"]\\n Day = data[\\\"day\\\"]\\n species = data[\\\"species\\\"]\\n lat = data[\\\"lattitude\\\"]\\n lng = data[\\\"longitude\\\"]\\n\\n # find the closest trap to the clicked location\\n closest_trap = find_closest_trap(lat,lng,trap_stations)\\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\\n \\n # add 0 in front of month\\n month = \\\"0\\\"+ Month if len(Month) == 1 else Month\\n day = \\\"0\\\"+ Day if len(Day) == 1 else Day\\n date = year + \\\"-\\\" + month + \\\"-\\\" + day\\n trap[\\\"date\\\"] = date\\n\\n # convert datetime to str to match differing formats\\n weather.station = weather.station.astype(str)\\n trap.station = trap.station.astype(int).astype(str)\\n weather.date = weather.date.astype(str)\\n\\n # merge trap & weather dataframes\\n merged = trap.merge(weather,on=[\\\"date\\\",\\\"station\\\"],how=\\\"inner\\\")\\n\\n # convert back datetime\\n merged.date = pd.to_datetime(merged.date)\\n\\n # extract features from datetime and dewpoint\\n merged['month'] = month\\n merged['year'] = year\\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\\n # add risk feature if month is between june and sept (peak virus season)\\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\\n\\n # Reorder columns & Assign to X\\n merged2 = merged.drop([\\\"date\\\", \\\"year\\\", \\\"trap\\\",\\\"species\\\"],1)\\n cols = [\\\"month\\\",\\\"week\\\",\\\"longitude\\\",\\\"latitude\\\",\\\"tmax\\\",\\\"tmin\\\",\\\"tavg\\\",\\\"depart\\\",\\\"dewpoint\\\",\\n \\\"wetbulb\\\",\\\"heat\\\",\\\"cool\\\",\\\"sunrise\\\",\\\"sunset\\\",\\\"preciptotal\\\",\\\"resultspeed\\\",\\\"avgspeed\\\",\\n \\\"month\\\",\\\"dry\\\",\\\"risk\\\"]\\n X = merged2[cols]\\n \\n # predict probability\\n pred = loaded_model.predict_proba(X)\\n prob = round(pred[0][1]*100,2)\\n return flask.jsonify(prob)\",\n \"def forecast_plot(df, predictions):\\n df['Forecast'] = np.nan\\n\\n last_date = df.iloc[-1].name\\n last_unix = last_date.timestamp()\\n one_day = 86400 # sec in day\\n next_unix = last_unix + one_day\\n\\n for i in predictions:\\n next_date = datetime.datetime.fromtimestamp(next_unix)\\n next_unix += one_day\\n df.loc[next_date] = [np.nan for _ in range(len(df.columns) - 1)] + [i]\\n\\n plt.plot(df['Close'])\\n plt.plot(df['Forecast'])\\n plt.legend(bbox_to_anchor=(1.01, 1))\\n plt.xlabel('Time(Yr-M)')\\n plt.ylabel('Value(USD)')\\n plt.show()\",\n \"def forecast(self, stock):\\n # Load the trained model\\n model_handler = ModelHandler()\\n model = model_handler.load_json_model(stock)\\n\\n # Importing the training set\\n dataset = pd.read_csv(stock.csv_name)\\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\\n\\n # Create the test dataset\\n dataset_test = dataset[len(dataset) - 30:]\\n real_stock_price = dataset_test.iloc[:, 1:2].values\\n dataset = dataset['Open']\\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\\n inputs = inputs.reshape(-1, 1)\\n\\n # Feature Scaling\\n sc = MinMaxScaler(feature_range=(0, 1))\\n inputs = sc.fit_transform(inputs)\\n\\n x_test = []\\n x_test.append(inputs[0:60, 0])\\n predicted_values = []\\n for i in range(1, 31):\\n x_test_np = np.array(x_test)\\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\\n new_data = model.predict(x_test_np)\\n predicted_values.append(new_data[0])\\n x_test[0] = np.delete(x_test[0], 0)\\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\\n\\n predicted_values = sc.inverse_transform(predicted_values)\\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\\n plt.gcf().autofmt_xdate()\\n plt.title(f'{stock.ticker} Stock Price Prediction')\\n plt.xlabel('Time')\\n plt.ylabel(f'{stock.ticker} Stock Price')\\n plt.legend()\\n plt.show()\",\n \"def predict(self, requested_day):\\n\\n df = load_data()\\n\\n # preprocess\\n df = preprocess(df)\\n df = filter_by_country(df, self.country_code)\\n\\n # separate cases from data\\n dates, Y = separate(df)\\n\\n # normalize Y\\n Y = normalize(Y)\\n\\n # apply look_back and generate needed samples\\n X, _ = apply_lookback(Y, look_back=self.look_back)\\n\\n # reshape to fit the model\\n X = reshape(X)\\n dates = reshape(dates)\\n dates = dates[self.look_back:]\\n\\n # unite samples (X) with dates\\n # a date of a sample corresponds to the future prediction value (Y)\\n united_samples = unite_dates_samples(dates.reshape(-1, 1),\\n X.reshape(-1, self.look_back))\\n\\n last_day = requested_day\\n predicted = 0\\n\\n for step in range(self.look_forward):\\n sample, last_day = get_sample(united_samples, last_day)\\n sample = sample.reshape(1, 1, self.look_back)\\n\\n predicted = self.model.predict(sample.astype(np.float32))\\n\\n united_samples, last_day = append_sample(united_samples, predicted,\\n self.look_back,\\n last_day)\\n\\n # date of the prediction\\n last_day = change_date(last_day, delta_days=-1)\\n\\n # chosen starting date\\n start_avail_day = change_date(last_day,\\n delta_days=-self.look_back)\\n\\n # convert to a real number of COVID-19 cases\\n predicted = denormalize(predicted)[0, 0]\\n predicted = int(predicted)\\n\\n prediction_info = {\\n 'prediction_date': last_day,\\n 'starting_date': start_avail_day,\\n 'prediction_new_cases': predicted\\n }\\n\\n return prediction_info, united_samples\",\n \"def do_forecasting(self, df_forecast, mon_names, available_target=False, name_target='yield'):\\n data = df_forecast.as_matrix(columns=self.vars_features) # convert dataframe column to matrix\\n predicted = self.model.predict(data)\\n\\n if available_target:\\n expected = df_forecast.as_matrix(columns=[name_target]).ravel()\\n if not self.classify: # REGRESSION\\n # Compute stats\\n results = compute_stats.ols(predicted.tolist(), expected.tolist())\\n bias = compute_stats.bias(predicted, expected)\\n rmse = compute_stats.rmse(predicted, expected)\\n mae = compute_stats.mae(predicted, expected)\\n\\n # Plot!\\n plot.plot_regression_scatter(expected, np.asarray(predicted),\\n annotate=r'$r^{2}$ ' + '{:0.2f}'.format(results.rsquared) + '\\\\n' +\\n 'peak NDVI date: ' + self.time_peak_ndvi.strftime('%b %d'),\\n xlabel='Expected yield',\\n ylabel='Predicted yield',\\n title=mon_names + ' ' + str(int(df_forecast[self.season].unique()[0])),\\n fname=self.task + '_' + '_'.join([mon_names]) + '_' + self.crop,\\n out_path=self.path_out_dir)\\n\\n # global expected vs predicted\\n if self.debug:\\n # any non-existing index will add row\\n self.df_global.loc[len(self.df_global)] = [np.nanmean(expected), np.nanmean(predicted), mon_names,\\n self.forecast_yr]\\n\\n return predicted, {'RMSE': rmse, 'MAE': mae, r'$r^{2}$': results.rsquared, 'Bias': bias}\\n else: # CLASSIFICATION\\n # Convert from crop condition class (e.g. 4) to string (e.g. exceptional)\\n expected, predicted = compute_stats.remove_nans(expected, predicted)\\n cm = confusion_matrix(expected, predicted, labels=self.dict_cc.keys()).T\\n\\n # Compute and plot class probabilities\\n proba_cc = self.model.predict_proba(data)\\n df_proba = pd.DataFrame(proba_cc, columns=self.dict_cc.values())\\n plot.plot_class_probabilities(df_proba, fname='proba_' + '_'.join([mon_names]) + '_' + self.crop,\\n out_path=self.path_out_dir)\\n\\n # Plot confusion matrix\\n plot.plot_confusion_matrix(cm, normalized=False, fname='cm_' + '_'.join([mon_names]) + '_' + self.crop,\\n xlabel='True class', ylabel='Predicted class', ticks=self.dict_cc.values(),\\n out_path=self.path_out_dir)\\n\\n # Normalize and plot confusion matrix\\n cm_normalized = normalize(cm.astype(float), axis=1, norm='l1')\\n plot.plot_confusion_matrix(cm_normalized, fname='norm_cm_' + '_'.join([mon_names]) + '_' + self.crop,\\n xlabel='True class', ylabel='Predicted class', normalized=True,\\n ticks=self.dict_cc.values(), out_path=self.path_out_dir)\\n\\n score_accuracy = accuracy_score(expected, predicted) * 100.0\\n score_precision = precision_score(expected, predicted, average='weighted') * 100.0\\n return predicted, {'Accuracy': score_accuracy, 'Precision': score_precision}\\n else:\\n return predicted, {'RMSE': np.nan, 'MAE': np.nan, r'$r^{2}$': np.nan, 'Bias': np.nan,\\n 'Nash-Sutcliff': np.nan}\",\n \"def prediction(home, away, stats, model):\\n\\n # Define categorical variables and initiate year variable.\\n categorical = ['buildUpPlaySpeedClass_home', 'buildUpPlayDribblingClass_home',\\n 'buildUpPlayPassingClass_home', 'buildUpPlayPositioningClass_home', \\n 'chanceCreationPassingClass_home', 'chanceCreationCrossingClass_home', \\n 'chanceCreationShootingClass_home', 'chanceCreationPositioningClass_home',\\n 'defencePressureClass_home', 'defenceAggressionClass_home', \\n 'defenceTeamWidthClass_home', 'defenceDefenderLineClass_home', \\n 'team_long_name_home', 'buildUpPlaySpeedClass_away',\\n 'buildUpPlayDribblingClass_away', 'buildUpPlayPassingClass_away', \\n 'buildUpPlayPositioningClass_away', 'chanceCreationPassingClass_away', \\n 'chanceCreationCrossingClass_away', 'chanceCreationShootingClass_away',\\n 'chanceCreationPositioningClass_away', 'defencePressureClass_away', \\n 'defenceAggressionClass_away', 'defenceTeamWidthClass_away', \\n 'defenceDefenderLineClass_away', 'team_long_name_away']\\n year = datetime.datetime.now().year\\n\\n # Create dummy variables.\\n stats = pd.get_dummies(stats, columns=categorical)\\n\\n # Map team attributes and playing styles for home and away teams.\\n test = stats[(stats['team_long_name_home_' + home.replace(' ', '')] == 1)\\n & (stats['team_long_name_away_' + away.replace(' ', '')] == 1)]\\n test['season_year'] = year\\n\\n # Make prediction from the loaded logistic regression model.\\n predict = model.predict_proba(test)[0]\\n\\n return predict\",\n \"def forc_model(self):\\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\\n for add in self.X.address1.unique():\\n add_mask = self.X.address1 == add\\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\\n top_index = foo.index[0]\\n clust = int(foo.cluster.values[0])\\n # get values from last visit for store\\n base_input = foo[self.model_mask_cols].values[0]\\n base_actual = self.y[top_index]\\n lag2_val = base_input[lag1_loc]\\n lag1_val = base_actual\\n\\n for i in range(1, self.num_periods + 1):\\n model = self.model_list[clust]\\n inputs = base_input\\n inputs[lag1_loc] = lag1_val\\n inputs[lag2_loc] = lag2_val\\n \\n pred = model.predict(inputs.reshape(1, -1))\\n self._update_cust_table(add, i, pred)\\n \\n lag2_val = lag1_val\\n lag1_val = pred\",\n \"async def _forecast_single(\\n self,\\n model: Prophet\\n ) -> pd.DataFrame:\\n future = model.make_future_dataframe(self._periods, 'H', False)\\n return model.predict(future)\",\n \"def train_predict(train_file_arg):\\n\\n # Load command line arguments\\n train_file = train_file_arg\\n parameter_file = \\\"prophet/lstm/trainingConfig.json\\\"\\n\\n # Load training parameters\\n params = json.loads(open(parameter_file).read())\\n\\n # Load time series dataset, and split it into train and test\\n x_train, y_train, x_test, _, x_test_raw, _,\\\\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\\n\\n # Build RNN (LSTM) model\\n lstm_layer = [1, params[\\\"window_size\\\"], params[\\\"hidden_unit\\\"], 1]\\n model = buildModel.rnn_lstm(lstm_layer, params)\\n\\n # Train RNN (LSTM) model with train set\\n model.fit(\\n x_train,\\n y_train,\\n batch_size=params[\\\"batch_size\\\"],\\n epochs=params[\\\"epochs\\\"],\\n validation_split=params[\\\"validation_split\\\"])\\n\\n # Check the model against test set\\n predicted = buildModel.predict_next_timestamp(model, x_test)\\n predicted_raw = []\\n for i in range(len(x_test_raw)):\\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\\n\\n # Predict next time stamp\\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\\n print(\\\"The next time stamp forecasting is: {}\\\".format(next_timestamp_raw))\\n\\n # Add 5 minutes for a new timestamp of predictions\\n last_datetime = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(last_datetime_epoch))\\n last_datetime = datetime.strptime(last_datetime, \\\"%Y-%m-%d %H:%M:%S\\\")\\n new_datetime = last_datetime + timedelta(seconds=300)\\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\\n new_datetime = new_datetime.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n # Concatenate datetime and price forecast\\n new_entry = \\\"coin_name,\\\" + str(new_datetime_epoch) + \\\",\\\" \\\\\\n + str(next_timestamp_raw) + \\\",coin_supply\\\" + \\\",coin_mc\\\" + \\\"\\\\n\\\"\\n\\n # Write to CSV file of new prediction\\n fd = open(train_file, \\\"a\\\")\\n fd.write(new_entry)\\n fd.close()\\n\\n # Return new prediction\\n return [new_datetime, str(next_timestamp_raw)]\",\n \"def _predict(self):\\n bull_wr = self.bull_win_rate\\n self.df = self.df.assign(bull_wr=bull_wr)\",\n \"def naive_forecasting(x_test, y_test):\\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred\",\n \"def compute(self, today, asset_ids, out, high):\\n start_month = today.month\\n current_month = start_month - 1 if start_month - 1 > 0 else 12\\n prev_month = current_month - 1 if current_month - 1 > 0 else 12\\n idx = -1\\n date_idx = today - 1\\n month_idx = date_idx.month\\n\\n current_end_month_idx = 0\\n current_start_month_idx = 0\\n\\n while idx > -MonthlyHigh.window_length:\\n if month_idx == current_month:\\n if current_end_month_idx == 0:\\n current_end_month_idx = idx\\n if month_idx == prev_month:\\n if current_start_month_idx == 0:\\n current_start_month_idx = idx + 1\\n\\n date_idx = date_idx - 1\\n month_idx = date_idx.month\\n idx = idx - 1\\n\\n if current_end_month_idx == -1:\\n current_month_high = high[current_start_month_idx:, :].max(\\n axis=0)\\n # current_month_low = low[current_start_month_idx:, :].min(axis=0)\\n else:\\n current_month_high = high[current_start_month_idx:current_end_month_idx + 1, :].max(\\n axis=0)\\n\\n # current_month_low = low[current_start_month_idx:current_end_month_idx + 1, :].min(axis=0)\\n out[:] = current_month_high\",\n \"def arima(X):\\n \\n sq_preds = status_quo_model(X)\\n X[\\\"id\\\"] = X.index\\n # get it to long form\\n longX = pd.melt(X, id_vars=['id'], value_vars=list(X.columns.values)[:-1])\\n # change the default column name to a proper one\\n longX['year'] = longX['variable']\\n longX.drop(columns = 'variable', inplace=True)\\n # sort by id and year so that we can fit individual time series models for each indicator\\n longX = longX.sort_values(['id', 'year'])\\n # group\\n grouped = longX.groupby('id')['value']\\n # for each group (a country-indicator combination)\\n forecasts = list()\\n with warnings.catch_warnings():\\n warnings.filterwarnings(\\\"ignore\\\")\\n for k, g in grouped:\\n g = g[-5:]\\n gi = g.interpolate(method = 'linear', limit = 50, limit_direction = 'backward')\\n model = ARIMA(gi, order=(2,1,0))\\n try:\\n results = model.fit(disp = 0)\\n forecasts.append(results.forecast()[0][0])\\n except (ValueError, numpy.linalg.linalg.LinAlgError) as e:\\n forecasts.append(sq_preds[k])\\n return forecasts\",\n \"def __init__(self,\\n model_configuration,\\n app_id=None,\\n predict_interval=PREDICT_INTERVAL,\\n metric=None):\\n super(MidtermArmaPredictor, self).__init__(model_configuration, app_id,\\n predict_interval, metric)\",\n \"def predict(self, instances):\\r\\n raise NotImplementedError\",\n \"def predict():\\n\\n\\n json_payload = request.json\\n #LOG.info(f\\\"JSON payload: %s\\\" %json_payload)\\n inference_payload = pd.DataFrame(json_payload)\\n #LOG.info(\\\"inference payload DataFrame: %s\\\" %inference_payload)\\n scaled_payload = scale(inference_payload)\\n prediction = list(clf.predict(scaled_payload))\\n return jsonify({'prediction': prediction})\",\n \"def predict(request, route_id, stop_id, planned_arrival, day_of_week, month,\\n departure_time_seconds, t_minus_1118):\\n\\n stop_lat_lon_dict = '/home/student/analytics/ml_dictionaries/db_stop_latlon.json'\\n\\n cluster_dict = '/home/student/analytics/ml_dictionaries/stops_to_cluster_300m_radius.json'\\n\\n labels_dir = '/home/student/analytics/models_label_encodings/'\\n\\n models_dir = '/home/student/analytics/models/'\\n\\n # this call updates the weather attributes to the current time and / or day\\n # implement front-end check to queries to JSON response out of range\\n\\n time = Time()\\n time.find_date(departure_time_seconds)\\n time.check_day(day_of_week)\\n\\n print(time.day_of_week, departure_time_seconds)\\n weather.update_rain_temp(time.day_of_week, departure_time_seconds)\\n\\n # this instantiates our formatter object with prediction inputs\\n\\n formatter = FormatInput(route_id,\\n stop_id,\\n planned_arrival,\\n weather.rain,\\n weather.temp,\\n time.day_of_week,\\n month,\\n t_minus_1118,\\n time.date,\\n stop_lat_lon_dict,\\n cluster_dict)\\n\\n # this adds and transforms features in preparation for prediction\\n\\n formatter.derive_features()\\n\\n # this label encodes the appropriate categorical variables\\n\\n formatter.label_encode(labels_dir)\\n\\n # this creates an input vector attribute to pass to the predictor\\n formatter.create_vector()\\n\\n # instantiate a predictor object\\n\\n predictor = Predict()\\n\\n # generate a prediction for a line, given a vector which is passed\\n\\n result = predictor.predict(models_dir, formatter.line, formatter.vector)\\n\\n # return the prediction\\n\\n return HttpResponse(result)\",\n \"def makePrediction(toPredict,candidateList):\\n\\n print(\\\"Predicting for: \\\" + toPredict['title'])\\n\\n if PREDICT_RATING:\\n actualRating = float(toPredict['actual_imdb_rating'])\\n predictedRating = round(predictRating(toPredict, candidateList),1)\\n percentDiff = round((actualRating - predictedRating) / actualRating * 100, 2)\\n print(\\\"Predicted rating: \\\" + str(predictedRating) + \\\" Actual Rating: \\\" + str(actualRating))\\n print(\\\"Difference in rating prediction: \\\" + str(percentDiff) + \\\"%\\\")\\n else:\\n predictedRating = round(predictRating(toPredict, candidateList), 1) # using rating to prune unrelated movies.\\n actualRevenue = int(toPredict['actual_revenue'])\\n predictedRevenue = int(predictRevenue(toPredict,candidateList,predictedRating) * float(toPredict['budget'])) #PredictRevenue method return mean RoR\\n percentDiff = round((actualRevenue - predictedRevenue) / actualRevenue * 100, 2)\\n print(str(\\\"Predicted revenue: \\\" + str(predictedRevenue) + \\\" Actual Revenue: \\\" + str(actualRevenue)))\\n print(\\\"Difference in revenue prediction: \\\" + str(percentDiff) + \\\"%\\\")\\n\\n #Tabulate how close the prediction was based on percent difference\\n if abs(percentDiff) <= 5:\\n withinFive.append(0)\\n\\n if abs(percentDiff) <= 10:\\n withinTen.append(0)\\n\\n if abs(percentDiff) <= 15:\\n withinFifteen.append(0)\\n\\n if abs(percentDiff) <= 20:\\n withinTwenty.append(0)\\n\\n if abs(percentDiff) <= 30:\\n withinThirty.append(0)\",\n \"def make_predictions(df, model):\\n predictions = pd.DataFrame({\\\"move\\\": model.predict(df.drop(\\\"move\\\", axis=1))})\\n predictions[\\\"move\\\"] = predictions[\\\"move\\\"].apply(lambda x: round(x))\\n return pd.concat([df[[\\\"brand\\\", \\\"store\\\", \\\"week\\\"]].reset_index(drop=True), predictions], axis=1)\",\n \"def predict_ensemble(test_df):\\n instances = [wongnai_predictor._dataset_reader.text_to_instance(word_tokenize(review)) \\n for review in list(test_df.review)]\\n model_paths = glob('output_*/model.tar.gz')\\n all_predicted_labels = []\\n for model_path in model_paths:\\n archive = load_archive(model_path) # load trained model\\n wongnai_predictor = Predictor.from_archive(archive, 'wongnai_predictor')\\n predicted_labels = [int(wongnai_predictor.predict_instance(instance)['predicted_label']) \\n for instance in instances]\\n all_predicted_labels.append(predicted_labels)\\n all_predicted_labels = np.array(all_predicted_labels)\\n predicted_labels_vote = mode(np.array(all_predicted_labels).T, axis=-1).mode.ravel()\\n test_df['rating'] = predicted_labels_vote\\n return test_df.drop('review', axis=1)\",\n \"def predict(data: pd.DataFrame) -> pd.DataFrame:\\n return pd.DataFrame(data={\\\"prediction\\\": trained_model.predict(data)})\",\n \"def make_ensemble_prediction(trn_features, models, test_df):\\n test_id: pd.Series = test_df.iloc[:, 0]\\n test_features: pd.DataFrame = test_df.iloc[:, 1:]\\n\\n test_features = pd.concat([trn_features, test_features])\\n\\n clean_test_features, test_log = clean_features(test_features)\\n clean_test_features = clean_test_features.iloc[trn_features.shape[0]:, :]\\n\\n predictions = np.empty([0, trn_features.shape[0] - 1])\\n for model in models:\\n prediction = np.array([model.predict(clean_test_features)])\\n predictions = np.vstack([predictions, prediction])\\n\\n prediction = np.mean(predictions, axis=0)\\n\\n # print(predictions, prediction)\\n result_df = pd.DataFrame(columns=[\\\"Id\\\", \\\"SalePrice\\\"])\\n\\n for i in range(test_id.shape[0]):\\n result_df = result_df.append(pd.DataFrame([[int(test_id.iloc[i]), prediction[i]]], columns=[\\\"Id\\\", \\\"SalePrice\\\"]))\\n\\n print(result_df)\\n result_df.to_csv('../data/result.csv', index=False)\",\n \"def test_monthly_resolution_hindcast(monthly_initialized, monthly_obs):\\n monthly_initialized.lead.attrs[\\\"units\\\"] = \\\"months\\\"\\n assert compute_hindcast(monthly_initialized, monthly_obs).all()\",\n \"def rolling_arima(train, test, endog, order, exog=None, dates=None, freq=None, missing='none'):\\n history = list(train)\\n predictions = []\\n for row in test:\\n model = sm.tsa.ARIMA(endog=history, order=order, exog=exog, dates=dates, freq=freq, missing=missing)\\n model_fit = model.fit(disp=0)\\n output = model_fit.forecast()\\n yhat = output[0][0]\\n predictions.append(yhat)\\n history.append(row)\\n\\n return predictions\",\n \"def build_predictions() -> List[str]:\\n\\n with open(os.path.join(UTILS_PATH, STREETS_FOR_PREDICTIONS_FILE)) as f:\\n streets_for_predictions = f.read().split('\\\\n')[:-1]\\n\\n street_to_id = {street: ind for ind, street in enumerate(streets_for_predictions)}\\n\\n yesterday = datetime.today() - timedelta(days=7) # shift for 7 days\\n last_month = set([(yesterday - timedelta(days=x)).strftime(DAY_FORMAT) for x in range(30)])\\n\\n with open(os.path.join(UTILS_PATH, CURRENT_FILENAMES_FILE), \\\"r\\\") as f:\\n FILENAMES = json.load(f)\\n\\n filename = FILENAMES[CRASHES_AFTER_ETL]\\n conn = sql.connect(f\\\"{METABASE_DATA_PATH}{filename}.db\\\")\\n query = f\\\"{SELECT_DATETIME_STREETS_QUERY} '{filename}'\\\"\\n df = pd.read_sql_query(query, conn)\\n\\n df[DATETIME] = df[DATETIME].apply(lambda x: x.split(\\\"T\\\")[0])\\n df = df[df[STREET_NAME].isin(streets_for_predictions)]\\n df = df.reset_index(drop=True)\\n\\n df = df[df[DATETIME].isin(last_month)]\\n df[DATETIME] = pd.to_datetime(df[DATETIME])\\n df = df.reset_index(drop=True)\\n\\n uniq_dates = sorted(set(df[DATETIME]))\\n date_to_id = {date: idd for idd, date in enumerate(uniq_dates)}\\n\\n X = np.zeros((len(street_to_id), len(date_to_id)), dtype=np.uint32)\\n\\n for row in df.iterrows():\\n info = row[1]\\n date = info[DATETIME]\\n street = info[STREET_NAME]\\n\\n date_id = date_to_id[date]\\n street_id = street_to_id[street]\\n\\n X[street_id, date_id] += 1\\n\\n model = CatBoostRegressor()\\n model.load_model(os.path.join(UTILS_PATH, PREDICTOR_FILE))\\n\\n y_pred = model.predict(X)\\n top_10_streets_inds = np.argsort(y_pred)[::-1][:10]\\n top_10_streets = [streets_for_predictions[ind] for ind in top_10_streets_inds]\\n\\n logging.info(\\\"Predictions successfully made.\\\")\\n\\n return top_10_streets\",\n \"def test_03_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n \\n self.assertTrue(os.path.exists(log_file))\",\n \"def rank_pre_extract(self, mention_data, predictions):\\n mdata = pd.DataFrame(mention_data)\\n\\n\\n\\n\\n pass\",\n \"def predict(self, y_hat_df):\\n check_is_fitted(self, 'fitted_models_')\\n\\n y_hat_df = deepcopy(y_hat_df[['unique_id', 'ds']])\\n\\n forecasts = []\\n uids = []\\n dss = []\\n name_models = []\\n for ts, name_model in product(y_hat_df.groupby('unique_id'), self.models.keys()):\\n uid, df = ts\\n h = len(df)\\n model = self.fitted_models_.loc[(uid, name_model)]\\n model = model.item()\\n y_hat = dask.delayed(model.predict)(h)\\n forecasts.append(y_hat)\\n uids.append(np.repeat(uid, h))\\n dss.append(df['ds'])\\n name_models.append(np.repeat(name_model, h))\\n\\n forecasts = dask.delayed(forecasts).compute(scheduler=self.scheduler)\\n forecasts = zip(uids, dss, name_models, forecasts)\\n\\n forecasts_df = []\\n for uid, ds, name_model, forecast in forecasts:\\n dict_df = {'unique_id': uid,\\n 'ds': ds,\\n 'model': name_model,\\n 'forecast': forecast}\\n df = dask.delayed(pd.DataFrame.from_dict)(dict_df)\\n forecasts_df.append(df)\\n\\n forecasts = dask.delayed(forecasts_df).compute()\\n forecasts = pd.concat(forecasts)\\n\\n forecasts = forecasts.set_index(['unique_id', 'ds', 'model']).unstack()\\n forecasts = forecasts.droplevel(0, 1).reset_index()\\n forecasts.columns.name = ''\\n\\n\\n return forecasts\",\n \"def predict(self, df):\\n results = [] \\n _ds = pdfds.DataFrameDataset(df, self.fields) \\n _iter = BucketIterator(_ds, batch_size=16, sort_key=lambda x: len(x.text),\\n train=False, sort=True, sort_within_batch=True)\\n self.odel.eval()\\n with torch.no_grad():\\n for (labels, text), _ in _iter:\\n labels = labels.type(torch.LongTensor)\\n text = text.type(torch.LongTensor)\\n _, output = self.model(text, labels)\\n sm = torch.nn.Softmax(dim=1)\\n results.extend( sm(output).tolist()[1] )\\n return results\",\n \"def fit_and_predict(df, \\n outcome: str='deaths', \\n method: str='exponential', \\n mode: str='predict_future', \\n target_day: np.ndarray=np.array([1]),\\n output_key: str=None,\\n demographic_vars=[],\\n verbose: bool=False):\\n \\n assert mode == 'predict_future' or mode == 'eval_mode', 'unknown mode'\\n if output_key is None:\\n output_key = f'predicted_{outcome}_{method}_{target_day[-1]}'\\n if len(demographic_vars) > 0:\\n output_key += '_demographics'\\n if method == 'AR':\\n print('currently deprecated')\\n raise NotImplementedError\\n loss, model, best_window = naive_autoreg_baselines.train_and_evaluate_model(train_df,test_df)\\n return naive_autoreg_baselines.make_predictions(test_df,model,best_window)\\n \\n elif method == 'exponential':\\n preds = exponential_modeling.exponential_fit(df[outcome].values, \\n mode=mode, \\n target_day=target_day)\\n \\n \\n df[output_key] = preds\\n #del test_df['predicted_deaths_exponential']\\n\\n return df\\n \\n elif method == 'linear':\\n preds = exponential_modeling.linear_fit(df[outcome].values, \\n mode=mode, \\n target_day=target_day)\\n \\n \\n df[output_key] = preds\\n #del test_df['predicted_deaths_exponential']\\n\\n return df \\n \\n elif method == 'shared_exponential':\\n # Fit a poisson GLM with shared parameters across counties. Input to the poisson GLM is demographic_vars and log(previous_days_deaths+1)\\n cur_day_predictions = exponential_modeling.fit_and_predict_shared_exponential(df,mode,outcome=outcome,demographic_vars=demographic_vars,target_day=target_day)\\n #if len(demographic_vars) > 0:\\n # output_key += '_demographics'\\n # import IPython\\n # IPython.embed()\\n df[output_key] = cur_day_predictions\\n return df\\n \\n elif method == 'ensemble':\\n print('please use fit_and_predict_ensemble instead')\\n \\n \\n else:\\n print('Unknown method')\\n raise ValueError\",\n \"def model_predict(df, model, encoders):\\n\\n data_enc = process_data(df, encoders, process_target=False)\\n\\n headers = ['probability']\\n predictions = pd.DataFrame(model.predict(data_enc), columns=headers)\\n\\n return predictions\",\n \"def make_predictions(data_dict):\\n new_data = pd.DataFrame(columns=[\\\"location\\\", \\\"date\\\",\\n \\\"logistic_prediction\\\",\\n \\\"logistic_logarithmic_prediction\\\",\\n \\\"logistic_polynomial_prediction\\\"])\\n for country in data_dict.keys():\\n data = data_dict[country][\\\"data\\\"]\\n x_data = np.array(list(range(500)))\\n model1 = LogisticRegressionModel(data_dict[country][\\\"data\\\"])\\n y_pred1 = model1.predict(x_data.reshape(-1, 1))\\n model2 = LogisticLogarithmicRegressionModel(data)\\n y_pred2 = model2.predict(x_data.reshape(-1, 1))\\n model3 = LogisticPolynomialRegressionModel(data)\\n y_pred3 = model3.predict(x_data.reshape(-1, 1))\\n model4 = PolynomialRegressionModel(data)\\n y_pred4 = model4.predict(x_data.reshape(-1, 1))\\n data = np.swapaxes(np.array([x_data, y_pred1, y_pred2,\\n y_pred3, y_pred4]), 0, 1)\\n tmp_data = pd.DataFrame(data, columns=[\\\"date\\\",\\n \\\"logistic_prediction\\\",\\n \\\"logistic_logarithmic_prediction\\\",\\n \\\"logistic_polynomial_prediction\\\",\\n \\\"polynomial_prediction\\\"])\\n tmp_data[\\\"location\\\"] = country\\n new_data = new_data.append(tmp_data)\\n return new_data\",\n \"def prophet_train(data): \\n \\n model = Prophet(interval_width=.95, changepoint_prior_scale=6, yearly_seasonality=True, \\n seasonality_prior_scale=1, weekly_seasonality=False, daily_seasonality=False)\\n model.add_seasonality(name='monthly', period=120, fourier_order=4)\\n \\n model.fit(data)\\n \\n future_dates = model.make_future_dataframe(periods=12, freq='MS')\\n forecast = model.predict(future_dates)\\n forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\\n results = forecast[forecast['ds']>='2018-01-01']\\n return(results)\",\n \"def predict(model, up_to_days=DEFAULT_UP_TO_DAYS, start_time=None, end_time=None, plot=False):\\n if start_time is None and end_time is None:\\n df_future = model.make_future_dataframe(periods=24 * up_to_days, freq='H')\\n start_future = df_future.iloc[-1, :]['ds'] - datetime.timedelta(days=up_to_days)\\n df_future = df_future[df_future['ds'] >= start_future]\\n elif start_time is not None and end_time is not None:\\n indices = pd.date_range(start_time, end_time, freq='H')\\n assert len(indices) > 0, \\\"Indices should not be empty\\\"\\n df_future = pd.DataFrame(columns=['ds', 'y'])\\n df_future['ds'] = indices\\n else:\\n raise ValueError(\\\"Either up_to_days or start_time and end_time must be set appropriately.\\\")\\n df_forecast = model.predict(df_future)\\n if plot:\\n model.plot(df_forecast)\\n model.plot_components(df_forecast)\\n return df_forecast\",\n \"def predict(\\n self, horizon: int, predict_fn: Callable = predictions_flow, **predict_fn_kwargs\\n ) -> dd.DataFrame:\\n model_future = self.client.scatter(self.model.model_, broadcast=True)\\n predictions_futures = self.client.map(\\n predict_fn,\\n self.ts,\\n model=model_future,\\n horizon=horizon,\\n **predict_fn_kwargs,\\n )\\n meta = self.client.submit(lambda x: x.head(), predictions_futures[0]).result()\\n return dd.from_delayed(\\n predictions_futures, meta=meta, divisions=self.data_divisions\\n )\",\n \"def forecast_temperature(y, results, end_date, **kwargs):\\n \\n # Forecast\\n forecast = results.get_prediction(start=(y.index.max() + timedelta(1)),\\n end=end_date,\\n **kwargs)\\n y_forecast = forecast.predicted_mean\\n forecast_ci = forecast.conf_int()\\n\\n # Graph forecast\\n ax = y.plot(label='observed')\\n y_forecast.plot(ax=ax, label='forecasted', alpha=.7, figsize=(15, 8))\\n ax.fill_between(forecast_ci.index,\\n forecast_ci.iloc[:, 0],\\n forecast_ci.iloc[:, 1], color='k', alpha=.2)\\n plt.title('Forecasted Average Monthly Temperature')\\n ax.set_xlabel('Date')\\n ax.set_ylabel('Temperature')\\n plt.legend()\\n plt.show()\\n \\n # Generate forecast table\\n forecast_table = forecast.summary_frame()\\n forecast_table.index.rename('Date',inplace=True)\\n forecast_table = round(forecast_table, 2)\\n \\n return forecast_table\",\n \"def predict(self, n: int) -> TimeSeries:\\n\\n if (not self._fit_called):\\n raise_log(Exception('fit() must be called before predict()'), logger)\",\n \"def _predict(self, X: TimeSeriesInstances, y=None) -> np.ndarray:\\n raise NotImplementedError\",\n \"def predict(self):\\n for column in self.data_to_predict.columns:\\n if column not in list(self.selected_features_):\\n self.data_to_predict.drop(column, axis=1, inplace=True)\\n for column in list(self.selected_features_):\\n if column not in self.data_to_predict.columns:\\n self.data_to_predict.loc[:, column] = 0\\n self.predictions = self.model.predict(\\n self.data_to_predict[self.selected_features_])\",\n \"def predict(self, data, current_timestamp, number_of_iterations=1):\\n\\n if self._topology is None:\\n logger.warning('Not ready for prediction - safer to run train first')\\n logger.info('Cromulon Oracle prediction on {}.'.format(current_timestamp))\\n\\n predict_y_list = []\\n for i in range(number_of_iterations):\\n predict_y, symbols, target_timestamps = self.predict_classification(data, current_timestamp)\\n predict_y_list.append(predict_y)\\n\\n predict_y_stack = np.stack(predict_y_list)\\n average_predict_y = np.mean(predict_y_stack, axis=0)\\n\\n means, conf_low, conf_high = self._data_transformation.inverse_transform_multi_predict_y(average_predict_y, symbols)\\n self.log_validity_of_predictions(means, conf_low, conf_high)\\n\\n means_pd = pd.DataFrame(data=means, columns=symbols, index=target_timestamps)\\n conf_low_pd = pd.DataFrame(data=conf_low, columns=symbols, index=target_timestamps)\\n conf_high_pd = pd.DataFrame(data=conf_high, columns=symbols, index=target_timestamps)\\n\\n means_pd, conf_low_pd, conf_high_pd = self.filter_predictions(means_pd, conf_low_pd, conf_high_pd)\\n\\n return OraclePrediction(means_pd, conf_low_pd, conf_high_pd, current_timestamp)\",\n \"def predict_AR(test, history, window, coeff):\\n predictions = list()\\n for t in range(len(test)):\\n length = len(history)\\n lag = [history[i] for i in range(length - window, length)] # keep the last window values\\n yhat = coeff[0]\\n for d in range(window): # make the prediction for the autoregressive model\\n yhat += coeff[d + 1] * lag[window - d - 1]\\n obs = test[t] # store the true value of the test set to use for future predictions\\n predictions.append(yhat)\\n history.append(obs) # and update the history\\n return pd.Series(predictions)\",\n \"def predict(self, model_collection, date, test_mode=False, verbose=False):\\n if verbose:\\n print(date)\\n #get predictions, metrics\\n model_predictions = model_collection.predict(date, verbose)\\n if(test_mode):\\n model_collection.get_voter_metrics()\\n \\n if verbose:\\n print(\\\"Model Predictions: \\\", model_predictions)\\n \\n predictions = {}\\n for i,model in enumerate(model_collection.models):\\n if(test_mode):\\n predictions[model.title] = (model_predictions[i], model.metrics['acc'], model.metrics['balance'], model.metrics['False Positive Rate'])\\n else:\\n predictions[model.title] = (model_predictions[i], model.metrics['acc'])\\n\\n \\n if verbose:\\n print(\\\"Updated Predictions in Voter\\\", predictions)\\n \\n if self.voting_type == \\\"accuracy\\\":\\n\\n sum_voting = 0\\n for pred in predictions.items():\\n pred = pred[1]\\n sum_voting += pred[0]*pred[1]\\n sum_voting += pred[0]*pred[1]\\n sum_voting = sum_voting/len(predictions)\\n if sum_voting > 0.4:\\n prediction = 1\\n else:\\n prediction = 0\\n\\n elif self.voting_type == 'Penrose':\\n total_votes =0\\n votes_toPass= 0\\n votes_toReject =0\\n for title, metrics in predictions.items():\\n pred = metrics[0]\\n acc = metrics[1] \\n balance = metrics[2] \\n FPR = metrics[3] \\n\\n if pred ==0 :\\n vote_to = 'reject'\\n else:\\n vote_to = 'Pass'\\n\\n multiplier = (acc)* math.sqrt(balance*FPR)\\n votes = int(multiplier)\\n\\n if vote_to == 'reject':\\n votes_toReject += votes\\n elif vote_to == 'Pass':\\n votes_toPass += votes\\n total_votes += votes\\n \\n ratio = votes_toPass/total_votes\\n if ratio > .5:\\n prediction = 1\\n else:\\n prediction = 0\\n\\n else:\\n prediction = 'you have not selected a valid voting method'\\n \\n if verbose:\\n print(\\\"Voting Output \\\", prediction, \\\" for model with accuracy \\\", model_collection.accuracy)\\n \\n return (prediction, model_collection.accuracy)\",\n \"def Empirical_Bayes_Estimator(df_train, df_test):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # define the prior weights (simple average, with intercept equal zero)\\n beta_0 = np.append(np.array(0), np.full(K, fill_value=1/K, dtype=float))\\n\\n # design matrix (intercept + individual forecasts)\\n F = df_train.iloc[:, 1:].values\\n F = np.insert(F, 0, 1, axis=1)\\n\\n # define y (observed values)\\n y = df_train.iloc[:, 0].values\\n\\n # OLS weights\\n beta_hat = np.dot(np.linalg.inv(np.dot(np.transpose(F), F)),\\n np.dot(np.transpose(F), y))\\n\\n # sigma\\n sigma_hat_sq = np.dot(\\n np.transpose(y - np.dot(F, beta_hat)),\\n y - np.dot(F, beta_hat)\\n ) / T\\n\\n # tau\\n num = np.dot(np.transpose(beta_hat - beta_0), beta_hat - beta_0)\\n denum = np.trace(np.linalg.inv(np.dot(np.transpose(F), F)))\\n tau_hat_sq = num / denum - sigma_hat_sq\\n\\n # combining weights\\n shrinkage = 1 - sigma_hat_sq / (sigma_hat_sq + tau_hat_sq)\\n beta_1_hat = beta_0 + shrinkage*(beta_hat - beta_0)\\n\\n # predictions\\n df_pred = pd.DataFrame(\\n {\\\"Empirical Bayes Estimator\\\":\\n beta_1_hat[0] + np.dot(df_test, beta_1_hat[1:])},\\n index=df_test.index)\\n\\n return df_pred\",\n \"def predict(model, dataframe, features, target_name='score', inplace=True):\\n if inplace is False:\\n dataframe = dataframe.copy()\\n dataframe[target_name] = model.predict(dataframe[features])\\n return dataframe\",\n \"def forecast(self, model, idx_rows=None, replace=True):\\n if idx_rows is None:\\n idx_rows = range(self.X.shape[0])\\n\\n forecastedY = model.predict(self.X[idx_rows, :])\\n if forecastedY.ndim == 1:\\n forecastedY = forecastedY[:, None]\\n\\n # ravel forecasts and, if replace=True, input new values to self.forecasts vector\\n idx_frc = self.add_forecasts(forecastedY, idx_rows, replace)\\n\\n return forecastedY, idx_frc\",\n \"def generate_hourly_forecast(num_pred_hours, consumption, model, scaler, lag):\\r\\n # allocate prediction frame\\r\\n preds_scaled = np.zeros(num_pred_hours)\\r\\n \\r\\n # initial X is last lag values from the cold start\\r\\n X = scaler.transform(consumption.values.reshape(-1, 1))[-lag:]\\r\\n \\r\\n # forecast\\r\\n for i in range(num_pred_hours):\\r\\n # predict scaled value for next time step\\r\\n yhat = model1.predict(X.reshape(1, 1, lag), batch_size=1)[0][0]\\r\\n preds_scaled[i] = yhat\\r\\n \\r\\n # update X to be latest data plus prediction\\r\\n X = pd.Series(X.ravel()).shift(-1).fillna(yhat).values\\r\\n\\r\\n # revert scale back to original range\\r\\n hourly_preds = scaler.inverse_transform(preds_scaled.reshape(-1, 1)).ravel()\\r\\n return hourly_preds\",\n \"def makePredictions(self, data, batchSize=1):\\n ds = tf.data.Dataset.from_tensor_slices(data)\\n ds = ds.window(self.lookBack, shift=self.forecast, drop_remainder=True)\\n ds = ds.flat_map(lambda w: w.batch(self.lookBack))\\n ds = ds.batch(batchSize).prefetch(1)\\n prediction = self.model.predict(ds)\\n return prediction\",\n \"def prediction_data(median_split,mean_split,std_split,degrees_split,weight_split,export_file):\\n DATA_TEST_PATH = '../data/test.csv' # Download train data and supply path here \\n print('\\\\nIMPORTING TESTING DATA :',end=\\\" \\\")\\n y_test, tX_test, ids_test = load_csv_data(DATA_TEST_PATH)\\n print('DONE')\\n \\n #5.a. Splitting the testing data\\n print('SPLITTING TESTING DATA :',end=\\\" \\\")\\n y_test_split,tx_test_split,id_test_split = split_dataset(y_test,tX_test,ids_test) \\n print('DONE') \\n #5.b. prediction on each model\\n y_pred = list()\\n \\n for split,(y_test_s,tx_test_s,id_test_s) in enumerate(zip(y_test_split,tx_test_split,id_test_split)): \\n print('PREDICTION FOR TESTING DATA SPLIT NUMBER',split)\\n \\n #Formatting to the correct datatype\\n y_test_s = np.squeeze(y_test_s)\\n tx_test_s = np.squeeze(tx_test_s)\\n id_test_s = np.squeeze(id_test_s)\\n print('Size of the vectors',y_test_s.shape,tx_test_s.shape) \\n #Formatting the data themselves\\n print('Counting NaN',end='. ')\\n tx_test_s = count_NaN(tx_test_s)\\n print('Sanitizing',end = ' . ')\\n tx_test_s,median_vec = sanitize_NaN(tx_test_s,median_split[split])\\n print('Standardizing',end = ' .')\\n tx_test_s,mean_te,std_te = standardize(tx_test_s,mean_split[split],std_split[split])\\n print('Building polynomial basis') \\n tx_test_s = build_poly(tx_test_s, degrees_split[split])\\n \\n #Prediction\\n y_pred.append(predict_labels(np.array(weight_split[split]), np.array(tx_test_s))) \\n \\n print('MERGING TESTING DATA',end=\\\"\\\")\\n y_pred_merged, ids_merged = merge_dataset(y_pred,id_test_split)\\n print('DONE')\\n \\n OUTPUT_PATH = 'results/output_sanitized_normalization_'+export_file+'.csv' \\n print('EXPORTING TESTING DATA WITH PREDICTIONS :',end=\\\" \\\")\\n \\n create_csv_submission(ids_merged, y_pred_merged, OUTPUT_PATH)\\n print('DONE')\",\n \"def predict(\\n prophet_model: Prophet,\\n df: Optional[pd.DataFrame] = None,\\n vectorized: bool = False,\\n) -> pd.DataFrame:\\n if prophet_model.history is None:\\n raise Exception(\\\"Model has not been fit.\\\")\\n\\n if df is None:\\n df = prophet_model.history.copy()\\n else:\\n if df.shape[0] == 0:\\n raise ValueError(\\\"Dataframe has no rows.\\\")\\n df = prophet_model.setup_dataframe(df)\\n\\n df[\\\"trend\\\"] = prophet_model.predict_trend(df)\\n # TODO: the seasoanl part will be computed twice when we need to compute uncertainty. Remove this part if possible.\\n seasonal_components = prophet_model.predict_seasonal_components(df)\\n\\n if prophet_model.uncertainty_samples:\\n intervals = predict_uncertainty(prophet_model, df, vectorized)\\n\\n else:\\n intervals = None\\n\\n # Drop columns except ds, cap, floor, and trend\\n cols = [\\\"ds\\\", \\\"trend\\\"]\\n if \\\"cap\\\" in df:\\n cols.append(\\\"cap\\\")\\n if prophet_model.logistic_floor:\\n cols.append(\\\"floor\\\")\\n # Add in forecast components\\n df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)\\n df2[\\\"yhat\\\"] = (\\n df2[\\\"trend\\\"] * (1 + df2[\\\"multiplicative_terms\\\"]) + df2[\\\"additive_terms\\\"]\\n )\\n return df2\",\n \"def date_prediction(config):\\n if config['functionality'] == 'best_flights':\\n departure_flight_date = date(config['departure_flight']['departure_date'][0],\\n config['departure_flight']['departure_date'][1],\\n config['departure_flight']['departure_date'][2])\\n return_flight_date = date(config['return_flight']['departure_date'][0],\\n config['return_flight']['departure_date'][1],\\n config['return_flight']['departure_date'][2])\\n div = config['prediction_period_days'] / 7\\n dates_search = []\\n for x in range(0, div + 1):\\n dates_search.append(\\n [(departure_flight_date + datetime.timedelta(days=x * 7)),\\n (return_flight_date + datetime.timedelta(days=x * 7))])\\n for i in dates_search:\\n i[0] = str(i[0])\\n year, month, day = i[0].split(\\\"-\\\")\\n i[0] = \\\"%s/%s/%s\\\" % (day, month, year)\\n i[1] = str(i[1])\\n year, month, day = i[1].split(\\\"-\\\")\\n i[1] = \\\"%s/%s/%s\\\" % (day, month, year)\\n return dates_search\\n elif config['functionality'] == 'flight_trends':\\n departure_flight_date = date(\\n config['departure_flight']['departure_date'][0],\\n config['departure_flight']['departure_date'][1],\\n config['departure_flight']['departure_date'][2])\\n return_flight_date = date(config['return_flight']['departure_date'][0],\\n config['return_flight']['departure_date'][1],\\n config['return_flight']['departure_date'][2])\\n dates_search = []\\n for x in range(0, config['prediction_period_days']):\\n dates_search.append(\\n [(departure_flight_date + datetime.timedelta(days=x)),\\n (return_flight_date + datetime.timedelta(days=x))])\\n for i in dates_search:\\n i[0] = str(i[0])\\n year, month, day = i[0].split(\\\"-\\\")\\n i[0] = \\\"%s/%s/%s\\\" % (day, month, year)\\n i[1] = str(i[1])\\n year, month, day = i[1].split(\\\"-\\\")\\n i[1] = \\\"%s/%s/%s\\\" % (day, month, year)\\n return dates_search\",\n \"def sts_predict_canteen_values(full_df, prediction_df, future=True):\\n\\n df = full_df.copy()\\n df = df.filter([\\\"Canteen\\\"])\\n test_period = prediction_df.shape[0]\\n\\n # The days between last row in dataset and today needs to be calculated\\n if future is True:\\n # Finding days between end date of dataset and today\\n date_today = datetime.now()\\n end_date = df.index[-1]\\n future_test_period = test_period + (date_today - end_date).days - 1\\n\\n # The whole dataframe is now the train set\\n train = df\\n # Creating a new dataframe containing all dates between end date and today + future days from prediction_df\\n date_df = pd.DataFrame()\\n date_df[\\\"date\\\"] = pd.date_range(\\n (end_date + timedelta(1)).date(),\\n periods=future_test_period,\\n freq=\\\"D\\\",\\n )\\n date_df.index = pd.to_datetime(date_df.pop(\\\"date\\\"))\\n # Setting the new df as test set\\n test = date_df\\n else:\\n # If future = False, the test period is the last part of the full_df, train is everything up until this point\\n train = df.iloc[:-test_period]\\n test = df.iloc[-test_period:]\\n\\n # Make prediction\\n _, predictions = prediction(train, test)\\n return predictions.iloc[-test_period:]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79838103","0.7393525","0.725368","0.69809437","0.6334739","0.63147026","0.6194552","0.6134306","0.6121274","0.6016633","0.601663","0.5980682","0.5806225","0.57996064","0.5768583","0.57633406","0.5728102","0.5712658","0.5658473","0.5626327","0.56168824","0.56023175","0.5600497","0.559221","0.5587459","0.55743897","0.556775","0.55274415","0.5521237","0.5512537","0.5510892","0.54862833","0.54602796","0.5449051","0.543622","0.5431298","0.54171634","0.537917","0.5365232","0.5360977","0.535331","0.5342065","0.53352195","0.5333091","0.53271246","0.5322252","0.53144675","0.5307835","0.53073627","0.53011763","0.5295313","0.528811","0.5285002","0.5284029","0.5276031","0.52606523","0.5259353","0.5250423","0.5246261","0.5237599","0.5233108","0.5226851","0.5226136","0.5223352","0.5222013","0.5211416","0.5207619","0.5205695","0.52028924","0.5200676","0.5198946","0.5198687","0.5197614","0.51925707","0.519066","0.5187147","0.5184957","0.5179825","0.51794016","0.5176227","0.5175767","0.517535","0.51726526","0.5159295","0.5158304","0.5156674","0.5153016","0.5152473","0.51520157","0.51513267","0.5149923","0.5148596","0.5143445","0.5122493","0.51182115","0.51159394","0.51092947","0.5108481","0.5108009","0.5105777"],"string":"[\n \"0.79838103\",\n \"0.7393525\",\n \"0.725368\",\n \"0.69809437\",\n \"0.6334739\",\n \"0.63147026\",\n \"0.6194552\",\n \"0.6134306\",\n \"0.6121274\",\n \"0.6016633\",\n \"0.601663\",\n \"0.5980682\",\n \"0.5806225\",\n \"0.57996064\",\n \"0.5768583\",\n \"0.57633406\",\n \"0.5728102\",\n \"0.5712658\",\n \"0.5658473\",\n \"0.5626327\",\n \"0.56168824\",\n \"0.56023175\",\n \"0.5600497\",\n \"0.559221\",\n \"0.5587459\",\n \"0.55743897\",\n \"0.556775\",\n \"0.55274415\",\n \"0.5521237\",\n \"0.5512537\",\n \"0.5510892\",\n \"0.54862833\",\n \"0.54602796\",\n \"0.5449051\",\n \"0.543622\",\n \"0.5431298\",\n \"0.54171634\",\n \"0.537917\",\n \"0.5365232\",\n \"0.5360977\",\n \"0.535331\",\n \"0.5342065\",\n \"0.53352195\",\n \"0.5333091\",\n \"0.53271246\",\n \"0.5322252\",\n \"0.53144675\",\n \"0.5307835\",\n \"0.53073627\",\n \"0.53011763\",\n \"0.5295313\",\n \"0.528811\",\n \"0.5285002\",\n \"0.5284029\",\n \"0.5276031\",\n \"0.52606523\",\n \"0.5259353\",\n \"0.5250423\",\n \"0.5246261\",\n \"0.5237599\",\n \"0.5233108\",\n \"0.5226851\",\n \"0.5226136\",\n \"0.5223352\",\n \"0.5222013\",\n \"0.5211416\",\n \"0.5207619\",\n \"0.5205695\",\n \"0.52028924\",\n \"0.5200676\",\n \"0.5198946\",\n \"0.5198687\",\n \"0.5197614\",\n \"0.51925707\",\n \"0.519066\",\n \"0.5187147\",\n \"0.5184957\",\n \"0.5179825\",\n \"0.51794016\",\n \"0.5176227\",\n \"0.5175767\",\n \"0.517535\",\n \"0.51726526\",\n \"0.5159295\",\n \"0.5158304\",\n \"0.5156674\",\n \"0.5153016\",\n \"0.5152473\",\n \"0.51520157\",\n \"0.51513267\",\n \"0.5149923\",\n \"0.5148596\",\n \"0.5143445\",\n \"0.5122493\",\n \"0.51182115\",\n \"0.51159394\",\n \"0.51092947\",\n \"0.5108481\",\n \"0.5108009\",\n \"0.5105777\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6652185"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":79780,"cells":{"query":{"kind":"string","value":"Takes a dataframe of processed eviction data as well as a set amount of lagged variables about future datapoints and how many months ahead to include in the prediction."},"document":{"kind":"string","value":"def arimax_by_month_train_test(model_data,future_data,months_ahead):\n transformed_df = arima_by_zip_data_transform(model_data)\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\n\n y_train = transformed_df\n\n\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]\n\n now = datetime.datetime.now()\n this_month = pd.to_datetime(str(now.year)+ '-'+ str(now.month) + '-'+str(1))\n y_test=y_test[(y_test.Month_Year>this_month)&(y_test.Month_Year<(this_month+pd.offsets.MonthBegin(months_ahead+1)))].sort_values('Month_Year')\n months_list = np.asarray(y_test.Month_Year)\n\n y_test = y_test.set_index(['Month_Year'],inplace=False)\n\n\n for i,row in y_train.Eviction_Notice.iteritems():\n if row>(y_train.Eviction_Notice.mean()+ y_train.Eviction_Notice.std()*3):\n y_train.Eviction_Notice.iloc[i]=y_train.Eviction_Notice.mean()\n\n y_train = y_train[['Month_Year','Eviction_Notice','CASANF0URN']].set_index(['Month_Year'],inplace=False)\n\n return y_train, y_test,months_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_predictions(year, month):\n \n start_date = str(year)+\"-\"+str(month)+\"-01\"\n end_date = str(year)+\"-\"+str(month)+\"-\"+str(monthrange(year, month)[1])\n\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\"%Y-%m-%d\").tolist()\n\n # predictfunction \n # do predictions\n pred_arr = []\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\n \n try:\n predictions = load_predictions(file_name)\n predictions = predictions.round()\n except:\n print(\"An exception occurred\")\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\n \n \n for index,row in predictions.iterrows():\n \n pred_mail = 0\n pred_counter = 0\n pred_tel = 0\n \n # check predictions dataframe for 'Datum'\n if 'Datum' in predictions.columns:\n date = row['Datum']\n else:\n break;\n\n # check predictions dataframe for 'Mail'\n if 'Mail' in predictions.columns:\n pred_mail = row['Mail']\n\n # check predictions dataframe for 'Schalter'\n if 'Schalter' in predictions.columns:\n pred_counter = row['Schalter']\n\n # check predictions dataframe for 'Tel'\n if 'Tel' in predictions.columns:\n pred_tel = row['Tel']\n \n \n pred_dict = {'date': date, \n 'predictions':{'mail' : pred_mail, \n 'tel' : pred_tel, \n 'counter' : pred_counter\n }\n }\n\n pred_arr.append(pred_dict)\n\n print(pred_arr) \n \n return pred_arr","def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\n columns=['month_year', 'predicted_evictions'])\n\n now = datetime.datetime.now()\n\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\n model.fit()\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\n\n predicted = y_hat.Eviction_Notice.values.tolist()\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\n temp_df['month_year']=months_list\n\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\n predictions_df = predictions_df[1:]\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\n\n return predictions_df, y_hat","def top_down_forecast(original_df,predictions_by_month,months_ahead):\n\n group_by_zip_df = top_down_forecast_data_processing(original_df,predictions_by_month)\n\n zip_perc_df= pd.DataFrame(np.random.randn(1, 4), columns=['zip_predicted','zip_code','perc_of_month','month_year'])\n\n for month in range(months_ahead):\n array_of_predictions = np.asarray([predictions_by_month.iloc[month]['predicted_evictions']]*group_by_zip_df.shape[0])\n zip_predicted = np.asarray(group_by_zip_df.perc_of_month*array_of_predictions)\n temp_df = pd.DataFrame({'total_evictions':array_of_predictions,'zip_predicted':zip_predicted,'zip_code':np.asarray(group_by_zip_df.Address_Zipcode), 'perc_of_month':np.asarray(group_by_zip_df.perc_of_month)})\n temp_df['month_year']=predictions_by_month.iloc[month]['month_year']\n zip_perc_df = zip_perc_df.append(temp_df,ignore_index=True)\n\n zip_perc_df = zip_perc_df[1:]\n zip_perc_df['month_year']=pd.to_datetime(zip_perc_df['month_year'])\n\n return zip_perc_df","def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\n df.dropna(inplace=True)\n ## day and month\n df['Date'] = pd.to_datetime(df['Date'])\n df['Month'] = df['Date'].dt.month\n df['dayowk'] = df['Date'].dt.dayofweek\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\n \n ##Previos n-day pct_changes\n df['1day_pct'] = df['Adj Close'].pct_change()\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\n \n ##Cumulative sum of 1day_pct\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\n \n ##EWMA of 7, 50 and 200 days\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\n ## Golden Cross vs Death Cross etc.\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n \n ##RSI and MACD\n df = RSI(df,14)\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\n \n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\n \n ##Classification target\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\n \n ## IS the stock Overbought or Oversold based on RSI?\n df['RSI_overbought'] = df['RSI']>70\n df['RSI_oversold'] = df['RSI']<30\n \n \n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\n# df = df.dropna()\n \n #df = df.reset_index(drop=True)\n \n ## Calculating how large the previos hot and cold streaks were\n f = 0\n df['prev_hot_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==1:\n f += 1\n if df['target'][i+1] ==0:\n df['prev_hot_streak'][i+1] = f\n f = 0\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_hot_streak'][i]==0:\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\n \n \n df['prev_cold_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==0:\n f += 1\n if df['target'][i+1] ==1:\n df['prev_cold_streak'][i+1] = f\n f = 0\n\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_cold_streak'][i]==0:\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\n \n ## Calculating current hot and cold streaks\n df['current_hot_streak'] = np.zeros(df.shape[0])\n df['current_cold_streak'] = np.zeros(df.shape[0])\n fhot=0\n fcold=0\n for i in range(df.shape[0]):\n if df['target'][i]==1:\n fhot += 1\n fcold = 0\n df['current_hot_streak'][i] = fhot\n elif df['target'][i]==0:\n fcold += 1\n fhot = 0\n df['current_cold_streak'][i] = fcold\n \n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\n \n ## Combinations of previos streaks\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\n \n ##Calculating days since max\n current_max = df['Adj Close'][0]\n df['days_from_max'] = np.zeros(df.shape[0])\n df['pct_from_max'] = np.zeros(df.shape[0])\n #print('blah')\n for i in range(1,df.shape[0]):\n if df['Adj Close'][i] > current_max:\n current_max = df['Adj Close'][i]\n # print(current_max)\n else:\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\n #print(df['days_from_max'][i])\n \n \n \n df.dropna(inplace=True)\n df = df.reset_index(drop=True)\n return df","def engineer_features(dataframe, holiday_dates, columns, time_lags=24, \n steps_ahead=1, drop_nan_rows=True):\n \n # Make a copy of the original dataframe\n data = dataframe[columns].copy()\n \n # Features engineering\n for col in data.columns:\n for i in range(1, time_lags+1):\n # Shift data by lag of 1 to time_lags (default: 24) hours\n data[col+'_{:d}h'.format(i)] = data[col].shift(periods=i) # time-lag\n data[col+'_diff'] = data[col].diff() # first-difference\n data[col+'_week'] = data[col].shift(periods=24*7) # previous week\n \n # Hour-of-day indicators with cyclical transform\n dayhour_ind = data.index.hour\n data['hr_sin'] = np.sin(dayhour_ind*(2.*np.pi/24))\n data['hr_cos'] = np.cos(dayhour_ind*(2.*np.pi/24))\n \n # Day-of-week indicators with cyclical transform\n weekday_ind = data.index.weekday\n data['week_sin'] = np.sin(weekday_ind*(2.*np.pi/7))\n data['week_cos'] = np.cos(weekday_ind*(2.*np.pi/7))\n\n # Weekend as a binary indicator\n data['weekend'] = np.asarray([0 if ind <= 4 else 1 for ind in weekday_ind])\n\n # Month indicators with cyclical transform\n month_ind = data.index.month\n data['mnth_sin'] = np.sin((month_ind-1)*(2.*np.pi/12))\n data['mnth_cos'] = np.cos((month_ind-1)*(2.*np.pi/12))\n \n # Holidays as a binary indicator\n data['holidays'] = 0\n for holiday, date in holiday_dates.items():\n if date[1] is None:\n # Single day\n data.loc[date[0], 'holidays'] = 1\n else:\n # Date range\n data.loc[date[0]:date[1], 'holidays'] = 1\n \n # Forecast horizont\n if steps_ahead == 1:\n # Single-step forecasting\n data['Load+0h'] = data['Load'].values\n else:\n # Multi-step forecasting\n for i in range(steps_ahead):\n data['Load'+'+{:d}h'.format(i)] = data['Load'].shift(-i)\n del data['Load']\n \n if drop_nan_rows:\n # Drop rows with NaN values\n data.dropna(inplace=True)\n \n return data","def predictions_for_df(df, inferrer):\n working_df = df.copy()\n working_df['predictions'] = inferrer.get_activations(\n working_df.sequence.values).tolist()\n return working_df","def top_down_forecast_data_processing(original_df,predictions_by_month):\n transformed_df=original_df.groupby('Month_Year').sum().reset_index()\\\n [['Month_Year','Eviction_Notice']]\n\n percentage_of_month_df = pd.merge(original_df[['Month_Year','Address_Zipcode',\\\n 'Eviction_Notice']],transformed_df,how = 'left',left_on='Month_Year',\\\n right_on='Month_Year',suffixes=('','_for_month'))\n\n percentage_of_month_df.dropna(inplace=True)\n\n percentage_of_month_df['perc_of_month']=\\\n percentage_of_month_df['Eviction_Notice']/percentage_of_month_df['Eviction_Notice_for_month']\n\n group_by_zip_df = percentage_of_month_df.groupby('Address_Zipcode').mean().reset_index()[['Address_Zipcode','perc_of_month']]\n\n return group_by_zip_df","def feature_engineer_ts(self, month=12):\n st_data_dt = self.get_st_data_dt()\n end_data_dt = self.get_end_data_dt()\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\n population = self.get_population()\n is_raw_partition = self.get_is_raw_partition()\n# Lag 2 months\n all_data = []\n# join past is_raw columns\n for d in date_list:\n \n population_partition = population[population['ft_data_dt'] == d] \n old_date = d - relativedelta(months=month)\n y = old_date.year\n m = old_date.month\n day = calendar.monthrange(y, m)[1]\n old_date = date(y, m, day)\n old_date = max(old_date, st_data_dt)\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\n date_list_join.reverse()\n for index, date_join in enumerate(date_list_join):\n if date_join.strftime(\"%Y-%m-%d\") not in is_raw_partition.keys():\n continue\n \n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\"%Y-%m-%d\")]\n \n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\n new_col = [c+'_'+str(index+1) for c in rename_col]\n name_dict = dict(list(zip(rename_col, new_col)))\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\n all_data.append(population_partition)\n ts_df = pd.concat(all_data)\n threshold_null = len(ts_df.columns) - 4\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\n \n def sum_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_sum_'+str(duration)+'mth'\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def mean_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_avg_'+str(duration)+'mth'\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def std_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_std_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def med_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_med_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def min_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_min_'+str(duration)+'mth'\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def max_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_max_'+str(duration)+'mth'\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q1_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q1_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q3_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q3_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def last_ts(self, df, feature):\n ft_name = feature+ '_last'\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\n return tmp_df\n \n ts_duration = [1, 3, 6, 9, 12]\n feature_list = self.get_is_raw_col()\n df = ts_df[['idd', 'ft_data_dt']]\n# Time Series Features\n for duration in ts_duration:\n for col in feature_list:\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\n df = pd.concat([df\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\n , med_ts(self, ts_df, col_list, col, duration)\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\n , min_ts(self, ts_df, col_list, col, duration)\\\n , max_ts(self, ts_df, col_list, col, duration)]\n , axis=1)\n self.set_all_data(df)","def _add_lagged_features(self, X: pd.DataFrame, lags: list) -> pd.DataFrame:\n for l in lags:\n X[f'sales_lag_{l + self.shift_days}'] = (X[['id', 'sales', 'd']]\n .groupby('id')['sales']\n .transform(lambda x: x.shift(l + self.shift_days))\n .fillna(0))\n return X","def create_shifted_orderbook_more_feature(ticker, start_date, end_date, lag_period = 5, pred_period = 7):\n # Retrieve the Nifty data from Yahoo finance:\n format = '%Y-%m-%d' # Formatting directives\n start = start_date.strftime(format)\n end = end_date.strftime(format)\n\n yf.pdr_override() # <== that's all it takes :-)\n stock_data = pdr.get_data_yahoo(ticker, start=start, end=end)\n\n # Creates stock lag\n stock_data.dropna()\n stock_lag = pd.DataFrame(data = stock_data, index=stock_data.index)\n\n stock_returns = pd.DataFrame(data = stock_data, index=stock_data.index)\n\n # Initializes dataframe values and smooths the closing price data\n stock_data_smooth = stock_data['Adj Close']\n exponential_smoothing(0.7, stock_data_smooth) #so the stock_data_smooth is smoothing\n\n\n #stock_lag['Volume'] = stock_returns['Volume'] = stock_data['Volume']\n stock_lag[\"Close\"] = stock_data_smooth #so, now the stock_lag[\"Close\"] is derive from Adj Close + smoothing.\n #print stock_lag[\"Close\"]\n\n # Sets lagging price data (previous days' price data as feature inputs)\n for i in range(0, lag_period):\n column_label = 'Lag{:d}'.format(i)\n stock_lag[column_label] = stock_lag['Close'].shift(1+i)\n\n # EMA- Momentum\n #stock_lag['EMA'] = talib.EMA(close, timeperiod = 30)\n ndays = 30\n name_EWMA = 'EWMA_' + str(ndays)\n stock_lag['EWMA_'] = EWMA(stock_lag,ndays )[name_EWMA]\n\n # Bollinger Bands\n #stock_lag['upperband'], stock_lag['middleband'], stock_lag['lowerband'] = talib.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)\n aa = BBANDS(stock_lag, ndays=30)\n stock_lag['upperband'] = aa['Upper BollingerBand']\n stock_lag['lowerband'] = aa['Lower BollingerBand']\n\n # StochK\n #stock_lag['slowk'], stock_lag['slowd'] = talib.STOCH(high, low, close, fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3,\n # slowd_matype=0)\n n = 30\n name_slowk = 'SO%k'\n name_slowd = 'SO%d_' + str(n)\n stock_lag['slowk'] = STOK(stock_lag)[name_slowk]\n stock_lag['slowd'] = STO(stock_lag, n)[name_slowd]\n\n # MACD- Momentum\n #macd, macdsignal, stock_lag['macdhist'] = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)\n n_fast = 12\n n_slow = 26\n name_macd = 'MACD_' + str(n_fast) + '_' + str(n_slow)\n name_macdsignal = 'MACDsign_' + str(n_fast) + '_' + str(n_slow)\n name_macdhist = 'MACDdiff_' + str(n_fast) + '_' + str(n_slow)\n macd = MACD(stock_lag, n_fast, n_slow)[name_macd]\n macdsignal = MACD(stock_lag, n_fast, n_slow)[name_macdsignal]\n stock_lag['macdhist'] = MACD(stock_lag, n_fast, n_slow)[name_macdhist]\n\n # CCI- Momentum\n #stock_lag['CCI'] = talib.CCI(high, low, close)\n stock_lag['CCI'] = CCI(stock_lag, ndays = 30)[\"CCI\"]\n #print stock_lag['CCI']\n\n # # RSI- Momentum\n # #stock_lag['RSI'] = talib.RSI(close, timeperiod=14)\n # ndays = 14\n # name_RSI = 'RSI_' + str(ndays)\n # stock_lag['RSI'] = RSI(stock_lag, n = ndays)[name_RSI]\n # #print stock_lag['RSI']\n\n\n # Chaikin- Volume\n #stock_lag['Chaikin'] = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)\n stock_lag['Chaikin'] = Chaikin(stock_lag)['Chaikin']\n #print stock_lag['Chaikin']\n\n stock_returns['Day Returns'] = stock_data['Adj Close'].pct_change() * 100\n # Sets lagging percent change data\n for i in range(0, lag_period):\n column_label = 'Lag{:d}'.format(i)\n stock_returns[column_label] = stock_lag[column_label].pct_change() * 100\n\n\n # Remove NaN's from stock lag\n print \"shape of stock_lag before dropna: \",stock_lag.shape[0]\n stock_lag = stock_lag.dropna(axis=0, how='any')\n print \"shape of stock_lag before dropna: \",stock_lag.shape[0]\n\n print \"shape of stock_returns before dropna: \",stock_returns.shape[0]\n # Adjusts stock_return data to same length as stock_lag\n stock_returns = stock_returns.tail(stock_lag.shape[0])\n print \"shape of stock_returns after dropna: \",stock_returns.shape[0]\n\n\n # Determine stock movement direction and lagging movement\n stock_movement = pd.DataFrame(index=stock_returns.index)\n stock_movement['Movement_0'] = np.sign(stock_returns['Day Returns'])\n stock_movement['Movement_0'][0] = 1\n for i in range(0, pred_period):\n column_label = 'Movement_{:d}'.format(i + 1)\n stock_movement[column_label] = stock_movement['Movement_0'].shift(i + 1)\n\n # Removes NaNs from 'stock_movement' and resizes 'stocks_returns' and 'stock_lag' accordingly\n print \"shape of stock_movement before dropna: \",stock_movement.shape[0]\n stock_movement = stock_movement.dropna(axis=0, how='any')\n print \"shape of stock_movement after dropna: \",stock_movement.shape[0]\n\n stock_returns = stock_returns[stock_returns.index <= stock_movement.index[stock_movement.index.__len__() - 1]]\n stock_returns = stock_returns.tail(stock_movement.shape[0])\n stock_lag = stock_lag[stock_lag.index <= stock_movement.index[stock_movement.index.__len__() - 1]]\n stock_lag = stock_lag.tail(stock_movement.shape[0])\n\n return stock_data, stock_returns, stock_lag, stock_movement","def add_feat_predict(df_in, rolling_win_size=15):\n cols =['Turbine_ID', 'Date']\n other_cols = []\n for i in df_in.columns:\n if i not in cols:\n other_cols.append(i)\n all_cols = cols + other_cols\n\n df_in = df_in[all_cols]\n\n sensor_cols = []\n for i in df_in.columns[2:]:\n sensor_cols.append(i)\n\n sensor_av_cols = [nm+'_av' for nm in sensor_cols]\n sensor_sd_cols = [nm+'_sd' for nm in sensor_cols]\n\n df_out = pd.DataFrame()\n\n ws = rolling_win_size\n\n #calculate rolling stats for each engine id\n\n for m_id in pd.unique(df_in.Turbine_ID):\n\n # get a subset for each engine sensors\n df_engine = df_in[df_in['Turbine_ID'] == m_id]\n df_sub = df_engine[sensor_cols]\n\n # get rolling mean for the subset\n av = df_sub.rolling(ws, min_periods=1).mean()\n av.columns = sensor_av_cols\n\n # get the rolling standard deviation for the subset\n sd = df_sub.rolling(ws, min_periods=1).std().fillna(0)\n sd.columns = sensor_sd_cols\n\n # combine the two new subset dataframes columns to the engine subset\n new_ftrs = pd.concat([df_engine,av,sd], axis=1)\n\n # add the new features rows to the output dataframe\n df_out = pd.concat([df_out,new_ftrs])\n df_out = df_out.sort_values(by=['Turbine_ID', 'Date'] )\n return df_out","def predict(self, next_days):\n last_date = self.series.index[-1]\n for time in range(next_days):\n row = {}\n for c in self.countries:\n history = self.series[c]\n series = history.astype(float)\n model = ARIMA(series, order=(2,1,0))\n model_fit = model.fit()\n output = model_fit.forecast()\n row[c] = output[0][0]\n last_date = last_date + pd.DateOffset(1)\n self.series.loc[last_date] = row","def store_predictions(self, preds, df, feature):\n\n prev_values = list(df[feature].iloc[:len(df) - len(self.players)])\n prev_values.extend(preds)\n\n df[feature] = prev_values\n\n return df","def test_model_avg_3_weeks_bt(df_y_train, df_y_predict, df_y_ground_truth_scaled, predict_period, n_samples_week, target):\n\n # Use average of last 3 weeks (for same time stamps) as predicted values\n df_hist = pd.concat([df_y_train, df_y_ground_truth_scaled], 0)\n df_hist_1week = df_hist.shift(n_samples_week)\n df_hist_2week = df_hist.shift(2*n_samples_week)\n df_hist_3week = df_hist.shift(3*n_samples_week)\n df_hist_all = pd.concat([df_hist_1week, df_hist_2week, df_hist_3week], 1)\n df_hist_all = df_hist_all[df_hist_all.index.isin(df_y_predict.index)]\n \n if target == \"count\":\n # Average\n prediction = df_hist_all.mean(axis = 1)\n elif target == \"level\":\n # Majority class\n prediction = df_hist_all.mode(axis = 1).iloc[:, 0]\n \n return prediction","def store_predictions(df):\n ts = df[df.columns[1]]\n base = pd.DataFrame(ts)\n preds = make_preds(ts, 'Predicted '+ df.columns[1])\n base.index = df['year']\n base = base.append(pd.DataFrame(preds), sort = True)\n for col in df.columns[2:]:\n ts = df[col]\n temp = pd.DataFrame(ts)\n preds = make_preds(ts, 'Predicted ' + col)\n temp.index = df['year']\n temp = temp.append(pd.DataFrame(preds), sort = True)\n base = base.join(temp)\n return base","def get_dict_X_dynamic(ar_iterations, forecast_cycle, input_k):\n dict_X_past = {}\n for i in range(ar_iterations + 1):\n idxs = get_idx_lag(\n idx_start=0, ar_iteration=i, forecast_cycle=forecast_cycle, input_k=input_k\n )\n idxs_past_data = idxs[idxs < 0]\n # Indexes of past data\n if idxs_past_data.size == 0:\n dict_X_past[i] = None\n else:\n dict_X_past[i] = idxs_past_data\n return dict_X_past","def get_pre_df(temp_pre_df):\n \n event_time_max = temp_pre_df['event_time'].max()\n cat_dfs = []\n for num in np.arange(0,(1080/2)+1,30)[1:]:\n # making <= null i.e keeping >\n temp_pre_df.loc[temp_pre_df['event_time'] <= int(num), 'event_time'] = np.nan\n for col in ['event_name', 'specialty', 'plan_type']:\n cat_df = temp_pre_df.groupby([\"id\", col]).agg({\"event_time\": 'count'}).unstack(level=col)\n cat_df = cat_df/(event_time_max-num)\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\n cat_dfs.append(cat_df)\n pre_df = pd.concat(cat_dfs, axis = 1) \n return pre_df.fillna(0)","def month_lag_distribution(source_df, field=\"month_lag\", path=path.path, nrows=None):\n _log.info(\"Creating features from {}\".format(field))\n prefix = source_df.split(\"_\")[0]\n source_df = \"{}/{}\".format(path, source_df)\n\n _log.info(\"Reading from {}\".format(source_df))\n try:\n df = pd.read_csv(source_df, usecols=[\"card_id\", field], nrows=nrows)\n _log.info(\"Successfully read from {}\".format(source_df))\n except Exception as e:\n _log.exception(e)\n\n _log.info(\"Computing distribution of month lag\")\n func_to_be_applied = [min, max, pd.Series.nunique]\n func_to_be_applied_dummy = [max, np.mean]\n rename_dict = create_rename_dict(prefix, field, func_to_be_applied)\n rename_dict_dummy = create_rename_dict(prefix, \"dummy\", func_to_be_applied_dummy)\n\n df[\"dummy\"] = 1\n df_features = df.groupby(\"card_id\").agg({field:func_to_be_applied}).reset_index()\n df_features = pd.concat([pd.DataFrame(df_features[\"card_id\"]), df_features[field]], axis=1, sort=False)\n\n _log.info(\"Renaming columns: {}\".format(rename_dict))\n df_features.rename(columns=rename_dict, inplace=True)\n\n _log.info(\"Computing time in month between transactions\")\n df_freq = (df.groupby([\"card_id\", field]).agg({\"dummy\": np.sum}).reset_index().groupby(\"card_id\")\n .agg({\"dummy\": func_to_be_applied_dummy}).reset_index())\n df_freq = pd.concat([pd.DataFrame(df_freq[\"card_id\"]), df_freq[\"dummy\"]], axis=1, sort=False)\n df_freq.rename(columns=rename_dict_dummy, inplace=True)\n\n _log.info(\"Creating final df\")\n df_features = df_features.merge(df_freq, how=\"inner\", on=\"card_id\")\n return df_features","def __init__(self, data_frame, mins_set):\n # super(FeaturePrevDelays, self).__init__()\n self.df = data_frame.copy()\n self.mins_set = mins_set","def new_features(df):\n print(\"Add new features ...\")\n # distinguish Spring, Fall and pregnant females (don't care about juvenilles/unknown)\n df[\"gender_plus\"] = df[\"Gender\"]\n df.loc[df.Gravid, \"gender_plus\"] = \"f_gra\"\n\n df[\"gender_seasons\"] = df[\"Gender\"]\n df.loc[df.Gravid, \"gender_seasons\"] = \"f_gra\"\n\n # add features\n df[\"Age_To_Weight\"] = df[\"Annuli\"] / df[\"Weight\"]\n\n # Calcuate Number of recaptures\n df_captures = df[[\"ID\", \"Date\"]].groupby(\"ID\").count()\n df_captures.columns = [\"recapture_count\"]\n df_captures.reset_index(inplace=True)\n df = pd.merge(df, df_captures, how=\"outer\", on=\"ID\")\n\n # recalculate annuli\n df_min = pd.pivot_table(\n df[df.Annuli > 0],\n values=[\"Date\", \"Annuli\"],\n index=[\"ID\"],\n aggfunc={\"Date\": min, \"Annuli\": min},\n )\n df_min.columns = [\"annuli_min\", \"date_min\"]\n df_min.reset_index(inplace=True)\n\n df = pd.merge(df, df_min, how=\"outer\", on=\"ID\")\n df[\"year\"] = df.Date.map(lambda x: x.year)\n df[\"year_min\"] = df.date_min.map(lambda x: x.year)\n df[\"Annuli_orig\"] = df.Annuli\n df.Annuli = df.year - df.year_min + df.annuli_min\n df.Annuli = np.nan_to_num(df.Annuli)\n df[\"Annuli\"] = pd.to_numeric(df[\"Annuli\"], downcast=\"integer\")\n\n # Annuli Buckets\n buckets = 5\n interval = int(df[\"Annuli\"].max() / buckets)\n buckets = [i for i in range(0, df[\"Annuli\"].max() + interval, interval)]\n labels = [\"'{0} - {1}'\".format(i, i + interval) for i in buckets]\n df[\"Annuli_Group\"] = pd.cut(\n df.Annuli, buckets, labels=labels[:-1], include_lowest=True\n )\n\n return df","def generate_time_series_df(eviction_df):\n evictions_by_month = get_counts_by_month(eviction_df, \"month\", \"total-eviction-filings\")\n timeseries_df = evictions_by_month\n return timeseries_df","def apply_all_transformations(df):\n df[\"age_quantile\"] = compute_age_quantile(df, 5)\n df[\"competition_open\"] = compute_competition_open(df)\n df[\"count\"] = df.groupby(\"store\")[[\"date\"]].transform(\"cumcount\")\n df[\"open_lag_1\"] = compute_open_lag(df)\n df[\"open_lag_2\"] = compute_open_lag(df, 2)\n df[\"open_lag_3\"] = compute_open_lag(df, 3)\n df[\"open_lead_1\"] = compute_open_lead(df)\n df[\"open_lead_2\"] = compute_open_lead(df, -2)\n df[\"open_lead_3\"] = compute_open_lead(df, -3)\n df[\"promo_lag_1\"] = compute_promo_lag(df)\n df[\"promo_lag_2\"] = compute_promo_lag(df, 2)\n df[\"promo_lag_3\"] = compute_promo_lag(df, 3)\n df[\"promo_lead_1\"] = compute_promo_lead(df)\n df[\"promo_lead_2\"] = compute_promo_lead(df, -2)\n df[\"promo_lead_3\"] = compute_promo_lead(df, -3)\n df[\"school_holiday_lag_1\"] = compute_school_holiday_lag(df)\n df[\"school_holiday_lag_2\"] = compute_school_holiday_lag(df, 2)\n df[\"school_holiday_lag_3\"] = compute_school_holiday_lag(df, 3)\n df[\"school_holiday_lead_1\"] = compute_school_holiday_lead(df)\n df[\"school_holiday_lead_2\"] = compute_school_holiday_lead(df, -2)\n df[\"school_holiday_lead_3\"] = compute_school_holiday_lead(df, -3)\n df[\"state_holiday_lag_1\"] = compute_state_holiday_lag(df)\n df[\"state_holiday_lag_2\"] = compute_state_holiday_lag(df, 2)\n df[\"state_holiday_lag_3\"] = compute_state_holiday_lag(df, 3)\n df[\"state_holiday_lead_1\"] = compute_state_holiday_lead(df)\n df[\"state_holiday_lead_2\"] = compute_state_holiday_lead(df, -2)\n df[\"state_holiday_lead_3\"] = compute_state_holiday_lead(df, -3)\n df[\"store_day_of_week\"] = compute_store_day_of_week(df)\n df[\"store_month\"] = compute_store_month(df)\n df[\"store_school_holiday\"] = compute_store_school_holiday(df)\n df[\"store_state_holiday\"] = compute_store_state_holiday(df)\n df[\"store_year\"] = compute_store_year(df)\n df[\"zscore\"] = compute_zscore(df, window=150)\n\n return df","def preprocess_data_pandas(raw_data_file: str, features_file: str, cols_to_save: List[str]) -> None:\n\n df = pd.read_csv(raw_data_file)\n\n df.sort_values(by=[\"id\", \"loan_date\"], inplace=True)\n df.reset_index(drop=True, inplace=True)\n\n df[\"loan_date\"] = pd.to_datetime(df['loan_date'], errors='coerce')\n df[\"birthday\"] = pd.to_datetime(df['birthday'], errors='coerce')\n df[\"job_start_date\"] = pd.to_datetime(df['job_start_date'], errors='coerce')\n\n df_grouped_by_id = df.groupby('id')\n\n # Feature nb_previous_loans\n df[\"nb_previous_loans\"] = df_grouped_by_id[\"loan_date\"].rank(method=\"first\") - 1\n\n # Feature avg_amount_loans_previous\n df[\"avg_amount_loans_previous\"] = df_grouped_by_id[\"loan_amount\"].transform(lambda x: x.expanding().mean())\n\n # Feature age\n df['age'] = (pd.to_datetime('today').normalize() - df['birthday']).dt.days // 365\n\n # Feature years_on_the_job\n df['years_on_the_job'] = (pd.to_datetime('today').normalize() - df['job_start_date']).dt.days // 365\n\n # Feature flag_own_car\n df['flag_own_car'] = df.flag_own_car.apply(lambda x: 0 if x == 'N' else 1)\n\n df = df[cols_to_save]\n df.to_csv(features_file, index=False)","def create_feats_and_preds(price_df, feat_days, pred_days):\n\n # create shifted percent features\n df_feats = price_n_days_out(price_df, days=feat_days)\n df_help = df_feats.copy()[['ticker', 'prediction_date', f'price_{feat_days}_out']]\n df_help.columns = ['ticker', 'date', 'close']\n df_preds = price_n_days_out(df_help, days=pred_days)\n\n # do some cleaning\n full_df = pd.merge(df_feats, df_preds, left_on=['ticker', 'prediction_date'], right_on=['ticker', 'date'])\n full_df.columns = ['ticker', 'past_date', 'past_close', 'current_date', 'current_price',\n 'percent_change_feat', 'date_y', 'close_y', 'prediction_date',\n 'price_5_out_y', 'percent_change_pred']\n full_df = full_df[['ticker', 'past_date', 'current_date', 'prediction_date',\n 'percent_change_feat', 'percent_change_pred']]\n return full_df","def ml_pred_post(ticker=None, stock_last_day=None, train_pred_df=None, val_pred_df=None, test_pred_df=None, \nX_features=None, y_features=None, n_past_days=90, n_future_days=5, loss='mean_squared_error', lr=0.01, epochs=12, batch_size=32, RMSE_train_pred=None, RMSE_val_pred=None):\n\n try:\n train_pred_df_dict = train_pred_df.to_dict(orient='records')\n val_pred_df_dict = val_pred_df.to_dict(orient='records')\n test_pred_df_dict = test_pred_df.to_dict(orient='records')\n \n n_X_features = len(X_features)\n n_y_features = len(y_features)\n\n pred_post_to_upload = {\n 'Stock': ticker,\n 'Datetime': stock_last_day,\n 'X_features': X_features,\n 'y_features': y_features,\n 'n_past_days': n_past_days,\n 'n_future_days': n_future_days,\n 'ML Model': {\n 'model': 'LSTM',\n 'parameters': {\n 'layers': {'LSTM_1 units': 64,\n 'LSTM_1 input_shape': (n_past_days, n_X_features),\n 'LSTM_2 units': 32,\n 'Dropout': 0.2,\n 'Dense units': n_future_days*n_y_features\n },\n 'compile': {\n 'optimizer': 'Adam',\n 'loss': str(loss),\n 'lr': lr\n },\n 'fit': {\n 'epochs': epochs,\n 'batch_size': batch_size\n } \n },\n\n },\n 'RMSE_train_pred': float(RMSE_train_pred),\n 'RMSE_val_pred': float(RMSE_val_pred),\n 'train_pred': train_pred_df_dict,\n 'val_pred': val_pred_df_dict,\n 'test_pred': test_pred_df_dict\n }\n return pred_post_to_upload\n\n except Exception as e:\n print('Something went wrong...')\n print(f'str(e) = {str(e)}')\n # print(f'repr(e) = {repr(e)}')\n\n # return pred_post_to_upload","def _predict(self):\n bull_wr = self.bull_win_rate\n self.df = self.df.assign(bull_wr=bull_wr)","def lagged_features(df, lags):\n df_list = []\n for lag in lags:\n df_shifted = df.shift(lag)\n df_shifted.columns = [x + \"_lag\" + str(lag) for x in df_shifted.columns]\n df_list.append(df_shifted)\n fea = pd.concat(df_list, axis=1)\n return fea","def prepareInputs(df: \"pd.dataFrame\") -> \"pd.dataFrame\":\n \n # 1. Exclude missing values\n df = df[df[\"POL_STATUS\"].notnull()]\n \n # 2. Clean the target variable\n df = df[df[\"POL_STATUS\"] != \"Unknown\"]\n df[\"lapse\"] = np.where(df[\"POL_STATUS\"] == \"Lapsed\", 1, 0)\n \n # 3. Create dummy variables for categorical variables\n categorical_cols = [\"CLAIM3YEARS\", \"BUS_USE\", \"AD_BUILDINGS\",\n \"APPR_ALARM\", \"CONTENTS_COVER\", \"P1_SEX\",\n \"BUILDINGS_COVER\", \"P1_POLICY_REFUSED\", \n \"APPR_ALARM\", \"APPR_LOCKS\", \"FLOODING\",\n \"NEIGH_WATCH\", \"SAFE_INSTALLED\", \"SEC_DISC_REQ\",\n \"SUBSIDENCE\", \"LEGAL_ADDON_POST_REN\", \n \"HOME_EM_ADDON_PRE_REN\",\"HOME_EM_ADDON_POST_REN\", \n \"GARDEN_ADDON_PRE_REN\", \"GARDEN_ADDON_POST_REN\", \n \"KEYCARE_ADDON_PRE_REN\", \"KEYCARE_ADDON_POST_REN\", \n \"HP1_ADDON_PRE_REN\", \"HP1_ADDON_POST_REN\",\n \"HP2_ADDON_PRE_REN\", \"HP2_ADDON_POST_REN\", \n \"HP3_ADDON_PRE_REN\", \"HP3_ADDON_POST_REN\", \n \"MTA_FLAG\", 'OCC_STATUS', 'OWNERSHIP_TYPE',\n 'PROP_TYPE', 'PAYMENT_METHOD', \"P1_EMP_STATUS\",\n \"P1_MAR_STATUS\"\n ]\n \n for col in categorical_cols:\n dummies = pd.get_dummies(df[col], \n drop_first = True,\n prefix = col\n )\n df = pd.concat([df, dummies], 1)\n \n # 4. Create age features\n df[\"age\"] = (datetime.strptime(\"2013-01-01\", \"%Y-%m-%d\") - pd.to_datetime(df[\"P1_DOB\"])).dt.days // 365\n df[\"property_age\"] = 2013 - df[\"YEARBUILT\"]\n df[\"cover_length\"] = 2013 - pd.to_datetime(df[\"COVER_START\"]).dt.year\n \n # 5. Impute missing value\n df[\"RISK_RATED_AREA_B_imputed\"] = df[\"RISK_RATED_AREA_B\"].fillna(df[\"RISK_RATED_AREA_B\"].mean())\n df[\"RISK_RATED_AREA_C_imputed\"] = df[\"RISK_RATED_AREA_C\"].fillna(df[\"RISK_RATED_AREA_C\"].mean())\n\n return df","def alspostprocess(data, prediction, features, user_features, movie_features, n_features=10):\r\n \r\n\r\n data['ALS'] = prediction[data.loc[:, 'userID']-1, data.loc[:, 'movieID']-1]\r\n features.append('ALS')\r\n \r\n total_features = len(movie_features)\r\n if n_features>total_features:\r\n n_features = total_features\r\n \r\n for i in range(n_features):\r\n data[\"UserFeature{}\".format(i)] = user_features[data.loc[:, 'userID']-1, i]\r\n features.append(\"UserFeature{}\".format(i))\r\n data[\"MovieFeature{}\".format(i)] = movie_features[i, data.loc[:, 'movieID']-1]\r\n features.append(\"MovieFeature{}\".format(i))\r\n return data, features","def rank_pre_extract(self, mention_data, predictions):\n mdata = pd.DataFrame(mention_data)\n\n\n\n\n pass","def lagger(\n dataset:pd.DataFrame, n_lags:int,\n price_columns : Union[str,List[str]]) -> pd.DataFrame:\n from toolz.curried import reduce\n df = reduce(\n lambda df, lag: df.assign(**{col + '_' +str(lag): dataset[[col]].shift(lag).values for col in price_columns}),\n range(1, n_lags + 1),\n dataset[price_columns])\n\n result = df.assign(**{col: dataset[col] for col in dataset.drop(price_columns, axis=1).columns}).fillna(0)\n return result[sorted(result.columns)]","def samples_timesteps_features(dataframe, columns, start_date, timesteps=72, \n steps_ahead=24, window_days=100, train_percent=80.):\n \n def overlap_windows(dataset, timesteps, steps_ahead):\n \"\"\" Create overlaping window of time-series data\n \n Parameters\n ----------\n dataset: pd.DataFrame\n time-series pandas dataset\n timesteps: int\n number of time steps from the past for creating output arrays\n steps_ahead: int\n number of time steps into the future for making predictions\n \n Returns\n -------\n X, y: np.array\n input and output 3-d arrays of overlaping time windows\n \"\"\"\n X = []; y = []\n \n start = 0\n for i in range(len(dataset)):\n # Define the end of the input sequence\n in_end = start + timesteps\n out_end = in_end + steps_ahead\n # Ensure that there is enough data\n if out_end <= len(dataset):\n X.append(dataset[start:in_end, :])\n # First column holds load values\n y.append(dataset[in_end:out_end, 0])\n # Move along one time step\n start += 1\n \n # Convert list to np.array\n X = np.asarray(X)\n y = np.asarray(y)\n \n return X, y\n\n\n data = dataframe.copy()\n \n if window_days*24 > data.values.shape[0]:\n raise ValueError('Variable window_days has too large value: {}*24h = {} > {}, which is more than there is data!'.format(window_days, window_days*24, \n data.values.shape[0]))\n \n # Training period\n # ---------------\n train_percent = train_percent/100.\n st = pd.to_datetime(start_date) # start date\n et = st + dt.timedelta(days=int(train_percent*window_days)) # end date\n train = data.loc[st:et].values\n \n # Standardize and transform training data set\n mean_std_values = {}\n for i, column in enumerate(columns):\n # Calculate mean and standard deviation only\n # from the training data set values\n mu = train[:,i].mean() # axis=0\n sd = train[:,i].std()\n mean_std_values[column] = (mu, sd)\n # Standardize training data\n train[:,i] = (train[:,i] - mu)/sd\n \n # Create overlapping windows with training data\n X_train, y_train = overlap_windows(train, timesteps, steps_ahead)\n \n # Testing / Validation period\n # ---------------------------\n sv = et \n ev = sv + dt.timedelta(days=int((1-train_percent)*window_days)+1)\n test = data.loc[sv:ev].values\n \n # Transform testing/validation data set\n for i, column in enumerate(columns):\n # Use mean and standard deviation from the\n # training data set\n mu = mean_std_values[column][0]\n sd = mean_std_values[column][1]\n # Standardize test data\n test[:,i] = (test[:,i] - mu)/sd\n \n # Create overlaping windows with test data\n X_test, y_test = overlap_windows(test, timesteps, steps_ahead)\n \n return mean_std_values, X_train, y_train, X_test, y_test","def preprocess_raw_data(df):\n def convert_date_to_datetime(_df):\n return _df.assign(Date=pd.to_datetime(_df['Date']))\n\n def fill_missing_miles_with_zero(_df):\n return _df.fillna({'Miles': 0})\n\n def filter_dates_prior_to_today(_df):\n return _df[_df['Date'] < datetime.datetime.today()]\n\n def calculate_rolling_averages(_df):\n _df['MA_10day'] = _df['Miles'].rolling(window=10).mean().fillna(0)\n _df['MA_30day'] = _df['Miles'].rolling(window=30).mean().fillna(0)\n return _df.sort_values('Date')\n\n pipeline = [\n convert_date_to_datetime,\n fill_missing_miles_with_zero,\n filter_dates_prior_to_today,\n calculate_rolling_averages,\n ]\n for func in pipeline:\n df = func(df)\n\n df['date_str_label'] = df['Date'].dt.strftime('%b-%d')\n\n return df","def predict(self):\n # format data\n df = self.normalize(self.daily)\n x = df.index.astype(np.int64).values.reshape(-1, 1)\n y = self.normalize(df[['Adj Close']]).values\n\n # format time\n one_day_time = 86400000000000\n x_tomorrow = x[-1] + one_day_time\n x_incl_tomorrow = np.append(x, [x_tomorrow], axis=0)\n dates = pd.to_datetime(x_incl_tomorrow.reshape(-1))\n\n # average the predictions\n lin_reg = self.linear_regression(x, y, x_tomorrow, x_incl_tomorrow, dates)\n knn = self.knn(x, y, x_tomorrow, x_incl_tomorrow, dates)\n tomorrow_norm = [(lin_reg + knn) / 2]\n today_norm = [df['Adj Close'][-1]]\n tomorrow = round((tomorrow_norm[0] * self.daily['Adj Close'][0]), 2)\n today = self.daily['Adj Close'][-1]\n percent_gain = round((((tomorrow / today) - 1) * 100), 2)\n percent_gain_int = abs(int(round(percent_gain, 0)))\n\n if percent_gain > 0:\n self.debug += '\\nExpected price gain: {} %, buys + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\n self.buys += percent_gain_int\n else:\n self.debug += '\\nExpected price gain: {} %, sells + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\n self.sells += percent_gain_int\n\n # plots dotted line connecting stock today with tomorrow's prediction\n predicting_line = np.append(today_norm, tomorrow_norm, axis=0)\n\n if self.will_plot:\n self.ax.plot(dates[-2:], predicting_line, color='cyan', dashes=([1, 1, 1, 1]))\n self.ax.plot(pd.to_datetime(x_tomorrow), tomorrow_norm, marker='o', markersize=3, color=\"cyan\")","def preprocess(self, X):\n X = X.copy()\n predictor_subset = self.predictor_subset.copy()\n if 'all' in predictor_subset:\n predictor_subset = add_all_predictors(predictor_subset, X.columns)\n \n use_temporal = 'temporal' in predictor_subset\n if use_temporal:\n X_temporal = get_temporal_predictors(\n X['TIMESTAMP_END']\n )\n predictor_subset.remove('temporal')\n\n X = X[predictor_subset]\n\n if use_temporal:\n X = pd.concat([X, X_temporal], axis=1)\n\n if 'WD' in predictor_subset:\n X = process_wind_direction_predictor(X)\n\n return X","def prob():\n # Get decision score for our example that came with the request\n data = flask.request.json\n #data[]\n year = data[\"year\"]\n Month = data[\"month\"]\n Day = data[\"day\"]\n species = data[\"species\"]\n lat = data[\"lattitude\"]\n lng = data[\"longitude\"]\n\n # find the closest trap to the clicked location\n closest_trap = find_closest_trap(lat,lng,trap_stations)\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\n \n # add 0 in front of month\n month = \"0\"+ Month if len(Month) == 1 else Month\n day = \"0\"+ Day if len(Day) == 1 else Day\n date = year + \"-\" + month + \"-\" + day\n trap[\"date\"] = date\n\n # convert datetime to str to match differing formats\n weather.station = weather.station.astype(str)\n trap.station = trap.station.astype(int).astype(str)\n weather.date = weather.date.astype(str)\n\n # merge trap & weather dataframes\n merged = trap.merge(weather,on=[\"date\",\"station\"],how=\"inner\")\n\n # convert back datetime\n merged.date = pd.to_datetime(merged.date)\n\n # extract features from datetime and dewpoint\n merged['month'] = month\n merged['year'] = year\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\n # add risk feature if month is between june and sept (peak virus season)\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\n\n # Reorder columns & Assign to X\n merged2 = merged.drop([\"date\", \"year\", \"trap\",\"species\"],1)\n cols = [\"month\",\"week\",\"longitude\",\"latitude\",\"tmax\",\"tmin\",\"tavg\",\"depart\",\"dewpoint\",\n \"wetbulb\",\"heat\",\"cool\",\"sunrise\",\"sunset\",\"preciptotal\",\"resultspeed\",\"avgspeed\",\n \"month\",\"dry\",\"risk\"]\n X = merged2[cols]\n \n # predict probability\n pred = loaded_model.predict_proba(X)\n prob = round(pred[0][1]*100,2)\n return flask.jsonify(prob)","def features_past_generation(features_creation_function,\n days,\n feature_names_prefix,\n data,\n indices):\n matches_outcomes=[]\n for i,match_indice in enumerate(indices):\n match=data.iloc[match_indice,:]\n past_matches=data[(data.Date<match.Date)&(data.Date>=match.Date-datetime.timedelta(days=days))]\n match_features_outcome_1=features_creation_function(1,match,past_matches)\n match_features_outcome_2=features_creation_function(2,match,past_matches)\n matches_outcomes.append(match_features_outcome_1)\n matches_outcomes.append(match_features_outcome_2)\n if i%100==0:\n print(str(i)+\"/\"+str(len(indices))+\" matches treated. \"+ features_creation_function.__name__ + str(days))\n train=pd.DataFrame(matches_outcomes)\n train.columns=[feature_names_prefix + \"_\" + str(days) +\"_\" +str(i) for i in range(len(train.columns))]\n \n \n \n return train","def prepare_data(self):\r\n annual_df = self.annual_df\r\n coef_df = self.coef_df\r\n quarter_df = self.quarter_df\r\n # historical_df = self.historical_df\r\n Event_Buffer = self.Event_Buffer\r\n\r\n Tot_Prod = coef_df[\"Product\"].nunique()\r\n # Tot_Week = coef_df[\"wk\"].nunique()\r\n Tot_Week = 52\r\n\r\n EDLP_Events = list(annual_df[\"RP_Events\"])\r\n Min_EDLP_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\r\n ]\r\n Max_EDLP_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in EDLP_Events\r\n ]\r\n\r\n TPR_Events = list(annual_df[\"TPR_Events\"])\r\n Min_TPR_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\r\n ]\r\n Max_TPR_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in TPR_Events\r\n ]\r\n\r\n Target_EDLP_Spend = [i for i in annual_df[\"PPG_RP_Spend\"]]\r\n Target_TPR_Spend = [i for i in annual_df[\"PPG_TPR_Spend\"]]\r\n Target_Trade_Spend = [i for i in annual_df[\"PPG_Total_Spend\"]]\r\n\r\n Mapping = {}\r\n Prod_Ind = coef_df[\"Product\"][0:Tot_Prod]\r\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\r\n Mapping[j] = i\r\n Mapping_reverse = {i: j for j, i in Mapping.items()}\r\n\r\n constants = [i for i in coef_df[\"constant\"]]\r\n\r\n Cat_Coef = coef_df[\"Catalogue\"][0:Tot_Prod]\r\n\r\n Disp_Coef = coef_df[\"Display\"][0:Tot_Prod]\r\n\r\n Base_Price_stg1 = [i for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg1 = []\r\n for pr in range(Tot_Prod):\r\n Intercepts_stg1.append(\r\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\r\n )\r\n\r\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg2 = [\r\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\r\n ] # noqa\r\n\r\n EDLP_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Regular\") == 1]]\r\n )\r\n TPR_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Promoted\") == 1]]\r\n )\r\n\r\n # ################################ Available EDLP Interactions pairs ##############################\r\n\r\n EDLP = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Regular\") > 1\r\n ]\r\n EDLP_Interactions = []\r\n for i in EDLP:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n EDLP_Interactions.append(temp)\r\n\r\n # ###################################### Available TPR Interactions pairs #########################\r\n\r\n TPR = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Promoted\") > 1\r\n ]\r\n TPR_Interactions = []\r\n for i in TPR:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n TPR_Interactions.append(temp)\r\n\r\n # ###################################### EDLP_Interaction_Coef_Values ############################\r\n\r\n EDLP_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Regular\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ###################################### TPR_Interaction_Coef_Values #############################\r\n\r\n TPR_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Promoted\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ##################################### Loading Pantry Loading Coefficients #######################\r\n\r\n Pantry_1 = list(coef_df[\"Pantry_Loading_1\"])\r\n Pantry_1 = [\r\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\r\n ]\r\n Pantry_2 = list(coef_df[\"Pantry_Loading_2\"])\r\n Pantry_2 = [\r\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\r\n ]\r\n\r\n # TE_Coeff = np.array(Promo_df[[\"TE_Promo\",\"TE_NoPromo\"]])\r\n self.Tot_Prod = Tot_Prod\r\n self.Tot_Week = Tot_Week\r\n self.EDLP_Events = EDLP_Events\r\n self.Min_EDLP_Events = Min_EDLP_Events\r\n self.Max_EDLP_Events = Max_EDLP_Events\r\n self.TPR_Events = TPR_Events\r\n self.Min_TPR_Events = Min_TPR_Events\r\n self.Max_TPR_Events = Max_TPR_Events\r\n\r\n self.Target_EDLP_Spend = Target_EDLP_Spend\r\n self.Target_TPR_Spend = Target_TPR_Spend\r\n self.Target_Trade_Spend = Target_Trade_Spend\r\n self.Mapping = Mapping\r\n self.Mapping_reverse = Mapping_reverse\r\n self.constants = constants\r\n self.EDLP_Coef = EDLP_Coef\r\n self.TPR_Coef = TPR_Coef\r\n\r\n self.EDLP_Interactions = EDLP_Interactions\r\n self.TPR_Interactions = TPR_Interactions\r\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\r\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\r\n self.Pantry_1 = Pantry_1\r\n self.Pantry_2 = Pantry_2\r\n\r\n self.Base_Price_stg1 = Base_Price_stg1\r\n self.Intercepts_stg1 = Intercepts_stg1\r\n self.Base_Price_stg2 = Base_Price_stg2\r\n self.Intercepts_stg2 = Intercepts_stg2\r\n\r\n self.Cat_Coef = Cat_Coef\r\n self.Disp_Coef = Disp_Coef","def create_predictors(y): # pragma: no cover\n lags = y[-1:-4:-1]\n\n return lags","def preprocess(dataframe_csvpath, cols_x, cols_y, window_in, window_out, data_div_frac, popu_size):\n \n #Loading .CSV file and creating dataframe\n df = pd.read_csv(dataframe_csvpath) \n len_ser = len(df[df['Series_No'] == 1])\n\n #randomly shuffle different series\n permute = np.random.permutation(range(1, len(set(df['Series_No']))))\n train_series_seq = permute[: int(len(set(df['Series_No'])) * data_div_frac)]\n test_series_seq = permute[int( len(set(df['Series_No'])) * data_div_frac):]\n \n #taking relevent columns from dataframe \n df_x = df[cols_x]\n df_y = df[cols_y]\n \n #Innitialize empty lists which are later to be appended\n train_seq, test_seq = [], []\n x_test = []\n y_true =[]\n \n #Creating time series data\n for series_no in train_series_seq:\n \n #new dataframe variable assignment for particular series drom df_x, df_y\n series_df_x = df_x[df_x['Series_No'] == series_no]\n series_df_y = df_x[df_y['Series_No'] == series_no]\n \n #converting into numpy arrays\n array_x = np.array(series_df_x)\n array_y = np.array(series_df_y)\n \n #for loop to append to x_train y_train arrays according to window_in, window_out\n for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked\n arrayx = array_x.copy()\n x = arrayx [idx:idx + window_in, : len(cols_x) - 1]\n #print(x)\n x[:,0:3] = x[:,0:3] / popu_size\n #print(x)\n arrayy = array_y.copy()\n y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]\n y = y / popu_size\n train_seq.append((x, y)) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]\n #y_train.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])\n #print(train_seq)\n\n #repeat for test sequence\n for series_no in test_series_seq:\n \n #new dataframe variable assignment for particular series drom df_x, df_y\n series_df_x = df_x[df_x['Series_No'] == series_no]\n series_df_y = df_x[df_y['Series_No'] == series_no]\n \n #converting into numpy arrays\n array_x = np.array(series_df_x)\n array_y = np.array(series_df_y)\n \n #for loop to append to x_train y_train arrays according to window_in, window_out\n for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked\n arrayx = array_x.copy()\n x = arrayx[idx:idx + window_in, : len(cols_x) - 1]\n x[:,0:3] = x[:,0:3] / popu_size\n x_test.append(x)\n arrayy = array_y.copy()\n y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]\n y = y / popu_size\n y_true.append(y)\n test_seq.append((x, y))\n \n \n #test_seq.append((array_x[idx:idx + window_in, : len(cols_x) - 1], array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]\n #y_test.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])\n\n \n win_len_per_ser = len_ser - window_in - window_out + 1\n \n return np.array(train_seq), np.array(test_seq), len_ser, win_len_per_ser, np.array(x_test), np.array(y_true)","def predictions(dataframe):\n # Select Features\n features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']]\n\n # Add UNIT to features using dummy variables\n dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\n features = features.join(dummy_units)\n\n # Values\n values = dataframe['ENTRIESn_hourly']\n m = len(values)\n\n features, mu, sigma = normalize_features(features)\n features['ones'] = np.ones(m) # Add a column of 1s (y intercept)\n\n # Convert features and values to numpy arrays\n features_array = np.array(features)\n values_array = np.array(values)\n\n # Set values for alpha, number of iterations.\n alpha = 0.1 # please feel free to change this value\n num_iterations = 75 # please feel free to change this value\n\n # Initialize theta, perform gradient descent\n theta_gradient_descent = np.zeros(len(features.columns))\n theta_gradient_descent, cost_history = gradient_descent(features_array,\n values_array,\n theta_gradient_descent,\n alpha,\n num_iterations)\n\n plot = None\n # -------------------------------------------------\n # Uncomment the next line to see your cost history\n # -------------------------------------------------\n # plot = plot_cost_history(alpha, cost_history)\n #\n # Please note, there is a possibility that plotting\n # this in addition to your calculation will exceed\n # the 30 second limit on the compute servers.\n\n predicted = np.dot(features_array, theta_gradient_descent)\n return predicted, plot","def process_data(dataframe):\n\n # add a new column for just the date in date format\n dataframe = createDateColumn(dataframe)\n # get the frequency of each date\n frequency = dataframe['Date Merged'].value_counts()\n # converting to df and assigning new names to the columns\n df_value_counts = pd.DataFrame(frequency)\n df_value_counts = df_value_counts.reset_index()\n # change column names\n df_value_counts.columns = ['dates', 'counts']\n # delete the the row with None\n dateFreq = df_value_counts.loc[df_value_counts[\"dates\"] != \"None\"]\n\n # 1. Create a graph for number of PRs merged over time\n numPRMerged_graph(dateFreq)\n # 2. Create a graph for avg PR merge time\n avgMergetime(dataframe)\n # 3. A table with PR info for each month\n getMonthlyPRinfo(dataframe)","def predict(df,x):\n ml,g,sl2,sm2,sd2= x\n _,in_dic = rl.build_data_strucutre(df,'length_box_um',1)\n pred_mat = rl.prediction_total(ml,g,sl2,sm2,in_dic['reind_v'],in_dic['dat_v'],in_dic['s'],in_dic['S'],in_dic['dt'],in_dic['lane_ID_v'],in_dic['val_v'],1,sd2,nproc=10)\n df = rl.merge_df_pred(df,pred_mat)\n return df","def applyTimeLag(dataset, lags, delta):\n \n # need to shift the y value back a day so we're predicting\n # next day price, rather than today!\n dataset.Return_Out = dataset.Return_Out.shift(-1)\n \n maxLag = max(lags)\n\n columns = dataset.columns[::(2*max(delta)-1)]\n for column in columns:\n for lag in lags:\n newcolumn = column + str(lag)\n dataset[newcolumn] = dataset[column].shift(lag)\n\n return dataset.iloc[maxLag:-1,:]","def predict(self, **kwargs):\n\n logging.debug(\n \"Call predict() with parameters. \"\n \"Forecast 1 step only, kwargs:{kwargs}\".format(kwargs=kwargs)\n )\n\n X_test = self.df[-self.step :][self.feature_names]\n X_test = self.scaler.transform(X_test)\n y_predict = self.model.predict(X_test)\n poly_now = self.y_train_season_obj[-1]\n first_occ = np.where(self.y_train_season_obj == poly_now)\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\n now = self.df[\"y\"][-self.step :]\n return (now - poly_now) - y_predict + polynext","def prepare_training_data(consumption_series, lag):\r\n # scale training data\r\n scaler = MinMaxScaler(feature_range=(-1, 1))\r\n consumption_vals = scaler.fit_transform(consumption_series.values.reshape(-1, 1))\r\n \r\n # convert consumption series to lagged features\r\n consumption_lagged = create_lagged_features(consumption_vals, lag=lag)\r\n\r\n # X, y format taking the first column (original time series) to be the y\r\n X = consumption_lagged.drop('consumption', axis=1).values\r\n y = consumption_lagged.consumption.values\r\n \r\n # keras expects 3 dimensional X\r\n X = X.reshape(X.shape[0], 1, X.shape[1])\r\n \r\n return X, y, scaler","def prepDataFrameForPreprocessor(df, drop_backers_count = True, drop_staff_pick = True):\n #df = pd.read_csv('data/df_clean.csv')\n df = dropCols(df,drop_backers_count,drop_staff_pick) #drop unnecessary columns\n df = dateTimeUpdate(df) #create time delta columns, add these and drop the original timesptamps\n #df.drop(df.columns[df.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)\n df = df[(df['state']=='failed') | (df['state']=='successful')]\n \n #labelencode y from string to int\n label_encoder = preprocessing.LabelEncoder()\n label_encoder.fit([\"failed\",\"successful\"])\n \n df[\"state\"] = label_encoder.transform(df.state)\n \n return df","def create_lag_features_with_time_feature(df = None, cols = None, time = None, n = 5, fillna = True):\n # assert\n assert(df is not None and cols is not None)\n # set attributes\n cols_to_rename = cols\n print(cols_to_rename)\n if type(n) == list:\n shift_range = n\n elif type(n) == int:\n shift_range = range(1, n+1)\n else:\n print(\"type of n is flase, set it to default: 5\")\n shift_range = range(1, 6)\n # try to get the new features\n for month_shift in tqdm(shift_range):\n train_shift = df.copy()\n train_shift[time] = train_shift[time] + month_shift\n foo = lambda x: '{}_lag_{}'.format(x, month_shift) if x in cols_to_rename else x\n train_shift = train_shift.rename(columns=foo)\n df = pd.merge(all_data, train_shift, on=index_cols, how='left').fillna(0)\n del train_shift\n gc.collect()\n return df","def _predict(self):\n bull_wr = self.bull_win_rate+np.where(self.df['bullAmount']>self.df['bearAmount'], 1, -1)*self.p_adjustment\n self.df = self.df.assign(bull_wr=bull_wr)","def make_predictions(df):\n t_labels = get_labels(\"labels_pca\")\n # clean data\n df = clean_data(df)\n # engineer data\n df = engineer_features(df)\n # predict\n with open(\"model.pkl\",\"r\") as mdl:\n model = pickle.load(mdl)\n mdl.close()\n predictions = model.predict(df[t_labels])\n return predictions","def anomaly_detect(data):\n reconstruction_dict = {}\n for location, dataframe in data.items():\n model_dir_path = os.path.join(THIS_DIR, './models')\n print(location)\n samples = len(dataframe.index.codes[0])\n timesteps = len(dataframe.columns.levels[1])\n np_data_o1 = dataframe[['occupancy1']].values\n np_data_o2 = dataframe[['occupancy2']].values\n np_data_diff = np.abs(np_data_o1 - np_data_o2)\n scaler = MinMaxScaler()\n np_data_o1 = scaler.fit_transform(np_data_o1)\n np_data_o2 = scaler.fit_transform(np_data_o2)\n np_data_diff = scaler.fit_transform(np_data_diff)\n np_data = np.stack((np_data_o1, np_data_o2, np_data_diff), axis=-1)\n print(str(np_data.shape[0]) + ' weeks processing') \n\n ae = LstmAutoEncoder()\n\n # fit the data and save model into model_dir_path\n ae.fit(np_data[:, :, :], model_dir_path=model_dir_path, std_dev_threshold=1.5)\n\n # load back the model saved in model_dir_path detect anomaly\n ae.load_model(model_dir_path)\n anomaly_information = ae.anomaly(np_data[:, :, :])\n reconstruction_error = {}\n if ae.threshold == 0.0:\n continue\n else:\n reconstruction_error['threshold'] = ae.threshold\n years = list(dataframe.index.get_level_values(0))\n weeks = list(dataframe.index.get_level_values(1))\n for idx, (is_anomaly, dist) in enumerate(anomaly_information):\n reconstruction_error[str(years[idx]) + ', ' +str(weeks[idx])] = [is_anomaly, dist]\n if is_anomaly == True:\n print(location + ' year ' + str(years[idx]) + ', week ' + str(weeks[idx]) + ' is anomalous')\n reconstruction_dict[location] = reconstruction_error\n\n return reconstruction_dict","def pre_processing_(data_df , serialized_objects):\n max_recency_acc_dig = serialized_objects['max_recency_acc_dig'] # These values are taken from trained model values\n max_recency_dig_2yr = serialized_objects['max_recency_dig_2yr'] # These values are taken from trained model values\n max_acc_recency_mf = serialized_objects['max_acc_recency_mf'] #These are values imported in training dataset. Same values needs to be used to impute missing values in unseen data\n\n data_df = data_df.na.fill({\n 'recency_acc_dig' : max_recency_acc_dig, # Filling missing values\n 'recency_dig_2yr' : max_recency_dig_2yr,\n 'acc_recency_mf' : max_acc_recency_mf\n })\n\n freq_acc_upg_2yrs_split = [-float('inf'), 0, 1, 2, float('inf')]\n bucketizer_freq_acc_upg_2yrs = Bucketizer(splits=freq_acc_upg_2yrs_split, inputCol='freq_acc_upg_acc_2yrs', outputCol='freq_acc_upg_acc_2yrs_bkt')\n data_df = bucketizer_freq_acc_upg_2yrs.setHandleInvalid('keep').transform(data_df) # Binning the freq_acc_upg_acc_2yrs column\n\n tot_purchase_split = [-float('inf'), 0, 1, 2, 3, float('inf')]\n bucketizer_tot_purchase = Bucketizer(splits=tot_purchase_split, inputCol='tot_accsry_purchse', outputCol='tot_accsry_purchse_bkt')\n data_df = bucketizer_tot_purchase.setHandleInvalid('keep').transform(data_df) # Binning the tot_accsry_purchse column\n\n del_cols_new = ['freq_acc_upg_acc_2yrs', 'tot_accsry_purchse']\n data_df = data_df.drop(*del_cols_new) # Dropping the older continuous columns\n return data_df","def preprocess_data(df, cat_columns, cf_columns):\n \n #define cat and cf columns, convert cat to dummies\n df_cat = pd.get_dummies(df[cat_columns], columns = cat_columns, drop_first=True, dtype=float)\n df_cat.rename(mapper= {\"sex_2\": \"sex_female\", \"education_2\":\"education_university\", \"education_3\": \"education_high_school\", \"education_4\":\"education_others\",\"marital_status_2\":\"marital_status_single\", \"marital_status_3\": \"marital_status_others\"}, axis = 1, inplace = True)\n df_cf = df[cf_columns]\n X = pd.concat([df_cf, df_cat], axis = 1)\n y = df[['default_payment_next_month']]\n print(\"dummy variables created\")\n \n #train-test split \n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n print(\"split done\")\n \n #resample the train sets\n smote = SMOTE(sampling_strategy = \"not majority\", random_state = 42)\n X_train_rs, y_train_rs = smote.fit_sample(X_train, y_train)\n print('original class distribution:')\n print(y[\"default_payment_next_month\"].value_counts())\n print('synthetic sample class distribution:')\n print(pd.Series(y_train_rs).value_counts()) \n return X, X_train_rs, X_test, y_train_rs, y_test","def build_prophet_preds(data_url: str,\n changepoint_scale: float,\n holidays_scale: float,\n seasonality_scale: float,\n growth ='logistic') -> DataFrame:\n\n # read in the data\n df = pd.read_csv(data_url, parse_dates=['date'])\n\n ### PART 1: USE PROPHET TO FIT TOP-LEVEL FORECAST ON ALL CASES\n\n # get total number of cases for each day\n cases = df.groupby('date')['positiveIncrease'].sum().reset_index()\n cases.rename({'date': 'ds', 'positiveIncrease': 'y'}, axis=1, inplace=True)\n\n # variables we'll use a few times\n cap = 1000000\n floor = 0\n\n # add cap + floor for logistic growth model\n cases['cap'] = cap\n cases['floor'] = floor\n\n # initialize -- parameters were set via cross validation -- see notebook for covid modeling for more detail\n mod = Prophet(growth='logistic',\n changepoint_prior_scale = changepoint_scale,\n holidays_prior_scale = holidays_scale,\n seasonality_prior_scale = seasonality_scale)\n\n mod.add_country_holidays(country_name='US')\n\n print(f\"Fitting Model with parameters: changepoint_scale = {mod.changepoint_prior_scale}, holidays_scale: {mod.holidays_prior_scale}, seasonality_scale: {mod.seasonality_prior_scale}\")\n mod.fit(cases)\n\n # make df for future predictions -- seven days from most recent date in data set\n dates = pd.date_range(df['date'].max() + pd.DateOffset(days=1), periods=7).tolist()\n df_vals = [(date, cap, floor) for date in dates]\n future = pd.DataFrame(df_vals, columns=['ds', 'cap', 'floor'])\n\n # get top level predictions for each day in our forecast\n top_level_preds = mod.predict(future)[['ds', 'yhat', 'yhat_upper', 'yhat_lower']]\n\n ### PART 2: PROJECT TOP LEVEL PREDICTIONS DOWN TO ALL 50 STATES!\n\n # create new df with date/state pairs for each state and date for forecast dates\n states = df['state'].unique().tolist()\n test_df_vals = [(date, state) for date in dates for state in states]\n test_df = pd.DataFrame(test_df_vals, columns=['date', 'state']).sort_values(by='date')\n\n # get 7 day moving average of proportions for each state in our dataset, as of last day of recorded data\n train_grp = df.groupby('date')['positiveIncrease'].sum()\n train_cases = df.merge(train_grp, on='date', how='left')\n train_cases.rename({'positiveIncrease_y': 'totalCases'}, axis=1, inplace=True)\n train_cases['proportion'] = train_cases['positiveIncrease_x'] / train_cases['totalCases']\n train_cases.set_index(['state', 'date'], inplace=True)\n train_cases.sort_index(level=[0, 1], inplace=True)\n prop_moving_avgs = train_cases.groupby(level=0)['proportion'].rolling(7).mean()\n final_props = prop_moving_avgs.groupby(level=0).last()\n\n # merge proportions w/ preds_df on each unique date, proportion values\n test_df = test_df.merge(final_props, left_on='state', right_index=True, how='left')\n test_df = test_df.merge(top_level_preds, left_on='date', right_on='ds', how='left')\n test_df.dropna(inplace=True)\n\n # this loop takes each of our top level forecasted values, and multiplies them by the proportion\n # for each state on each day\n for col in ['yhat', 'yhat_upper', 'yhat_lower']:\n test_df[col] = test_df['proportion'] * test_df[col]\n\n # don't need these columns\n test_df.drop(['ds', 'proportion'], axis=1, inplace=True)\n\n # re-organize df to get a column for upper, lower, middle prediction for each state on each day\n test_df = test_df.pivot(index='state', columns='date', values=['yhat', 'yhat_upper', 'yhat_lower'])\n\n # following lines are to format test_df to something more readable in the database\n\n # get unique dates in test_df, in ascending order\n times = sorted(list(set(time for label, time in test_df.columns if 'yhat' in label)))\n # rename test_df columns to make them more semantic\n columns = [f'day{i}_{ending}' for ending in ['pred', 'pred_upper', 'pred_lower'] for i in range(1, len(times) + 1)]\n test_df.columns = columns\n\n # note what date day 1, 2, 3, etc actually are\n for idx, time in enumerate(times, start=1):\n test_df[f\"day{idx}_date\"] = time\n\n # popout out the state index one more time\n test_df.reset_index(inplace=True)\n\n # these last lines add a final line to test_df for total values for the entire usa\n num_vals = test_df.select_dtypes(include=np.number).sum()\n time_vals = test_df.select_dtypes(include=np.datetime64).iloc[0]\n index_vals = num_vals.index.tolist() + time_vals.index.tolist()\n index_vals.insert(0, 'state')\n\n series_vals = num_vals.tolist() + time_vals.tolist()\n series_vals.insert(0, 'USA')\n\n test_df = test_df.append(pd.Series(series_vals, index=index_vals), ignore_index=True)\n test_df['model'] = 'Prophet'\n test_df['dt'] = np.datetime64('now')\n\n return test_df","def predict(self, requested_day):\n\n df = load_data()\n\n # preprocess\n df = preprocess(df)\n df = filter_by_country(df, self.country_code)\n\n # separate cases from data\n dates, Y = separate(df)\n\n # normalize Y\n Y = normalize(Y)\n\n # apply look_back and generate needed samples\n X, _ = apply_lookback(Y, look_back=self.look_back)\n\n # reshape to fit the model\n X = reshape(X)\n dates = reshape(dates)\n dates = dates[self.look_back:]\n\n # unite samples (X) with dates\n # a date of a sample corresponds to the future prediction value (Y)\n united_samples = unite_dates_samples(dates.reshape(-1, 1),\n X.reshape(-1, self.look_back))\n\n last_day = requested_day\n predicted = 0\n\n for step in range(self.look_forward):\n sample, last_day = get_sample(united_samples, last_day)\n sample = sample.reshape(1, 1, self.look_back)\n\n predicted = self.model.predict(sample.astype(np.float32))\n\n united_samples, last_day = append_sample(united_samples, predicted,\n self.look_back,\n last_day)\n\n # date of the prediction\n last_day = change_date(last_day, delta_days=-1)\n\n # chosen starting date\n start_avail_day = change_date(last_day,\n delta_days=-self.look_back)\n\n # convert to a real number of COVID-19 cases\n predicted = denormalize(predicted)[0, 0]\n predicted = int(predicted)\n\n prediction_info = {\n 'prediction_date': last_day,\n 'starting_date': start_avail_day,\n 'prediction_new_cases': predicted\n }\n\n return prediction_info, united_samples","def extractlag(player, stat4lag, lag ):\n\n db_name = \"NHLseasonML_seasonstats.db\"\n \n # connect to our database that will hold everything\n conn = sqlite3.connect(db_name)\n\n with conn:\n # get the cursor so we can do stuff\n cur = conn.cursor()\n\n # Notice that the stats extracted are hard-coded...\n cur.execute(\"SELECT seasonId, points, goals, ppPoints, shots, timeOnIcePerGame, assists, gamesplayed, playerId \\\n FROM s_skater_summary \\\n WHERE seasonId NOT IN (20172018) \\\n AND playerId=?\", [player])\n\n data = cur.fetchall()\n \n if len(data) > 0: # only lag if some data is retreived\n\n # import data into a dataframe\n df = pd.DataFrame(data)\n\n # name the columns of df\n df.columns = ('year', 'points', 'goals', 'ppPoints', 'shots', 'timeOnIcePerGame', 'assists', 'games', 'id')\n # ensure the results are sorted by year, latest at the top:\n df = df.sort_values(['year'],ascending = False) # this step was not necessary for seasonML1 - results were already sorted!\n # create a dataframe of shifted values - these are lagged w.r.t. the original dataframe\n dfshift = df.shift(lag)\n # name the columns of the shifted df\n dfshift.columns = ('yearlag', 'pointslag', 'goalslag', 'ppPointslag', 'shotslag', 'timeOnIcePerGamelag', 'assistslag', 'gameslag', 'idlag')\n\n # find the index of the column desired for lagging\n columnindex = df.columns.get_loc(stat4lag)\n\n # append the appropriate column of the shifted df to the end of the original df\n df = df.join(dfshift.iloc[:,columnindex]).iloc[lag:,:]\n\n #return df # may consider changing to return an array\n return np.array(df)\n \n else: # return NaNs of appropriate shape in case no data is retreived from database\n \n # create an empty array\n temp = np.empty((1,6))\n # fill it with NaNs\n temp.fill(np.nan)\n # convert to a Dataframe\n df = pd.DataFrame(temp)\n # name these columns to match typical output\n df.columns = ('year', 'points', 'goals', 'ppPoints', 'shots', 'timeOnIcePerGame', 'assists', 'games', 'id','pointslag')\n \n #return df\n return np.array(df)","def predict_sales() -> str:\r\n # HTML tables and list that are filled depending on the user input.\r\n html_gr_month_table = app.config[\"table_monthly_growth\"]\r\n html_gr_week_table = app.config[\"table_weekly_growth\"]\r\n html_csv_list = \"\"\r\n # Empty filename and path to sales forecast graph; also dep. on user input.\r\n graph_filename = \"\"\r\n graph_filepath = \"'File has not been created yet!'\"\r\n # Contains HTML form data inputted by the user submitted using POST.\r\n response = request.form\r\n num_months_input = response.get(\"num_months_input\")\r\n csv_filename_input = response.get(\"csv_filename_input\")\r\n # True if user inputted num of past months for prediction and uploaded CSV.\r\n if num_months_input is not None and csv_filename_input is not None:\r\n # Loads data from CSV into dataframe & removes rows with missing values\r\n file_name = csv_filename_input\r\n dataframe = load_csv_clean_to_dataframe(file_name)\r\n try:\r\n # Replaces index numbers of df with datetimes to get a time series.\r\n dataframe = convert_to_time_series(dataframe)\r\n except KeyError as error:\r\n error_message = (\"The following column name is missing \"\r\n \"in the data: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Calculates Quantity ordered averages for every past week.\r\n column_to_avg = \"Quantity ordered\"\r\n average_by = \"W\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\r\n # Calculates Quantity ordered averages for every past month.\r\n average_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_month = average_data(dataframe, column_to_avg,\r\n average_by)\r\n # Calculates past Quantity ordered average growth rates.\r\n growth_rates_per_week = past_avgs_per_week.pct_change()\r\n growth_rates_per_month = past_avgs_per_month.pct_change()\r\n # Creates HTML table containing past monthly sales growth rates.\r\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\r\n app.config[\"table_monthly_growth\"] = html_gr_month_table\r\n # Creates HTML table containing past weekly sales growth rates.\r\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\r\n app.config[\"table_weekly_growth\"] = html_gr_week_table\r\n # Filters dataframe for beer types.\r\n dunkel = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Dunkel\"]\r\n pilsner = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Pilsner\"]\r\n red_helles = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Red Helles\"]\r\n # Monthly basis;\r\n # calculates dunkel's Quantity ordered sums for each month.\r\n column_to_sum = \"Quantity ordered\"\r\n sum_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\r\n # Calculates pilsner's Quantity ordered sums for each month.\r\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\r\n # Calculates red_helles' Quantity ordered sums for each month.\r\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\r\n all_order_sum_month = {\"dunkers\": dunk_order_sum_per_month,\r\n \"pilsner\": pils_order_sum_per_month,\r\n \"red_helles\": redh_order_sum_per_month}\r\n # Clears monthly sales forecasts from the dictionary.\r\n app.config[\"monthly_sales_forecasts\"] = {}\r\n # Hyperparameters (hp) for seasonal ARIMA model.\r\n order = app.config[\"order\"]\r\n # Sets num_months_in. as num of past periods to be considered by model.\r\n app.config[\"seasonal_order\"][3] = int(num_months_input)\r\n seasonal_order = app.config[\"seasonal_order\"]\r\n # Fits ARIMA model and forecasts monthly sales for each beer type.\r\n try:\r\n for beer_type in all_order_sum_month:\r\n model = define_and_fit_model(all_order_sum_month[beer_type],\r\n False, order, seasonal_order)\r\n # Forecasts 12 months into the future with the fitted model.\r\n forecast = model.get_forecast(steps=12)\r\n # Stores monthly sales forecasts in dictionary by beer_type.\r\n app.config[\"monthly_sales_forecasts\"][beer_type] = forecast\r\n # Plots actual and predicted num of beers sold and saves plot.\r\n plot_size = (15, 10)\r\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\r\n plot_size, beer_type)\r\n except ValueError as error:\r\n error_message = (\"Wrong hyperparameters are set, the model \"\r\n + \"can not be fitted: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Gets filepath of this module and thus filepath to graph (png file).\r\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\r\n graph_filename = \"monthly_forecast_and_past.png\"\r\n try:\r\n # Opens plotted graph.\r\n img = Image.open(graph_filename)\r\n img.show()\r\n except (FileNotFoundError, PermissionError) as error:\r\n app.config[\"logger\"].error(error)\r\n # Finds names of all uploaded CSV files in folder where this module is.\r\n all_uploaded_csv = []\r\n for csv_file in glob.glob(\"*.csv\"):\r\n all_uploaded_csv.append(csv_file)\r\n # Creates HTML drop-down list containing all uploaded CSV filenames.\r\n html_csv_list = update_csv_list(all_uploaded_csv)\r\n return (\"\"\"<style>\r\n h1, h2, h3 {\r\n font-family: arial, sans-serif;\r\n }\r\n table {\r\n font-family: arial, sans-serif;\r\n border-collapse: collapse;\r\n width: 100%;\r\n }\r\n td, th {\r\n border: 1px solid #dddddd;\r\n text-align: left;\r\n padding: 8px;\r\n }\r\n tr:nth-child(even) {\r\n background-color: #dddddd;\r\n }\r\n </style>\r\n <h2>Predict sales</h2>\r\n <form action=\"/predict_sales\" method=\"POST\">\r\n Please select an uploaded CSV file that should be used for the \r\n sales forecast (if the list is empty, no CSV file has been \r\n uploaded yet; to upload a file, please go back to the \r\n tracking screen).<br>\r\n <select name=\"csv_filename_input\">\"\"\"\r\n + html_csv_list\r\n + \"\"\"</select><br><br>\r\n Please enter how many <b>months</b> of past data should be\r\n used for the sales forecast:<br>\r\n (Note: If the number of past periods is higher than the number\r\n of periods in the past sales data, the nonexistent periods are\r\n recorded as 0 sales, which, if too many periods are 0,\r\n makes the prediction zero.\r\n <br>\r\n <input type=\"number\" name=\"num_months_input\" min=\"2\"\r\n required=\"required\">\r\n <br><br>\r\n <input type=\"submit\" value=\"Predict sales\">\r\n </form>\r\n In case the graph is not displayed, please open the file \"\"\"\r\n + graph_filename + \" manually using the path: \"\r\n + graph_filepath\r\n + \"\"\"<form action=\"/\" method=\"POST\">\r\n <input type=\"hidden\">\r\n <br>\r\n <input type=\"submit\" value=\"Go back to tracking screen\">\r\n </form> \r\n <h3>Average sales growth rates of the past months</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Monthly average</th>\r\n </tr>\"\"\" + html_gr_month_table + \"</table>\"\r\n + \"\"\"<h3>Average sales growth rates of the past weeks</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Weekly average</th>\r\n </tr>\"\"\" + html_gr_week_table + \"</table>\")","def generate_ml_matrix(full_df, pred_ticker, feat_days):\n\n feature_tickers = [i for i in full_df.ticker.unique() if i != pred_ticker]\n dfml = full_df[full_df.ticker == pred_ticker].drop('ticker', axis=1)\n dfml.rename({'percent_change_feat': f'{pred_ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\n dfml.rename({'percent_change_pred': f'{pred_ticker}_percent_change_pred'}, axis=1, inplace=True)\n for ticker in feature_tickers:\n help_df = full_df[full_df.ticker == ticker][['past_date', 'current_date', 'prediction_date', 'percent_change_feat']]\n help_df.rename({'percent_change_feat': f'{ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\n dfml = pd.merge(dfml, help_df,\n left_on=['past_date', 'current_date', 'prediction_date'],\n right_on=['past_date', 'current_date', 'prediction_date'],\n how='left')\n return dfml","def monthly_viewed(df):\n df = convert_to_datetime(df)\n today = datetime.date.today()\n this_month_start = today - timedelta(days=30)\n last_month_start = today - timedelta(days=60)\n month_per_min = []\n lastmonth_per_min = []\n thismonth_viewed = []\n lastmonth_viewed = []\n for index, row in df.iterrows():\n if row['session_start'].date() >= this_month_start:\n per_min = get_cards_per_min(row)\n month_per_min.append(per_min)\n thismonth_viewed.append(row['total_looked_at'])\n if last_month_start <= row['session_start'].date() < this_month_start:\n per_min = get_cards_per_min(row)\n lastmonth_per_min.append(per_min)\n lastmonth_viewed.append(row['total_looked_at'])\n month_viewed_result = total_viewed(thismonth_viewed, lastmonth_viewed)\n month_viewed_result['total_viewed_monthly'] = month_viewed_result.pop('total_viewed')\n return month_viewed_result","def make_auto_regressive_dataset(df, autoreg_window, log=True, deaths=True, cases=False, predict_deaths=True):\n\n assert (deaths == True or cases == True)\n feature_array = []\n ys = []\n _cases = list(df['cases'])\n _deaths = list(df['deaths'])\n for i in range(len(_cases)):\n for j in range(len(_cases[i]) - (autoreg_window + 1)):\n if predict_deaths:\n contains_event = sum(_deaths[i][j:j + autoreg_window + 1]) > 0\n else:\n contains_event = sum(_cases[i][j:j + autoreg_window + 1]) > 0\n if contains_event > 0:\n cases_window = _cases[i][j:j + autoreg_window]\n if log:\n cases_window = [np.log(v + 1) for v in cases_window]\n deaths_window = _deaths[i][j:j + autoreg_window]\n if log:\n deaths_window = [np.log(v + 1) for v in deaths_window]\n if predict_deaths:\n y_val = _deaths[i][j + autoreg_window + 1]\n else:\n y_val = _cases[i][j + autoreg_window + 1]\n if log:\n y_val = np.log(y_val + 1)\n features = []\n if deaths == True:\n features.extend(deaths_window)\n if cases == True:\n features.extend(cases_window)\n feature_array.append(features)\n ys.append(y_val)\n return feature_array, ys","def generate_admission_prediction():\n query = \"SELECT * FROM admissions_data\"\n result = general_db_func.query_database(query)\n admission_data = pd.DataFrame(result)\n admission_data.rename(columns={0: \"year\", 1: \"university\", 2: \"admissions\"}, inplace=True)\n admission_data = admission_data.sort_values(by=[\"university\", \"year\"], ascending=True)\n admission_data = admission_data.groupby(\"university\")\n admission_data = [admission_data.get_group(x) for x in admission_data.groups]\n\n for uni in admission_data:\n uni_name = list(uni[\"university\"])[0]\n uni = uni.drop(columns=[\"university\"])\n uni.set_index(uni.year, inplace=True)\n\n historic_points = generate_admission_year_points()\n historic_admissions = fill_none_admissions(uni[\"admissions\"].tolist())\n\n historic_data = (historic_points, historic_admissions)\n\n try:\n # Generate autoregressive model\n model = AR(uni[\"admissions\"])\n model_fit = model.fit()\n\n # predict next 2 years\n prediction_points = generate_admission_year_points(future=True)\n predicted_admissions = model_fit.predict(len(uni[\"year\"]), len(uni[\"year\"])+1).to_list()\n predicted_data = (prediction_points, predicted_admissions)\n insert_predicted_admission(uni_name, historic_data, predicted_data)\n except ValueError as e:\n print(e)","def _forecast(model, predict_steps):\n\n pred_uc = model.get_forecast(steps=predict_steps)\n\n # Produce the forecasted tables\n predicted_mean_df = pred_uc.predicted_mean.reset_index()\n predicted_mean_df.columns = ['Date', 'Predicted_Mean']\n return predicted_mean_df","def time_to_n_previous(df, cols, dummy_col, generated_feature_name, params={'n':1,'fillna':np.nan}):\n n = int(params['n'])\n if n > 0:\n n = -n\n params['n'] = n\n return time_to_n_next(df, cols, dummy_col, generated_feature_name, params)","def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \r\n \r\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\r\n \r\n if dropnan==False:\r\n series.replace(pd.np.nan,0,inplace=True)\r\n \r\n # Dropping other variables:\r\n if dropVars:\r\n index = list(np.arange(series.shape[1]-2,\r\n series.shape[1]-len(predictors)-2,\r\n -1))\r\n \r\n labels = [item for idx,item in enumerate(series.columns) \r\n if idx in index]\r\n \r\n #print(\"Eliminando variáveis: {}\".format(labels))\r\n series.drop(labels,axis=1,inplace=True) \r\n \r\n return series","def tag_data(df):\n fault_dic = get_fault_info()\n #nan_cols = check_nan_value(df)\n #df.drop(columns=nan_cols, inplace=True)\n df.dropna(axis=1, how='all', inplace=True)\n df.reset_index(drop=True, inplace=True)\n df[\"tag\"] = 0\n df[\"flag\"] = 0\n df[\"30_day\"] = 0\n for row in df.itertuples():\n number = getattr(row, \"serial_number\")\n manufacturer = getattr(row, \"manufacturer\")\n model = getattr(row, \"model\")\n key = tuple([manufacturer, model, number]) #get keys to look up in fault_dic\n if key in fault_dic:\n fault_date = fault_dic[key][\"date\"]\n date = str(getattr(row, \"dt\"))\n days_diff = (pd.to_datetime(fault_date) - pd.to_datetime(date)).days\n if days_diff >= 0 and days_diff <= 30:\n df.loc[row[0], \"tag\"] = fault_dic[key][\"tag\"] + 1\n if days_diff == 0:\n df.loc[row[0], \"flag\"] = 1\n if days_diff == 30:\n df.loc[row[0], \"30_day\"] = 1\n correct_column_type(df) \n return df","def _predict(cls, model, is_log_transformed,\n raw_actual, interpolated_actual,\n training_end=None, seasonal_feature_scoring=None, pred_date=None, order_of_diff=None,\n training_tail=None, ext_training_features=None, pred_len=None, freq=None,\n include_holidays_exog=None):\n\n import numpy as np\n import pandas as pd\n import scipy.stats as st\n from numpy.linalg import LinAlgError\n import math\n\n alpha = cls._sig_level\n alpha_extreme = cls._sig_level_extreme\n\n include_holidays_exog = include_holidays_exog if ext_training_features else 0\n\n index = pd.date_range(start=training_end, end=pred_date, freq=freq)[1:] # Holidays are always daily.\n\n de_obj = DataExploration()\n pred_exog = de_obj._get_exog_data(pred_date, pred_date, index) if include_holidays_exog else None\n\n if pred_exog is not None and set(pred_exog.columns.values) != set(ext_training_features):\n missing_col_list = list(set(ext_training_features) - set(pred_exog.columns.values))\n common_cols = list(set(ext_training_features).intersection(set(pred_exog.columns.values)))\n temp_df = pred_exog[common_cols]\n missing_feat_df = pd.DataFrame(np.zeros([len(pred_exog), len(missing_col_list)]),\n columns=missing_col_list, index=pred_exog.index.values)\n pred_exog = pd.concat([temp_df, missing_feat_df], axis=1)\n pred_exog = pred_exog[ext_training_features]\n\n freq = \"1\" + freq if not any(char.isdigit() for char in freq) else freq\n\n forecast_ndays = int((pred_date - pd.Timestamp(training_end)) / pd.Timedelta(freq))\n model_freshness = forecast_ndays / float(pred_len)\n\n try:\n if forecast_ndays > pred_len:\n raise ValueError('Current trained model object expired')\n\n float_min = 1e-10\n\n # set exogenous (holiday) variables for input data\n if include_holidays_exog:\n pred_exog = pred_exog.loc[pd.Timestamp(training_end) + pd.Timedelta(freq): pred_date]\n else:\n pred_exog = None\n\n if seasonal_feature_scoring:\n if not include_holidays_exog:\n pred_exog = seasonal_feature_scoring[:forecast_ndays]\n else:\n pred_exog['fourier_feature'] = seasonal_feature_scoring[:forecast_ndays]\n\n forecast = list(model.forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog))\n interpolated_training_data = list(zip(*training_tail))[1]\n\n for order in list(reversed(range(order_of_diff))):\n training_data_diff = np.diff(interpolated_training_data,\n order) if order > 0 else interpolated_training_data\n\n forecast_diff_mean = [training_data_diff[-1]]\n forecast_diff_ci = []\n\n for i in range(forecast_ndays):\n forecast_diff_mean.append(forecast_diff_mean[-1] + forecast[0][i])\n forecast_diff_ci.append([forecast_diff_mean[-1] -\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i]),\n forecast_diff_mean[-1] +\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i])])\n forecast[0] = forecast_diff_mean[1:]\n forecast[2] = forecast_diff_ci\n\n if is_log_transformed:\n transformed_back_forecast = np.exp(forecast[0][-1] + ((forecast[1][-1] ** 2) / 2.0)) - 1\n transformed_back_std_err = np.sqrt((np.exp(forecast[1][-1] ** 2) - 1) * (np.exp((2 * forecast[0][-1]) +\n (forecast[1][\n -1] ** 2))))\n transformed_back_CILower = transformed_back_forecast - \\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\n if transformed_back_std_err != 0 else transformed_back_forecast\n transformed_back_CIUpper = transformed_back_forecast + \\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\n if transformed_back_std_err != 0 else transformed_back_forecast\n transformed_back_interpolated_actual = float(np.exp(interpolated_actual) - 1)\n if np.sum(np.isnan(forecast[0][-1])) or np.isnan(forecast[1][-1]):\n raise ValueError('Predicted null value')\n\n if is_log_transformed:\n zscore = (transformed_back_interpolated_actual -\n transformed_back_forecast) / max(float(transformed_back_std_err), float_min)\n\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\n if math.isnan(anomaly_probability) or math.isnan(transformed_back_CILower) \\\n or math.isnan(transformed_back_CIUpper):\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN under log transform')\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\n\n result = {'Success': True,\n 'IsLogTransformed': is_log_transformed,\n 'LogTransformedAdjustedActual': interpolated_actual,\n 'LogTransformedPrediction': float(forecast[0][-1]),\n 'LogTransformedStdErr': float(forecast[1][-1]),\n 'LogTransformedCILower': float(forecast[2][-1][0]),\n 'LogTransformedCIUpper': float(forecast[2][-1][1]),\n 'AdjustedActual': transformed_back_interpolated_actual,\n 'Prediction': float(transformed_back_forecast) if not float(\n transformed_back_forecast) == float('inf') else 0.0,\n 'StdErr': float(transformed_back_std_err) if not float(\n transformed_back_std_err) == float('inf') else 0.0,\n 'CILower': float(transformed_back_CILower) if not float(\n transformed_back_CILower) == float('-inf') else 0.0,\n 'CIUpper': float(transformed_back_CIUpper) if not float(\n transformed_back_CIUpper) == float('inf') else 0.0,\n 'ConfLevel': float(1.0 - alpha) * 100,\n 'ExogenousHolidays': include_holidays_exog,\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\n 'ModelFreshness': model_freshness}\n\n else:\n zscore = (interpolated_actual - forecast[0][-1]) / max(float(forecast[1][-1]), float_min)\n\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\n if math.isnan(anomaly_probability) or math.isnan(forecast[2][-1][0]) or math.isnan(forecast[2][-1][1]):\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN')\n\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\n\n result = {'Success': True,\n 'IsLogTransformed': is_log_transformed,\n 'AdjustedActual': interpolated_actual,\n 'Prediction': float(forecast[0][-1]) if not float(\n forecast[0][-1]) == float('inf') else 0.0,\n 'StdErr': float(forecast[1][-1]) if not float(\n forecast[1][-1]) == float('inf') else 0.0,\n 'CILower': float(forecast[2][-1][0]) if not float(\n forecast[2][-1][0]) == float('-inf') else 0.0,\n 'CIUpper': float(forecast[2][-1][1]) if not float(\n forecast[2][-1][1]) == float('inf') else 0.0,\n 'ConfLevel': float(1.0 - alpha) * 100,\n 'ExogenousHolidays': include_holidays_exog,\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\n 'ModelFreshness': model_freshness}\n\n except (LinAlgError, ValueError, LADStructuralError) as e:\n result = {'Success': False,\n 'AdjustedActual': interpolated_actual,\n 'ErrorMessage': str(e)}\n\n return result","def _basis_function_n_step_prediction(self, X, y, steps_ahead, forecast_horizon):\n if len(y) < self.max_lag:\n raise ValueError(\n \"Insufficient initial condition elements! Expected at least\"\n f\" {self.max_lag} elements.\"\n )\n\n if X is not None:\n forecast_horizon = X.shape[0]\n else:\n forecast_horizon = forecast_horizon + self.max_lag\n\n yhat = super()._basis_function_n_step_prediction(\n X, y, steps_ahead, forecast_horizon\n )\n return yhat.reshape(-1, 1)","def forc_model(self):\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\n for add in self.X.address1.unique():\n add_mask = self.X.address1 == add\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\n top_index = foo.index[0]\n clust = int(foo.cluster.values[0])\n # get values from last visit for store\n base_input = foo[self.model_mask_cols].values[0]\n base_actual = self.y[top_index]\n lag2_val = base_input[lag1_loc]\n lag1_val = base_actual\n\n for i in range(1, self.num_periods + 1):\n model = self.model_list[clust]\n inputs = base_input\n inputs[lag1_loc] = lag1_val\n inputs[lag2_loc] = lag2_val\n \n pred = model.predict(inputs.reshape(1, -1))\n self._update_cust_table(add, i, pred)\n \n lag2_val = lag1_val\n lag1_val = pred","def FE_add_lagged_targets_by_date_category(train2, target_col, date_col, category_col, test2):\r\n train2 = copy.deepcopy(train2)\r\n test2 = copy.deepcopy(test2)\r\n new_col = target_col+'_array'\r\n train2[new_col] = 0\r\n i = 0\r\n train2.reset_index(drop=False, inplace=True) # index column will be called 'index'\r\n train2 = train2.set_index(pd.to_datetime(train2.pop(date_col)))\r\n train2.sort_index(inplace=True)\r\n for index, each_train in tqdm(train2.iterrows(), total=train2.shape[0]):\r\n before_grp = train2.loc[train2.index < index]\r\n prim = each_train[category_col]\r\n mask = (before_grp[category_col] == prim)\r\n if len(before_grp.loc[mask]) > 0:\r\n price_val = before_grp.loc[mask,target_col].values.tolist()\r\n else:\r\n price_val = before_grp[target_col].values.tolist()\r\n #price_val = f\"{price_val}\"\r\n price_val = np.mean(price_val)\r\n #print(' i = ', i+1, price_val)\r\n train2.iloc[i,-1] = price_val\r\n i += 1\r\n if not isinstance(test2, str):\r\n test2[new_col] = 0\r\n i = 0\r\n test2.reset_index(drop=False, inplace=True) # index column will be called 'index'\r\n test2 = test2.set_index(pd.to_datetime(test2.pop(date_col)))\r\n test2.sort_index(inplace=True)\r\n for index1, each_test in tqdm(test2.iterrows(), total=test2.shape[0]):\r\n before_grp = train2.loc[train2.index < index1]\r\n prim = each_test[category_col]\r\n mask = (before_grp[category_col] == prim)\r\n if len(before_grp.loc[mask]) > 0:\r\n price_val = before_grp.loc[mask,target_col].values.tolist()\r\n else:\r\n price_val = before_grp[target_col].values.tolist()\r\n price_val = np.mean(price_val)\r\n test2.iloc[i,-1] = price_val\r\n i += 1\r\n return train2.set_index('index'), test2.set_index('index')","def create_date_data(gt_id,\n target_horizon,\n experiment,\n date_features=[\"mei\", \"mjo\",\n \"pca_sst_2010\", \"pca_icec_2010\",\n \"pca_wind_hgt_10_2010\",\n \"pca_wind_hgt_100_2010\",\n \"pca_wind_hgt_500_2010\",\n \"pca_wind_hgt_850_2010\"]):\n\n time_start = time.time()\n\n # --------\n # Prepare experiment cache directory and saved file names\n # --------\n\n # Name of cache directory for storing non-submission-date specific\n # intermediate files\n cache_dir = os.path.join('results', experiment, 'shared',\n '{}_{}'.format(gt_id, target_horizon))\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\n\n # if cache_dir doesn't exist, create it\n if not os.path.isdir(cache_dir):\n os.makedirs(cache_dir)\n\n # Filenames for data file to be stored in cache_dir\n date_data_file = os.path.join(\n cache_dir, \"date_data-{}_{}.h5\".format(gt_id, target_horizon))\n\n # --------\n # Creates date_data dataframe.\n # --------\n # Get number of days between start date of observation period used for prediction\n # (2 weeks behind) and start date of target period (2 or 4 weeks ahead)\n start_deltas = [get_start_delta(target_horizon, gt_id)\n for gt_id in date_features]\n\n # Load masked date features\n print \"Loading date features\"\n date_data = get_date_features(gt_ids=date_features, gt_shifts=start_deltas,\n first_year=get_first_year(gt_id))\n\n print \"Loading additional date features\"\n t = time.time()\n if 'mjo' in date_features:\n # Add cosine and sine transforms of MJO phase\n mjo_phase_name = 'phase_shift'+str(get_start_delta(target_horizon, 'mjo'))\n date_data['cos_'+mjo_phase_name] = np.cos((2*np.pi*date_data[mjo_phase_name])/8)\n date_data['sin_'+mjo_phase_name] = np.sin((2*np.pi*date_data[mjo_phase_name])/8)\n print \"Elapsed: {}s\".format(time.time() - t)\n # Save date features to disk\n print \"Saving date features to \"+date_data_file\n t = time.time()\n date_data.to_hdf(date_data_file, key=\"data\", mode=\"w\")\n subprocess.call(\"chmod a+w \"+date_data_file, shell=True)\n print \"Elapsed: {}s\".format(time.time() - t)\n\n print \"Finished generating date_data matrix.\"\n print \"Total time elapsed: {}s\".format(time.time()-time_start)\n return list(date_data)","def create_features(energy_data, label=None):\n energy_data['date'] = energy_data.index\n energy_data['hour'] = energy_data['Datetime'].dt.hour\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\n energy_data['month'] = energy_data['Datetime'].dt.month\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\n energy_data['year'] = energy_data['Datetime'].dt.year\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\n\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\n \"pjme_8_hrs_mean\", \"pjme_12_hrs_mean\", \"pjme_24_hrs_mean\", \"pjme_4_hrs_std\",\n \"pjme_8_hrs_std\", \"pjme_12_hrs_std\", \"pjme_24_hrs_std\",\n \"pjme_4_hrs_max\", \"pjme_8_hrs_max\", \"pjme_12_hrs_max\", \"pjme_24_hrs_max\",\n \"pjme_4_hrs_min\", \"pjme_8_hrs_min\", \"pjme_12_hrs_min\", \"pjme_24_hrs_min\"]]\n if label:\n label = energy_data[label]\n return features, label\n return features","def add_all_features(df):\n df.reset_index(drop=True, inplace=True)\n df = target_indicators(df)\n df = momentum_indicators(df)\n df = trend_indicators(df)\n df = volatility_indicators(df)\n df = volume_indicators(df)\n df = special_indicators(df)\n return df","def preprocess_feature(df):","def predict_XGB():\n #read future games\n future_mstr = pd.read_csv(FG_filename)\n #extract features and prepare X for predictions\n features = future_mstr.columns[6:]\n X_new = future_mstr[features]\n #load saved model\n xgb = joblib.load(XG_filename)\n #make predictions\n XG_preds = xgb.predict(X_new)\n future_mstr['Predictions'] = XG_preds\n #return the relevant prediction information\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Predictions']\n prediction_mstr_all = future_mstr[cols]\n \n return prediction_mstr_all","def create_my_dataset(df, predict_window):\n\n x = []\n y = []\n for i in range(predict_window, df.shape[0]):\n x.append(df[i-predict_window:i,0])\n y.append(df[i,0])\n #convert data to numpy array\n x = np.array(x)\n y = np.array(y)\n \n return x,y","def _preprocess(self):\n\n self.df = self.df[(self.df['days_b_screening_arrest'] <= 30)\n & (self.df['days_b_screening_arrest'] >= -30)\n & (self.df['is_recid'] != -1)\n & (self.df['c_charge_degree'] != 'O')\n & (self.df['score_text'] != 'N/A')]\n\n self.df['c_jail_out'] = pd.to_datetime(self.df['c_jail_out'])\n self.df['c_jail_in'] = pd.to_datetime(self.df['c_jail_in'])\n self.df['length_of_stay'] = (self.df['c_jail_out']\n - self.df['c_jail_in'])\n\n self.df['score_factor'] = np.where(self.df['score_text']\n != 'Low',\n 'HighScore', 'LowScore')\n self.df['y_pred'] = (self.df['score_factor'] == 'HighScore')","def embed_vectors_1d(self,lag,embed,predict):\n\n tsize = self.X.shape[0]\n t_iter = tsize-predict-(lag*(embed-1))\n\n features = np.zeros((t_iter,embed))\n targets = np.zeros((t_iter,predict))\n\n for ii in range(t_iter):\n\n end_val = ii+lag*(embed-1)+1\n\n part = self.X[ii : end_val]\n\n features[ii,:] = part[::(lag)]\n targets[ii,:] = self.X[end_val:end_val+predict]\n return features, targets","def _add_lags(self, X, y=None, extrapolate=1, update_features_df=False):\n\n # Add lag target to the features if required\n # This will create an additional feature for each sample i.e. the previous value of y \n if y is not None and self.model.lag_target:\n X[\"previous_y\"] = y.shift(1)\n \n if update_features_df:\n # Check the target's data type\n dt = 'float' if is_numeric_dtype(y.iloc[:,0]) else 'str'\n # Set the preprocessing feature strategy for the lag targets\n if self.model.estimator_type == 'classifier':\n fs = 'one hot encoding' \n elif self.model.scale_lag_target and not self.model.scale_target:\n fs = 'scaling'\n else:\n fs = 'none'\n self.model.scale_lag_target\n # Update feature definitions for the model\n self.model.features_df.loc['previous_y'] = [self.model.name, 'previous_y', 'feature', dt, fs, '']\n\n if self.model.lags:\n # Add the lag observations\n X = utils.add_lags(X, lag=self.model.lags, extrapolate=extrapolate, dropna=True, suffix=\"t\")\n \n if update_features_df:\n # Duplicate the feature definitions by the number of lags\n self.model.features_df = pd.concat([self.model.features_df] * (self.model.lags+extrapolate))\n # Set the new feature names as the index of the feature definitions data frame\n self.model.features_df['name'] = X.columns\n self.model.features_df = self.model.features_df.set_index('name', drop=True)\n\n if self.model.debug:\n self._print_log(11, data=X)\n\n return X","def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n\n dataframe['ema6'] = ta.EMA(dataframe, timeperiod=9)\n dataframe['ema24'] = ta.EMA(dataframe, timeperiod=18)\n\n dataframe['ema11'] = ta.EMA(dataframe, timeperiod=32)\n dataframe['ema25'] = ta.EMA(dataframe, timeperiod=64)\n\n dataframe['ema'] =dataframe['ema6']-dataframe['ema24']\n dataframe['ema2'] = dataframe['ema11'] - dataframe['ema25']\n\n dataframe['ema']= dataframe['ema']*0.6 + dataframe['ema2']*0.5\n dataframe['ema2'] = ta.SMA(dataframe['ema'], timeperiod=29)\n\n return dataframe","def create_regressor_attributes(df, attribute, list_of_prev_t_instants) :\n \n list_of_prev_t_instants.sort()\n start = list_of_prev_t_instants[-1] \n end = len(df)\n df['datetime'] = df.index\n df.reset_index(drop=True)\n\n df_copy = df[start:end]\n df_copy.reset_index(inplace=True, drop=True)\n\n for attribute in attribute :\n foobar = pd.DataFrame()\n\n for prev_t in list_of_prev_t_instants :\n new_col = pd.DataFrame(df[attribute].iloc[(start - prev_t) : (end - prev_t)])\n new_col.reset_index(drop=True, inplace=True)\n new_col.rename(columns={attribute : '{}_(t-{})'.format(attribute, prev_t)}, inplace=True)\n foobar = pd.concat([foobar, new_col], sort=False, axis=1)\n\n df_copy = pd.concat([df_copy, foobar], sort=False, axis=1)\n \n df_copy.set_index(['datetime'], drop=True, inplace=True)\n return df_copy","def __init__(self, pandas_dataframe, dates_column, target_column, regressors=None, train_test_split=0.66, seed=7,\n look_back=1, look_forward=1, interval=0):\n data = pd.DataFrame(index=pandas_dataframe[dates_column].values, data=pandas_dataframe[target_column].values)\n # Calculate the training set size\n train_size = int(len(data)*train_test_split)\n # Scale the data pre-train/test split\n scaler = MinMaxScaler(feature_range=(0, 1))\n self.scaler = scaler\n data = scaler.fit_transform(data)\n # Get the time series as stationary (for the given interval, if 0 don't make it a series of 0)\n if interval > 0:\n data = difference(data, interval)\n # Map the series to a supervised problem (values for days 1-n with regressors for these days to predict days\n # n + 1 ... n + k\n x, y = timeseries_to_supervised(data, look_back=look_back, look_forward=look_forward)\n # Split train and test\n self.x_train, self.y_train = x[:train_size], y[:train_size]\n self.x_test, self.y_test = x[train_size:], y[train_size:]\n # Use regressors if required\n if regressors is not None:\n self.x_train, self.x_test = add_regressors(self.x_train, self.x_test, regressors, pandas_dataframe,\n dates_column, look_forward, look_back)\n # Set last attributes\n self.seed = seed\n self.look_back = look_back\n self.look_forward = look_forward\n self.regressors = regressors","def count_in_previous_n_time_unit(df, cols, dummy_col, generated_feature_name, params=None): \n group_cols = cols[:-1]\n calc_col = cols[-1]\n n = int(params['n'])\n encodings = df[group_cols[0]].copy()\n if len(group_cols) > 1:\n for c in group_cols[1 : ]:\n encodings = encodings * (10 ** (int(np.log(df[c].max() + 1) / np.log(10)) + 1)) + df[c]\n encodings = encodings.values\n times = df[calc_col].values\n dict_count = defaultdict(int)\n result = []\n bound = 0\n for cur in range(len(encodings)):\n while abs(times[cur] - times[bound]) > n:\n dict_count[encodings[bound]] -= 1\n bound += 1\n result.append(dict_count[encodings[cur]])\n dict_count[encodings[cur]] += 1\n r = pd.DataFrame(result, columns=[generated_feature_name], dtype=utils.set_type(result, 'uint'))\n del encodings, times, dict_count, result, bound, n\n gc.collect()\n module_logger.debug('feature generated: {}'.format(generated_feature_name))\n return r","def pre_get_data(df):\n\n df_len = len(df.iloc[0, :]) - 1\n\n select_cols = []\n\n for i in range(df_len): #Get Columns that contain number values\n\n if type(df.iloc[0, i + 1]) is np.float64:\n if math.isnan(df.iloc[0, i + 1]) == False:\n select_cols.append(i + 1)\n elif type(df.iloc[0, i + 1]) is np.float:\n if math.isnan(df.iloc[0, i + 1]) == False:\n select_cols.append(i + 1)\n\n\n res_df = df.iloc[:, select_cols]\n\n list_pop = list(res_df)\n list_res = ['B_F1_Bool_Result', 'Event_Date', 'B_WClass']\n list_pop.pop()\n\n for item in list_pop:\n if \"F1\" in item:\n aa = item\n bb = aa.replace(\"F1\", \"F2\")\n if bb in list_pop:\n cc = aa.replace(\"F1\", \"F12\")\n df[cc] = df[aa] - df[bb]\n list_res.append(cc)\n\n elif \"F2\" not in item:\n list_res.append(item)\n\n\n\n bw = df['B_WClass']\n i = -1\n j = df.columns.get_loc('B_WClass')\n\n for item in bw:\n i = i + 1\n if item != item:\n df.iloc[i, j] = np.nan\n else:\n df.iloc[i, j] = get_weight[item]\n\n df['B_WClass'] = df['B_WClass'].astype(float)\n res_df = df[list_res]\n\n return res_df","def decide_input(yt, max_lag_period, forecast_period): \n correlations = list()\n yt_mean = np.mean(yt)\n\n #correlations of time series yt at lag k (yt-1...yt-max_lag_period+1)\n for k in range(1, max_lag_period + 2):\n yt_k = shift(yt, k, cval=np.NaN)\n a = yt-yt_mean\n a = np.nan_to_num(a)\n b = yt_k-yt_mean\n b = np.nan_to_num(b)\n print(np.dot(a, b))\n r_k = np.sum(np.dot(a, b))\n correlations.append(r_k)\n \n print(\"CORRELATIONS\", correlations)\n \n s = list()\n muls = list()\n for i in range(0, len(correlations)-1):\n for j in range(0, len(correlations)-1):\n if(j != i):\n muls.append(abs(correlations[i-j]))\n \n s.append(abs(correlations[i + forecast_period])/abs(np.prod(muls)))\n \n print(s)\n lag_periods = np.argpartition(s, -max_lag_period)[-max_lag_period:] + 1\n \n print(\"lag_periods\", lag_periods)\n return lag_periods","def generate_features(df):\n df_new = pd.DataFrame()\n \n # 6 original features\n df_new['open'] = df['open']\n df_new['open_1'] = df['open'].shift(1)\n df_new['close_1'] = df['close'].shift(1)\n df_new['high_1'] = df['high'].shift(1)\n df_new['low_1'] = df['low'].shift(1)\n df_new['volume_1'] = df['volume'].shift(1)\n \n # 50 original features\n # average price\n df_new['avg_price_5'] = df['close'].rolling(window=5).mean().shift(1)\n df_new['avg_price_30'] = df['close'].rolling(window=21).mean().shift(1)\n df_new['avg_price_90'] = df['close'].rolling(window=63).mean().shift(1)\n df_new['avg_price_365'] = df['close'].rolling(window=252).mean().shift(1)\n \n # average price ratio\n df_new['ratio_avg_price_5_30'] = df_new['avg_price_5'] / df_new['avg_price_30']\n df_new['ratio_avg_price_905_'] = df_new['avg_price_5'] / df_new['avg_price_90']\n df_new['ratio_avg_price_5_365'] = df_new['avg_price_5'] / df_new['avg_price_365']\n df_new['ratio_avg_price_30_90'] = df_new['avg_price_30'] / df_new['avg_price_90']\n df_new['ratio_avg_price_30_365'] = df_new['avg_price_30'] / df_new['avg_price_365']\n df_new['ratio_avg_price_90_365'] = df_new['avg_price_90'] / df_new['avg_price_365'] \n \n \n # average volume\n df_new['avg_volume_5'] = df['volume'].rolling(window=5).mean().shift(1)\n df_new['avg_volume_30'] = df['volume'].rolling(window=21).mean().shift(1)\n df_new['avg_volume_90'] = df['volume'].rolling(window=63).mean().shift(1)\n df_new['avg_volume_365'] = df['volume'].rolling(window=252).mean().shift(1)\n \n #average volume ratio\n df_new['ratio_avg_volume_5_30'] = df_new['avg_volume_5'] / df_new['avg_volume_30']\n df_new['ratio_avg_volumee_5_90'] = df_new['avg_volume_5'] / df_new['avg_volume_90'] \n df_new['ratio_avg_volume_5_365'] = df_new['avg_volume_5'] / df_new['avg_volume_365']\n df_new['ratio_avg_volume_30_90'] = df_new['avg_volume_30'] / df_new['avg_volume_90']\n df_new['ratio_avg_volume_30_365'] = df_new['avg_volume_30'] / df_new['avg_volume_365']\n df_new['ratio_avg_volume_90_365'] = df_new['avg_volume_90'] / df_new['avg_volume_365'] \n \n \n # standard deviation of prices\n df_new['std_price_5'] = df['close'].rolling(window=5).std().shift(1)\n df_new['std_price_30'] = df['close'].rolling(window=21).std().shift(1)\n df_new['std_price_90'] = df['close'].rolling(window=63).std().shift(1) \n df_new['std_price_365'] = df['close'].rolling(window=252).std().shift(1)\n \n # standard deviation ratio of prices \n df_new['ratio_std_price_5_30'] = df_new['std_price_5'] / df_new['std_price_30']\n df_new['ratio_std_price_5_90'] = df_new['std_price_5'] / df_new['std_price_90']\n df_new['ratio_std_price_5_365'] = df_new['std_price_5'] / df_new['std_price_365']\n df_new['ratio_std_price_30_90'] = df_new['std_price_30'] / df_new['std_price_90'] \n df_new['ratio_std_price_30_365'] = df_new['std_price_30'] / df_new['std_price_365'] \n df_new['ratio_std_price_90_365'] = df_new['std_price_90'] / df_new['std_price_365'] \n \n \n # standard deviation of volumes\n df_new['std_volume_5'] = df['volume'].rolling(window=5).std().shift(1)\n df_new['std_volume_30'] = df['volume'].rolling(window=21).std().shift(1)\n df_new['std_volume_90'] = df['volume'].rolling(window=63).std().shift(1)\n df_new['std_volume_365'] = df['volume'].rolling(window=252).std().shift(1)\n \n #standard deviation ratio of volumes\n df_new['ratio_std_volume_5_30'] = df_new['std_volume_5'] / df_new['std_volume_30']\n df_new['ratio_std_volume_5_90'] = df_new['std_volume_5'] / df_new['std_volume_90']\n df_new['ratio_std_volume_5_365'] = df_new['std_volume_5'] / df_new['std_volume_365'] \n df_new['ratio_std_volume_30_90'] = df_new['std_volume_30'] / df_new['std_volume_90']\n df_new['ratio_std_volume_30_365'] = df_new['std_volume_30'] / df_new['std_volume_365']\n df_new['ratio_std_volume_90_365'] = df_new['std_volume_90'] / df_new['std_volume_365'] \n \n # return\n df_new['return_1'] = ((df['close'] - df['close'].shift(1)) / df['close'].shift(1)).shift(1)\n df_new['return_5'] = ((df['close'] - df['close'].shift(5)) / df['close'].shift(5)).shift(1)\n df_new['return_30'] = ((df['close'] - df['close'].shift(21)) / df['close'].shift(21)).shift(1)\n df_new['return_90'] = ((df['close'] - df['close'].shift(63)) / df['close'].shift(63)).shift(1) \n df_new['return_365'] = ((df['close'] - df['close'].shift(252)) / df['close'].shift(252)).shift(1)\n \n #average of return\n df_new['moving_avg_5'] = df_new['return_1'].rolling(window=5).mean()\n df_new['moving_avg_30'] = df_new['return_1'].rolling(window=21).mean()\n df_new['moving_avg_90'] = df_new['return_1'].rolling(window=63).mean()\n df_new['moving_avg_365'] = df_new['return_1'].rolling(window=252).mean()\n \n # the target\n df_new['close'] = df['close']\n df_new = df_new.dropna(axis=0)\n return df_new","def gain_loss_month_daily(current_data):\n months = current_data.groupby(pd.Grouper(freq='MS'))\n losses_monthly = pd.DataFrame(0, columns=current_data.columns, index=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n for mo in months:\n # Per month per year, sum daily gains/losses\n monthly_sum = np.zeros((mo[1].shape[1],))\n for i in range(mo[1].shape[0] - 1):\n # loop over all days in month, except last day\n monthly_sum = monthly_sum + (mo[1].iloc[i+1] - mo[1].iloc[i])\n mon_str = str(mo[0].month_name()[:3])\n for index, val in monthly_sum.items():\n if val < 0:\n losses_monthly.loc[mon_str, index] = losses_monthly.loc[mon_str, index] + 1\n\n return losses_monthly","def series_to_supervised(data, n_in=1, n_out=1, dropnan=True, stride=None, dates=False, leaks=True):\n df = pd.DataFrame(data)\n \n time = None\n if 'date' in df.columns:\n time = 'date'\n elif 'time' in df.columns:\n time = 'time'\n if time != None:\n df = df.drop([time], axis=1)\n \n if 'leak' in df.columns:\n df = df.drop(['leak'], axis=1) \n n_vars = df.shape[1]\n times_column = list()\n if dates and time != None:\n times_column = data[time]\n del data\n \n cols, names, pivots = list(), list(), list()\n \n # input sequence (t-n, ... t-1)\n for i in range(n_in, 0, -1):\n cols.append(df.shift(i))\n names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n\t# forecast sequence (t, t+1, ... t+n)\n for i in range(0, n_out):\n cols.append(df.shift(-i))\n if i == 0:\n names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n else:\n names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n\t# put it all together\n agg = pd.concat(cols, axis=1)\n \n agg.columns = names\n\n #stride - delete windows\n if stride != None:\n indexes_to_drop = list()\n for i in range(stride, agg.shape[0], stride):\n print(\"index\", i)\n pivots += [i]\n \n onset = 0\n offset = pivots[0]\n for i in range(0, len(pivots)):\n print(\"onset\", onset)\n print(\"offset\", offset)\n to_drop = [ x for x in range(onset,offset)]\n indexes_to_drop += to_drop\n try:\n onset = pivots[i] + 1\n offset = pivots[i+1]\n \n except IndexError:\n onset = pivots[i] + 1\n offset = agg.shape[0]\n to_drop = [ x for x in range(onset,offset)]\n indexes_to_drop += to_drop\n \n \n \n print(\"indexes_to_drop\", indexes_to_drop)\n \n agg.drop(df.index[indexes_to_drop], inplace=True)\n \"\"\"\n if dates and time!=None:\n agg[time] = times_column\n \"\"\" \n # drop rows with NaN values \n if dropnan:\n agg.dropna(inplace=True)\n \n\n return agg","def prepare_reg_data_for_prediction(dataframe, model_dict, user_keyword, task_name):\r\n parent_dir = Path.cwd().parent\r\n pickle_dir = parent_dir.joinpath('default_results', 'pickle_files_feat_eng')\r\n\r\n feature_X_user,affect_dataframe, affect_index_dataframe = df, df, df\r\n emo_X_test_dict = {}\r\n affect_index_dict ={}\r\n\r\n for emotion, model_prop in model_dict.items():\r\n #Get the data with the emotion class\r\n if user_keyword == 'validation':\r\n affect_dataframe = dataframe[dataframe['Affect Dimension'] == 1]\r\n affect_index_list = dataframe.index[dataframe['Affect Dimension'] == 1].tolist()\r\n else:\r\n affect_dataframe = dataframe[dataframe[emotion] == 1]\r\n affect_index_list = dataframe.index[dataframe[emotion] == 1].tolist()\r\n test_tweets = affect_dataframe.iloc[:, [0, 1, 2]]\r\n\r\n #Perform preprocessing, feature extraction and transformation for the tweets to be predicted\r\n print(emotion, test_tweets.shape)\r\n if test_tweets.empty == False:\r\n preprocessed_X_user = Preprocessor.perform(test_tweets, emotion, user_keyword, task_name)\r\n feature_X_user = Feature_Transformer.perform(preprocessed_X_user, emotion, user_keyword, task_name)\r\n vectorizer = Dictionaries.vectorizer_dict[model_prop[2]]\r\n\r\n #Fit transform the vectorizer with the corresponding preprocessed training data\r\n if os.path.exists(pickle_dir.joinpath(emotion + '_r_train_preprocess_df.pkl')):\r\n preprocess_train_df = pd.read_pickle(pickle_dir.joinpath(emotion + '_r_train_preprocess_df.pkl'))\r\n train_vect = vectorizer.fit_transform(preprocess_train_df['preprocessed_text'].values)\r\n train_vect_df = pd.DataFrame(train_vect.toarray(), columns=vectorizer.get_feature_names())\r\n print(emotion, 'train-shape', train_vect_df.shape, sep='\\n')\r\n else:\r\n #If the file doesnt exist, exit the program with instructions\r\n print('\\nRequired files does not exist.\\n\\n Please, train the models first by running > Modelling.py')\r\n sys.exit(1)\r\n\r\n # Use the same vectorizer to transform test data and then perform the feature union\r\n vector_X = vectorizer.transform(preprocessed_X_user['preprocessed_text'].values)\r\n test_vect_df = pd.DataFrame(vector_X.toarray(), columns=vectorizer.get_feature_names())\r\n X_test = pd.DataFrame(pd.concat([test_vect_df, feature_X_user], axis=1)) #####?\r\n emo_X_test_dict[emotion] = X_test\r\n affect_index_dict[emotion] = affect_index_list\r\n else:\r\n emo_X_test_dict[emotion] = pd.DataFrame\r\n affect_index_dict[emotion] = []\r\n\r\n return emo_X_test_dict, affect_index_dict","def build_predictions() -> List[str]:\n\n with open(os.path.join(UTILS_PATH, STREETS_FOR_PREDICTIONS_FILE)) as f:\n streets_for_predictions = f.read().split('\\n')[:-1]\n\n street_to_id = {street: ind for ind, street in enumerate(streets_for_predictions)}\n\n yesterday = datetime.today() - timedelta(days=7) # shift for 7 days\n last_month = set([(yesterday - timedelta(days=x)).strftime(DAY_FORMAT) for x in range(30)])\n\n with open(os.path.join(UTILS_PATH, CURRENT_FILENAMES_FILE), \"r\") as f:\n FILENAMES = json.load(f)\n\n filename = FILENAMES[CRASHES_AFTER_ETL]\n conn = sql.connect(f\"{METABASE_DATA_PATH}{filename}.db\")\n query = f\"{SELECT_DATETIME_STREETS_QUERY} '{filename}'\"\n df = pd.read_sql_query(query, conn)\n\n df[DATETIME] = df[DATETIME].apply(lambda x: x.split(\"T\")[0])\n df = df[df[STREET_NAME].isin(streets_for_predictions)]\n df = df.reset_index(drop=True)\n\n df = df[df[DATETIME].isin(last_month)]\n df[DATETIME] = pd.to_datetime(df[DATETIME])\n df = df.reset_index(drop=True)\n\n uniq_dates = sorted(set(df[DATETIME]))\n date_to_id = {date: idd for idd, date in enumerate(uniq_dates)}\n\n X = np.zeros((len(street_to_id), len(date_to_id)), dtype=np.uint32)\n\n for row in df.iterrows():\n info = row[1]\n date = info[DATETIME]\n street = info[STREET_NAME]\n\n date_id = date_to_id[date]\n street_id = street_to_id[street]\n\n X[street_id, date_id] += 1\n\n model = CatBoostRegressor()\n model.load_model(os.path.join(UTILS_PATH, PREDICTOR_FILE))\n\n y_pred = model.predict(X)\n top_10_streets_inds = np.argsort(y_pred)[::-1][:10]\n top_10_streets = [streets_for_predictions[ind] for ind in top_10_streets_inds]\n\n logging.info(\"Predictions successfully made.\")\n\n return top_10_streets","def get_perf(self) :\n self.train()\n self.df_true = self.df_true[self.length:]\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)","def generate_dataframe(forecast, observed):\n dataframe = pd.DataFrame(columns=COLUMNS, dtype=str)\n\n # Write cumulative forecasts.\n forecast_date_str = FORECAST_DATE.strftime(\"%Y-%m-%d\")\n for cum_week in sorted(forecast.keys()):\n target_end_date = FIRST_WEEK + ((cum_week - 1) * datetime.timedelta(7)) \n target_end_date_str = target_end_date.strftime(\"%Y-%m-%d\")\n # Terminate the loop after 8 weeks of forecasts.\n if cum_week >= 8:\n break\n \n # Skip forecasts before the forecast date.\n if target_end_date <= FORECAST_DATE:\n continue\n\n # Write a row for \"weeks ahead\" if forecast end day is a Saturday.\n if target_end_date >= FIRST_WEEK and target_end_date.weekday() == 5:\n target = str(cum_week) + \" wk ahead cum death\"\n for state_id in forecast[cum_week].keys():\n for quantile in forecast[cum_week][state_id].keys():\n val = observed[(FORECAST_DATE - datetime.timedelta(1)).strftime(\"%Y-%m-%d\")][state_id]\n for i in range(1, cum_week + 1):\n val += forecast[i][state_id][quantile]\n if quantile == \"point\":\n dataframe = dataframe.append(\n generate_new_row(\n forecast_date=forecast_date_str,\n target=target,\n target_end_date=target_end_date_str,\n location=str(state_id),\n type=\"point\",\n quantile=\"NA\",\n value=val\n ), ignore_index=True)\n else:\n dataframe = dataframe.append(\n generate_new_row(\n forecast_date=forecast_date_str,\n target=target,\n target_end_date=target_end_date_str,\n location=str(state_id),\n type=\"quantile\",\n quantile=quantile,\n value=val\n ), ignore_index=True)\n \n # Write incident forecasts.\n forecast_date_str = FORECAST_DATE.strftime(\"%Y-%m-%d\")\n for cum_week in sorted(forecast.keys()):\n target_end_date = FIRST_WEEK + ((cum_week - 1) * datetime.timedelta(7)) \n target_end_date_str = target_end_date.strftime(\"%Y-%m-%d\")\n # Terminate the loop after 8 weeks of forecasts.\n if cum_week >= 8:\n break\n \n # Skip forecasts before the forecast date.\n if target_end_date <= FORECAST_DATE:\n continue\n\n if target_end_date >= FIRST_WEEK and target_end_date.weekday() == 5:\n target = str(cum_week) + \" wk ahead inc death\"\n for state_id in forecast[cum_week].keys():\n for quantile in forecast[cum_week][state_id].keys():\n if quantile == \"point\":\n dataframe = dataframe.append(\n generate_new_row(\n forecast_date=forecast_date_str,\n target=target,\n target_end_date=target_end_date_str,\n location=str(state_id),\n type=\"point\",\n quantile=\"NA\",\n value=forecast[cum_week][state_id][quantile]\n ), ignore_index=True)\n else:\n dataframe = dataframe.append(\n generate_new_row(\n forecast_date=forecast_date_str,\n target=target,\n target_end_date=target_end_date_str,\n location=str(state_id),\n type=\"quantile\",\n quantile=quantile,\n value=forecast[cum_week][state_id][quantile]\n ), ignore_index=True)\n \n return dataframe","def get_idx_lag(idx_start, ar_iteration, forecast_cycle, input_k):\n return idx_start + (forecast_cycle * ar_iteration) + input_k","def gain_loss_whole_month(current_data):\n months = current_data.groupby(pd.Grouper(freq='MS'))\n losses_monthly = pd.DataFrame(0, columns=current_data.columns, index=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n for mo in months:\n # Per month per year, subtract final value from starting value\n diff = (mo[1].iloc[-1] - mo[1].iloc[0]).rename(mo[0])\n mon_str = str(diff.name.month_name()[:3])\n for index, val in diff.items():\n if val < 0:\n losses_monthly.loc[mon_str, index] = losses_monthly.loc[mon_str, index] + 1\n\n return losses_monthly","def preprocess(df):\n \n # drop the following columns - irrelevant now\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\n , 'tmdbId', 'popularity', 'year']\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n # drop all of the language columns\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\"lan\"]\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n\n # loop through the columns we want to aggregate\n for col_type in [\n \"original_language_\"\n , \"prod_comp_cntry_\"\n , \"prod_comp_names_\"\n , \"writers_\"\n , \"actors_\"\n , \"genres_\"\n , \"director_\"\n ]:\n # create a dictionary of each unique value and its frequency\n val_freq = {}\n for col in df.columns:\n if col.startswith(col_type):\n val_freq[col] = df[col].sum()\n\n # create a dataframe from this dictionary; sort by count\n counts = pd.DataFrame.from_dict(\n val_freq\n , orient='index'\n , columns=['count']\n ).sort_values('count', ascending=False)\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\n\n # handle special case of production company country\n if col_type == \"prod_comp_cntry_\":\n DROP_COLUMNS = [col for col in counts.index][3:]\n\n # handle special case of directors\n elif col_type == \"director_\":\n DIRECTOR_COLS = [col for col in df.columns\n if col.startswith(\"director_\")\n and col!=\"director_pop\"]\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\n DROP_COLUMNS = DIRECTOR_COLS\n\n # handle special case of actors\n elif col_type == \"actors_\":\n ACTORS_COLS = [col for col in df.columns if \"actors\" in col]\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\n DROP_COLUMNS = ACTORS_COLS\n\n # handle all the other cases\n else:\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\n\n\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n ##########################################################################\n # adjust the data for inflation\n CPI_tf = df['CPIAUCSL'].max()\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n # no longer need CPI data\n df.drop('CPIAUCSL', axis=1, inplace=True)\n \n ########################################################################## \n # add in useful features about the cast and crew \n df['cast_crew_sum_pop'] = (\n df['director_pop']\n + df['avg_actor_pop']\n + df['avg_writer_pop']\n )\n df['cast_crew_product_pop'] = (\n df['director_pop']\n * df['avg_actor_pop']\n * df['avg_writer_pop']\n )\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\n df = df.query('10000 <= revenue').copy()\n df = df.query('100000 <= budget').copy()\n df.drop('sum_actor_pop', axis=1, inplace=True)\n df.drop('min_writer_pop', axis=1, inplace=True)\n\n # code to transform columns\n for col in [\n \"budget\", \"director_pop\", \"avg_writer_pop\"\n , \"max_writer_pop\", \"avg_actor_pop\", \"max_actor_pop\"\n , \"min_actor_pop\", 'cast_crew_sum_pop'\n , 'cast_crew_product_pop'\n ]:\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\n df.drop(col, axis=1, inplace=True)\n \n return df","def dataset_extract_features_from_date(dataset,date_feature): \n dataset['dayofmonth'] = dataset[date_feature].dt.day\n dataset['dayofyear'] = dataset[date_feature].dt.dayofyear \n dataset['dayofweek'] = dataset[date_feature].dt.dayofweek\n dataset['month'] = dataset[date_feature].dt.month\n dataset['year'] = dataset[date_feature].dt.year\n dataset['weekofyear'] = dataset[date_feature].dt.weekofyear\n dataset['is_month_start'] = (dataset[date_feature].dt.is_month_start).astype(int)\n dataset['is_month_end'] = (dataset[date_feature].dt.is_month_end).astype(int)\n return dataset","def preprocess(data,scale):\n ##log_transformation\n #data['log_sale_price'] = np.log(data['sale_price'])\n #data['log_lot_area'] = np.log(data['lot_area'])\n #data['house_age'] = data['year_sold']- data['year_built']\n \n y = data['stay']\n \n #sales['log_sale_price'] = np.log(sales['sale_price'])\n #sales['log_lot_area'] = np.log(sales['lot_area'])\n #sales['house_age'] = sales['year_sold']- sales['year_built']\n data_dummy = data.copy()\n \n #dummy coding\n data_scale = pd.get_dummies(data_dummy).drop(columns = ['stay'])\n\n \n #scale the value\n if scale == True:\n S = StandardScaler().fit(data_scale)\n data_scale = S.transform(data_scale)\n \n return y, data_scale","def _assess(self):\n # get eruptions\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\n # check if data file exists\n self.exists = os.path.isfile(self.file)\n if not self.exists:\n t0 = datetime(2011,1,1)\n t1 = datetime(2011,1,2)\n self.update(t0,t1)\n # check date of latest data in file\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def prepare_X_array(Y, y_var, X_vars, predef_pcas, X_var_definitions, \n X_clxp_definitions, include_persistence=False):\n \n from sklearn.preprocessing import StandardScaler #, Imputer\n from sklearn.impute import SimpleImputer\n from sklearn.utils import resample\n \n # An empty dataframe containing only dates as an index variable,\n # and probably persistence variables\n X = Y[y_var].to_dataframe().drop(columns=y_var)\n if(include_persistence):\n X['LCL_'+y_var+'-1'] = np.roll(Y[y_var], 1)\n X['LCL_'+y_var+'-2'] = np.roll(Y[y_var], 2)\n X['LCL_'+y_var+'-1'][0:1] = Y[y_var].values[0]\n X['LCL_'+y_var+'-2'][0:2] = Y[y_var].values[0]\n \n # Variables from Climate Explorer (https://climexp.knmi.nl/)\n for i,vr in enumerate(X_clxp_definitions): \n index = read_monthly_indices_from_CLIMEXP(vr)\n \n index = index.resample(time='3M').mean()\n index = index.groupby('time.season') - index.groupby('time.season').mean('time')\n \n X = pd.merge(X, index.to_dataframe(), left_index=True, right_index=True, how='left')\n \n for lag in X_clxp_definitions[vr]:\n X[vr+'-'+str(lag)] = np.roll(X[vr], lag)\n X[vr+'-'+str(lag)][0:lag] = X[vr][0]\n \n X = X.drop(columns=vr)\n \n # Remove unnecessary columns\n drops = ['season','season_x','season_y','month_x','month_y','month',y_var]\n for dr in drops:\n try: X = X.drop(columns=dr)\n except: pass\n\n # Variables from gridded datasets\n X_pcas, X_eigs, X_errs = {}, {}, {}\n for i,vr in enumerate(X_var_definitions):\n \n trn_yrs = X_var_definitions[vr][3]\n all_yrs = X_var_definitions[vr][4]\n\n trn_idx = bool_index_to_int_index(np.isin(X_vars[i]['time.year'], trn_yrs))\n all_idx = bool_index_to_int_index(np.isin(X_vars[i]['time.year'], all_yrs))\n \n try:\n cps = predef_pcas[vr].transform(X_vars[i].values)\n _, pca, svl = predef_pcas[vr], -99, -99\n print('Predefined PCA model used')\n except:\n #_, pca, svl = apply_PCA(X_vars[i].values[trn_idx],X_var_definitions[vr][1])\n cps, pca, svl = apply_PCA(X_vars[i].values,X_var_definitions[vr][1])\n #cps = pca.transform(X_vars[i].values)\n print('PCAs calculated from data')\n \n # Sampling uncertainty according to North et al. (1982)\n eig = svl**2 \n N = X_vars[i].values.shape[0]\n sampling_err = eig*(2/N)**(0.5)\n \n X_pcas[vr] = pca; X_eigs[vr] = eig; X_errs[vr] = sampling_err\n \n for cpn in range(X_var_definitions[vr][1]):\n vrb_name = vr+str(cpn+1)\n X.loc[:,vrb_name] = cps[:,cpn]\n \n \n for lag in X_var_definitions[vr][2]:\n X[vrb_name+'-'+str(lag)] = np.roll(X[vrb_name], lag)\n X[vrb_name+'-'+str(lag)][0:lag] = X[vrb_name][0]\n \n X = X.drop(columns=vrb_name)\n \n # Fill (potential) holes with mean values and normalize to N(0,1) \n X[:] = SimpleImputer().fit_transform(X.values)\n X[:] = StandardScaler().fit_transform(X.values)\n\n return X, X_pcas, X_eigs, X_errs","def run():\n\n df = read_input() # the parameters\n df = add_time_period(df) # a feature\n df = is_holiday(df) # a feature\n df = scale_continous(df) # continous feature transformation\n df = encode_dummy(df) # categorical feature transformation\n df = order_columns(df) # ordering model inputs\n model = load_model() # the multiple linear regression model\n prediction = int(model.predict(df)) # form a prediction\n return prediction # return the prediction","def add_change_features(df):\n # copy input for comparison of outputs\n df_copy = df.copy()\n\n # calculate interval change features\n df_copy[\"Duration_Start\"] = (\n df_copy[\"Schedule_Start\"] - df_copy[\"Design_Start\"]\n ).dt.days\n df_copy[\"Duration_End\"] = (\n df_copy[\"Schedule_End\"] - df_copy[\"Design_Start\"]\n ).dt.days\n df_copy[\"Schedule_Change\"] = (\n df_copy[\"Duration_End\"] - df_copy[\"Duration_Start\"]\n )\n df_copy[\"Budget_Change\"] = df_copy[\"Budget_End\"] - df_copy[\"Budget_Start\"]\n\n # define schedule change ratio\n df_copy[\"Schedule_Change_Ratio\"] = (\n df_copy[\"Schedule_Change\"] / df_copy[\"Duration_Start\"]\n )\n # define budget change ratio\n df_copy[\"Budget_Change_Ratio\"] = (\n df_copy[\"Budget_Change\"] / df_copy[\"Budget_Start\"]\n )\n\n # define project metrics\n df_copy[\"Budget_Abs_Per_Error\"] = (\n df_copy[\"Budget_Start\"] - df_copy[\"Budget_End\"]\n ).abs() / df_copy[\"Budget_End\"]\n\n df_copy[\"Budget_Rel_Per_Error\"] = (\n df_copy[\"Budget_Start\"] - df_copy[\"Budget_End\"]\n ).abs() / df_copy[\"Budget_Start\"]\n\n df_copy[\"Duration_End_Ratio\"] = (\n df_copy[\"Duration_End\"] / df_copy[\"Duration_Start\"]\n )\n df_copy[\"Budget_End_Ratio\"] = (\n df_copy[\"Budget_End\"] / df_copy[\"Budget_Start\"]\n )\n\n # previously titled 'Mark Metric'\n df_copy[\"Duration_Ratio_Inv\"] = (\n df_copy[\"Duration_Start\"] / df_copy[\"Duration_End\"]\n ) - 1\n df_copy[\"Budget_Ratio_Inv\"] = (\n df_copy[\"Budget_Start\"] / df_copy[\"Budget_End\"]\n ) - 1\n\n return df_copy"],"string":"[\n \"def get_predictions(year, month):\\n \\n start_date = str(year)+\\\"-\\\"+str(month)+\\\"-01\\\"\\n end_date = str(year)+\\\"-\\\"+str(month)+\\\"-\\\"+str(monthrange(year, month)[1])\\n\\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\\\"%Y-%m-%d\\\").tolist()\\n\\n # predictfunction \\n # do predictions\\n pred_arr = []\\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\\n \\n try:\\n predictions = load_predictions(file_name)\\n predictions = predictions.round()\\n except:\\n print(\\\"An exception occurred\\\")\\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\\n \\n \\n for index,row in predictions.iterrows():\\n \\n pred_mail = 0\\n pred_counter = 0\\n pred_tel = 0\\n \\n # check predictions dataframe for 'Datum'\\n if 'Datum' in predictions.columns:\\n date = row['Datum']\\n else:\\n break;\\n\\n # check predictions dataframe for 'Mail'\\n if 'Mail' in predictions.columns:\\n pred_mail = row['Mail']\\n\\n # check predictions dataframe for 'Schalter'\\n if 'Schalter' in predictions.columns:\\n pred_counter = row['Schalter']\\n\\n # check predictions dataframe for 'Tel'\\n if 'Tel' in predictions.columns:\\n pred_tel = row['Tel']\\n \\n \\n pred_dict = {'date': date, \\n 'predictions':{'mail' : pred_mail, \\n 'tel' : pred_tel, \\n 'counter' : pred_counter\\n }\\n }\\n\\n pred_arr.append(pred_dict)\\n\\n print(pred_arr) \\n \\n return pred_arr\",\n \"def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\\\\n columns=['month_year', 'predicted_evictions'])\\n\\n now = datetime.datetime.now()\\n\\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\\n model.fit()\\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\\n\\n predicted = y_hat.Eviction_Notice.values.tolist()\\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\\n temp_df['month_year']=months_list\\n\\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\\n predictions_df = predictions_df[1:]\\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\\n\\n return predictions_df, y_hat\",\n \"def top_down_forecast(original_df,predictions_by_month,months_ahead):\\n\\n group_by_zip_df = top_down_forecast_data_processing(original_df,predictions_by_month)\\n\\n zip_perc_df= pd.DataFrame(np.random.randn(1, 4), columns=['zip_predicted','zip_code','perc_of_month','month_year'])\\n\\n for month in range(months_ahead):\\n array_of_predictions = np.asarray([predictions_by_month.iloc[month]['predicted_evictions']]*group_by_zip_df.shape[0])\\n zip_predicted = np.asarray(group_by_zip_df.perc_of_month*array_of_predictions)\\n temp_df = pd.DataFrame({'total_evictions':array_of_predictions,'zip_predicted':zip_predicted,'zip_code':np.asarray(group_by_zip_df.Address_Zipcode), 'perc_of_month':np.asarray(group_by_zip_df.perc_of_month)})\\n temp_df['month_year']=predictions_by_month.iloc[month]['month_year']\\n zip_perc_df = zip_perc_df.append(temp_df,ignore_index=True)\\n\\n zip_perc_df = zip_perc_df[1:]\\n zip_perc_df['month_year']=pd.to_datetime(zip_perc_df['month_year'])\\n\\n return zip_perc_df\",\n \"def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\\n df.dropna(inplace=True)\\n ## day and month\\n df['Date'] = pd.to_datetime(df['Date'])\\n df['Month'] = df['Date'].dt.month\\n df['dayowk'] = df['Date'].dt.dayofweek\\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\\n \\n ##Previos n-day pct_changes\\n df['1day_pct'] = df['Adj Close'].pct_change()\\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\\n \\n ##Cumulative sum of 1day_pct\\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\\n \\n ##EWMA of 7, 50 and 200 days\\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\\n ## Golden Cross vs Death Cross etc.\\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n \\n ##RSI and MACD\\n df = RSI(df,14)\\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\\n \\n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\\n \\n ##Classification target\\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\\n \\n ## IS the stock Overbought or Oversold based on RSI?\\n df['RSI_overbought'] = df['RSI']>70\\n df['RSI_oversold'] = df['RSI']<30\\n \\n \\n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\\n# df = df.dropna()\\n \\n #df = df.reset_index(drop=True)\\n \\n ## Calculating how large the previos hot and cold streaks were\\n f = 0\\n df['prev_hot_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==1:\\n f += 1\\n if df['target'][i+1] ==0:\\n df['prev_hot_streak'][i+1] = f\\n f = 0\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_hot_streak'][i]==0:\\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\\n \\n \\n df['prev_cold_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==0:\\n f += 1\\n if df['target'][i+1] ==1:\\n df['prev_cold_streak'][i+1] = f\\n f = 0\\n\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_cold_streak'][i]==0:\\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\\n \\n ## Calculating current hot and cold streaks\\n df['current_hot_streak'] = np.zeros(df.shape[0])\\n df['current_cold_streak'] = np.zeros(df.shape[0])\\n fhot=0\\n fcold=0\\n for i in range(df.shape[0]):\\n if df['target'][i]==1:\\n fhot += 1\\n fcold = 0\\n df['current_hot_streak'][i] = fhot\\n elif df['target'][i]==0:\\n fcold += 1\\n fhot = 0\\n df['current_cold_streak'][i] = fcold\\n \\n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\\n \\n ## Combinations of previos streaks\\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\\n \\n ##Calculating days since max\\n current_max = df['Adj Close'][0]\\n df['days_from_max'] = np.zeros(df.shape[0])\\n df['pct_from_max'] = np.zeros(df.shape[0])\\n #print('blah')\\n for i in range(1,df.shape[0]):\\n if df['Adj Close'][i] > current_max:\\n current_max = df['Adj Close'][i]\\n # print(current_max)\\n else:\\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\\n #print(df['days_from_max'][i])\\n \\n \\n \\n df.dropna(inplace=True)\\n df = df.reset_index(drop=True)\\n return df\",\n \"def engineer_features(dataframe, holiday_dates, columns, time_lags=24, \\n steps_ahead=1, drop_nan_rows=True):\\n \\n # Make a copy of the original dataframe\\n data = dataframe[columns].copy()\\n \\n # Features engineering\\n for col in data.columns:\\n for i in range(1, time_lags+1):\\n # Shift data by lag of 1 to time_lags (default: 24) hours\\n data[col+'_{:d}h'.format(i)] = data[col].shift(periods=i) # time-lag\\n data[col+'_diff'] = data[col].diff() # first-difference\\n data[col+'_week'] = data[col].shift(periods=24*7) # previous week\\n \\n # Hour-of-day indicators with cyclical transform\\n dayhour_ind = data.index.hour\\n data['hr_sin'] = np.sin(dayhour_ind*(2.*np.pi/24))\\n data['hr_cos'] = np.cos(dayhour_ind*(2.*np.pi/24))\\n \\n # Day-of-week indicators with cyclical transform\\n weekday_ind = data.index.weekday\\n data['week_sin'] = np.sin(weekday_ind*(2.*np.pi/7))\\n data['week_cos'] = np.cos(weekday_ind*(2.*np.pi/7))\\n\\n # Weekend as a binary indicator\\n data['weekend'] = np.asarray([0 if ind <= 4 else 1 for ind in weekday_ind])\\n\\n # Month indicators with cyclical transform\\n month_ind = data.index.month\\n data['mnth_sin'] = np.sin((month_ind-1)*(2.*np.pi/12))\\n data['mnth_cos'] = np.cos((month_ind-1)*(2.*np.pi/12))\\n \\n # Holidays as a binary indicator\\n data['holidays'] = 0\\n for holiday, date in holiday_dates.items():\\n if date[1] is None:\\n # Single day\\n data.loc[date[0], 'holidays'] = 1\\n else:\\n # Date range\\n data.loc[date[0]:date[1], 'holidays'] = 1\\n \\n # Forecast horizont\\n if steps_ahead == 1:\\n # Single-step forecasting\\n data['Load+0h'] = data['Load'].values\\n else:\\n # Multi-step forecasting\\n for i in range(steps_ahead):\\n data['Load'+'+{:d}h'.format(i)] = data['Load'].shift(-i)\\n del data['Load']\\n \\n if drop_nan_rows:\\n # Drop rows with NaN values\\n data.dropna(inplace=True)\\n \\n return data\",\n \"def predictions_for_df(df, inferrer):\\n working_df = df.copy()\\n working_df['predictions'] = inferrer.get_activations(\\n working_df.sequence.values).tolist()\\n return working_df\",\n \"def top_down_forecast_data_processing(original_df,predictions_by_month):\\n transformed_df=original_df.groupby('Month_Year').sum().reset_index()\\\\\\n [['Month_Year','Eviction_Notice']]\\n\\n percentage_of_month_df = pd.merge(original_df[['Month_Year','Address_Zipcode',\\\\\\n 'Eviction_Notice']],transformed_df,how = 'left',left_on='Month_Year',\\\\\\n right_on='Month_Year',suffixes=('','_for_month'))\\n\\n percentage_of_month_df.dropna(inplace=True)\\n\\n percentage_of_month_df['perc_of_month']=\\\\\\n percentage_of_month_df['Eviction_Notice']/percentage_of_month_df['Eviction_Notice_for_month']\\n\\n group_by_zip_df = percentage_of_month_df.groupby('Address_Zipcode').mean().reset_index()[['Address_Zipcode','perc_of_month']]\\n\\n return group_by_zip_df\",\n \"def feature_engineer_ts(self, month=12):\\n st_data_dt = self.get_st_data_dt()\\n end_data_dt = self.get_end_data_dt()\\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n population = self.get_population()\\n is_raw_partition = self.get_is_raw_partition()\\n# Lag 2 months\\n all_data = []\\n# join past is_raw columns\\n for d in date_list:\\n \\n population_partition = population[population['ft_data_dt'] == d] \\n old_date = d - relativedelta(months=month)\\n y = old_date.year\\n m = old_date.month\\n day = calendar.monthrange(y, m)[1]\\n old_date = date(y, m, day)\\n old_date = max(old_date, st_data_dt)\\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n date_list_join.reverse()\\n for index, date_join in enumerate(date_list_join):\\n if date_join.strftime(\\\"%Y-%m-%d\\\") not in is_raw_partition.keys():\\n continue\\n \\n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\\\"%Y-%m-%d\\\")]\\n \\n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\\n new_col = [c+'_'+str(index+1) for c in rename_col]\\n name_dict = dict(list(zip(rename_col, new_col)))\\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\\n all_data.append(population_partition)\\n ts_df = pd.concat(all_data)\\n threshold_null = len(ts_df.columns) - 4\\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\\n \\n def sum_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_sum_'+str(duration)+'mth'\\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def mean_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_avg_'+str(duration)+'mth'\\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def std_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_std_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def med_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_med_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def min_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_min_'+str(duration)+'mth'\\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def max_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_max_'+str(duration)+'mth'\\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q1_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q1_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q3_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q3_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def last_ts(self, df, feature):\\n ft_name = feature+ '_last'\\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\\n return tmp_df\\n \\n ts_duration = [1, 3, 6, 9, 12]\\n feature_list = self.get_is_raw_col()\\n df = ts_df[['idd', 'ft_data_dt']]\\n# Time Series Features\\n for duration in ts_duration:\\n for col in feature_list:\\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\\n df = pd.concat([df\\\\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\\\\n , med_ts(self, ts_df, col_list, col, duration)\\\\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\\\\n , min_ts(self, ts_df, col_list, col, duration)\\\\\\n , max_ts(self, ts_df, col_list, col, duration)]\\n , axis=1)\\n self.set_all_data(df)\",\n \"def _add_lagged_features(self, X: pd.DataFrame, lags: list) -> pd.DataFrame:\\n for l in lags:\\n X[f'sales_lag_{l + self.shift_days}'] = (X[['id', 'sales', 'd']]\\n .groupby('id')['sales']\\n .transform(lambda x: x.shift(l + self.shift_days))\\n .fillna(0))\\n return X\",\n \"def create_shifted_orderbook_more_feature(ticker, start_date, end_date, lag_period = 5, pred_period = 7):\\n # Retrieve the Nifty data from Yahoo finance:\\n format = '%Y-%m-%d' # Formatting directives\\n start = start_date.strftime(format)\\n end = end_date.strftime(format)\\n\\n yf.pdr_override() # <== that's all it takes :-)\\n stock_data = pdr.get_data_yahoo(ticker, start=start, end=end)\\n\\n # Creates stock lag\\n stock_data.dropna()\\n stock_lag = pd.DataFrame(data = stock_data, index=stock_data.index)\\n\\n stock_returns = pd.DataFrame(data = stock_data, index=stock_data.index)\\n\\n # Initializes dataframe values and smooths the closing price data\\n stock_data_smooth = stock_data['Adj Close']\\n exponential_smoothing(0.7, stock_data_smooth) #so the stock_data_smooth is smoothing\\n\\n\\n #stock_lag['Volume'] = stock_returns['Volume'] = stock_data['Volume']\\n stock_lag[\\\"Close\\\"] = stock_data_smooth #so, now the stock_lag[\\\"Close\\\"] is derive from Adj Close + smoothing.\\n #print stock_lag[\\\"Close\\\"]\\n\\n # Sets lagging price data (previous days' price data as feature inputs)\\n for i in range(0, lag_period):\\n column_label = 'Lag{:d}'.format(i)\\n stock_lag[column_label] = stock_lag['Close'].shift(1+i)\\n\\n # EMA- Momentum\\n #stock_lag['EMA'] = talib.EMA(close, timeperiod = 30)\\n ndays = 30\\n name_EWMA = 'EWMA_' + str(ndays)\\n stock_lag['EWMA_'] = EWMA(stock_lag,ndays )[name_EWMA]\\n\\n # Bollinger Bands\\n #stock_lag['upperband'], stock_lag['middleband'], stock_lag['lowerband'] = talib.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)\\n aa = BBANDS(stock_lag, ndays=30)\\n stock_lag['upperband'] = aa['Upper BollingerBand']\\n stock_lag['lowerband'] = aa['Lower BollingerBand']\\n\\n # StochK\\n #stock_lag['slowk'], stock_lag['slowd'] = talib.STOCH(high, low, close, fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3,\\n # slowd_matype=0)\\n n = 30\\n name_slowk = 'SO%k'\\n name_slowd = 'SO%d_' + str(n)\\n stock_lag['slowk'] = STOK(stock_lag)[name_slowk]\\n stock_lag['slowd'] = STO(stock_lag, n)[name_slowd]\\n\\n # MACD- Momentum\\n #macd, macdsignal, stock_lag['macdhist'] = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)\\n n_fast = 12\\n n_slow = 26\\n name_macd = 'MACD_' + str(n_fast) + '_' + str(n_slow)\\n name_macdsignal = 'MACDsign_' + str(n_fast) + '_' + str(n_slow)\\n name_macdhist = 'MACDdiff_' + str(n_fast) + '_' + str(n_slow)\\n macd = MACD(stock_lag, n_fast, n_slow)[name_macd]\\n macdsignal = MACD(stock_lag, n_fast, n_slow)[name_macdsignal]\\n stock_lag['macdhist'] = MACD(stock_lag, n_fast, n_slow)[name_macdhist]\\n\\n # CCI- Momentum\\n #stock_lag['CCI'] = talib.CCI(high, low, close)\\n stock_lag['CCI'] = CCI(stock_lag, ndays = 30)[\\\"CCI\\\"]\\n #print stock_lag['CCI']\\n\\n # # RSI- Momentum\\n # #stock_lag['RSI'] = talib.RSI(close, timeperiod=14)\\n # ndays = 14\\n # name_RSI = 'RSI_' + str(ndays)\\n # stock_lag['RSI'] = RSI(stock_lag, n = ndays)[name_RSI]\\n # #print stock_lag['RSI']\\n\\n\\n # Chaikin- Volume\\n #stock_lag['Chaikin'] = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)\\n stock_lag['Chaikin'] = Chaikin(stock_lag)['Chaikin']\\n #print stock_lag['Chaikin']\\n\\n stock_returns['Day Returns'] = stock_data['Adj Close'].pct_change() * 100\\n # Sets lagging percent change data\\n for i in range(0, lag_period):\\n column_label = 'Lag{:d}'.format(i)\\n stock_returns[column_label] = stock_lag[column_label].pct_change() * 100\\n\\n\\n # Remove NaN's from stock lag\\n print \\\"shape of stock_lag before dropna: \\\",stock_lag.shape[0]\\n stock_lag = stock_lag.dropna(axis=0, how='any')\\n print \\\"shape of stock_lag before dropna: \\\",stock_lag.shape[0]\\n\\n print \\\"shape of stock_returns before dropna: \\\",stock_returns.shape[0]\\n # Adjusts stock_return data to same length as stock_lag\\n stock_returns = stock_returns.tail(stock_lag.shape[0])\\n print \\\"shape of stock_returns after dropna: \\\",stock_returns.shape[0]\\n\\n\\n # Determine stock movement direction and lagging movement\\n stock_movement = pd.DataFrame(index=stock_returns.index)\\n stock_movement['Movement_0'] = np.sign(stock_returns['Day Returns'])\\n stock_movement['Movement_0'][0] = 1\\n for i in range(0, pred_period):\\n column_label = 'Movement_{:d}'.format(i + 1)\\n stock_movement[column_label] = stock_movement['Movement_0'].shift(i + 1)\\n\\n # Removes NaNs from 'stock_movement' and resizes 'stocks_returns' and 'stock_lag' accordingly\\n print \\\"shape of stock_movement before dropna: \\\",stock_movement.shape[0]\\n stock_movement = stock_movement.dropna(axis=0, how='any')\\n print \\\"shape of stock_movement after dropna: \\\",stock_movement.shape[0]\\n\\n stock_returns = stock_returns[stock_returns.index <= stock_movement.index[stock_movement.index.__len__() - 1]]\\n stock_returns = stock_returns.tail(stock_movement.shape[0])\\n stock_lag = stock_lag[stock_lag.index <= stock_movement.index[stock_movement.index.__len__() - 1]]\\n stock_lag = stock_lag.tail(stock_movement.shape[0])\\n\\n return stock_data, stock_returns, stock_lag, stock_movement\",\n \"def add_feat_predict(df_in, rolling_win_size=15):\\n cols =['Turbine_ID', 'Date']\\n other_cols = []\\n for i in df_in.columns:\\n if i not in cols:\\n other_cols.append(i)\\n all_cols = cols + other_cols\\n\\n df_in = df_in[all_cols]\\n\\n sensor_cols = []\\n for i in df_in.columns[2:]:\\n sensor_cols.append(i)\\n\\n sensor_av_cols = [nm+'_av' for nm in sensor_cols]\\n sensor_sd_cols = [nm+'_sd' for nm in sensor_cols]\\n\\n df_out = pd.DataFrame()\\n\\n ws = rolling_win_size\\n\\n #calculate rolling stats for each engine id\\n\\n for m_id in pd.unique(df_in.Turbine_ID):\\n\\n # get a subset for each engine sensors\\n df_engine = df_in[df_in['Turbine_ID'] == m_id]\\n df_sub = df_engine[sensor_cols]\\n\\n # get rolling mean for the subset\\n av = df_sub.rolling(ws, min_periods=1).mean()\\n av.columns = sensor_av_cols\\n\\n # get the rolling standard deviation for the subset\\n sd = df_sub.rolling(ws, min_periods=1).std().fillna(0)\\n sd.columns = sensor_sd_cols\\n\\n # combine the two new subset dataframes columns to the engine subset\\n new_ftrs = pd.concat([df_engine,av,sd], axis=1)\\n\\n # add the new features rows to the output dataframe\\n df_out = pd.concat([df_out,new_ftrs])\\n df_out = df_out.sort_values(by=['Turbine_ID', 'Date'] )\\n return df_out\",\n \"def predict(self, next_days):\\n last_date = self.series.index[-1]\\n for time in range(next_days):\\n row = {}\\n for c in self.countries:\\n history = self.series[c]\\n series = history.astype(float)\\n model = ARIMA(series, order=(2,1,0))\\n model_fit = model.fit()\\n output = model_fit.forecast()\\n row[c] = output[0][0]\\n last_date = last_date + pd.DateOffset(1)\\n self.series.loc[last_date] = row\",\n \"def store_predictions(self, preds, df, feature):\\n\\n prev_values = list(df[feature].iloc[:len(df) - len(self.players)])\\n prev_values.extend(preds)\\n\\n df[feature] = prev_values\\n\\n return df\",\n \"def test_model_avg_3_weeks_bt(df_y_train, df_y_predict, df_y_ground_truth_scaled, predict_period, n_samples_week, target):\\n\\n # Use average of last 3 weeks (for same time stamps) as predicted values\\n df_hist = pd.concat([df_y_train, df_y_ground_truth_scaled], 0)\\n df_hist_1week = df_hist.shift(n_samples_week)\\n df_hist_2week = df_hist.shift(2*n_samples_week)\\n df_hist_3week = df_hist.shift(3*n_samples_week)\\n df_hist_all = pd.concat([df_hist_1week, df_hist_2week, df_hist_3week], 1)\\n df_hist_all = df_hist_all[df_hist_all.index.isin(df_y_predict.index)]\\n \\n if target == \\\"count\\\":\\n # Average\\n prediction = df_hist_all.mean(axis = 1)\\n elif target == \\\"level\\\":\\n # Majority class\\n prediction = df_hist_all.mode(axis = 1).iloc[:, 0]\\n \\n return prediction\",\n \"def store_predictions(df):\\n ts = df[df.columns[1]]\\n base = pd.DataFrame(ts)\\n preds = make_preds(ts, 'Predicted '+ df.columns[1])\\n base.index = df['year']\\n base = base.append(pd.DataFrame(preds), sort = True)\\n for col in df.columns[2:]:\\n ts = df[col]\\n temp = pd.DataFrame(ts)\\n preds = make_preds(ts, 'Predicted ' + col)\\n temp.index = df['year']\\n temp = temp.append(pd.DataFrame(preds), sort = True)\\n base = base.join(temp)\\n return base\",\n \"def get_dict_X_dynamic(ar_iterations, forecast_cycle, input_k):\\n dict_X_past = {}\\n for i in range(ar_iterations + 1):\\n idxs = get_idx_lag(\\n idx_start=0, ar_iteration=i, forecast_cycle=forecast_cycle, input_k=input_k\\n )\\n idxs_past_data = idxs[idxs < 0]\\n # Indexes of past data\\n if idxs_past_data.size == 0:\\n dict_X_past[i] = None\\n else:\\n dict_X_past[i] = idxs_past_data\\n return dict_X_past\",\n \"def get_pre_df(temp_pre_df):\\n \\n event_time_max = temp_pre_df['event_time'].max()\\n cat_dfs = []\\n for num in np.arange(0,(1080/2)+1,30)[1:]:\\n # making <= null i.e keeping >\\n temp_pre_df.loc[temp_pre_df['event_time'] <= int(num), 'event_time'] = np.nan\\n for col in ['event_name', 'specialty', 'plan_type']:\\n cat_df = temp_pre_df.groupby([\\\"id\\\", col]).agg({\\\"event_time\\\": 'count'}).unstack(level=col)\\n cat_df = cat_df/(event_time_max-num)\\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\\n cat_dfs.append(cat_df)\\n pre_df = pd.concat(cat_dfs, axis = 1) \\n return pre_df.fillna(0)\",\n \"def month_lag_distribution(source_df, field=\\\"month_lag\\\", path=path.path, nrows=None):\\n _log.info(\\\"Creating features from {}\\\".format(field))\\n prefix = source_df.split(\\\"_\\\")[0]\\n source_df = \\\"{}/{}\\\".format(path, source_df)\\n\\n _log.info(\\\"Reading from {}\\\".format(source_df))\\n try:\\n df = pd.read_csv(source_df, usecols=[\\\"card_id\\\", field], nrows=nrows)\\n _log.info(\\\"Successfully read from {}\\\".format(source_df))\\n except Exception as e:\\n _log.exception(e)\\n\\n _log.info(\\\"Computing distribution of month lag\\\")\\n func_to_be_applied = [min, max, pd.Series.nunique]\\n func_to_be_applied_dummy = [max, np.mean]\\n rename_dict = create_rename_dict(prefix, field, func_to_be_applied)\\n rename_dict_dummy = create_rename_dict(prefix, \\\"dummy\\\", func_to_be_applied_dummy)\\n\\n df[\\\"dummy\\\"] = 1\\n df_features = df.groupby(\\\"card_id\\\").agg({field:func_to_be_applied}).reset_index()\\n df_features = pd.concat([pd.DataFrame(df_features[\\\"card_id\\\"]), df_features[field]], axis=1, sort=False)\\n\\n _log.info(\\\"Renaming columns: {}\\\".format(rename_dict))\\n df_features.rename(columns=rename_dict, inplace=True)\\n\\n _log.info(\\\"Computing time in month between transactions\\\")\\n df_freq = (df.groupby([\\\"card_id\\\", field]).agg({\\\"dummy\\\": np.sum}).reset_index().groupby(\\\"card_id\\\")\\n .agg({\\\"dummy\\\": func_to_be_applied_dummy}).reset_index())\\n df_freq = pd.concat([pd.DataFrame(df_freq[\\\"card_id\\\"]), df_freq[\\\"dummy\\\"]], axis=1, sort=False)\\n df_freq.rename(columns=rename_dict_dummy, inplace=True)\\n\\n _log.info(\\\"Creating final df\\\")\\n df_features = df_features.merge(df_freq, how=\\\"inner\\\", on=\\\"card_id\\\")\\n return df_features\",\n \"def __init__(self, data_frame, mins_set):\\n # super(FeaturePrevDelays, self).__init__()\\n self.df = data_frame.copy()\\n self.mins_set = mins_set\",\n \"def new_features(df):\\n print(\\\"Add new features ...\\\")\\n # distinguish Spring, Fall and pregnant females (don't care about juvenilles/unknown)\\n df[\\\"gender_plus\\\"] = df[\\\"Gender\\\"]\\n df.loc[df.Gravid, \\\"gender_plus\\\"] = \\\"f_gra\\\"\\n\\n df[\\\"gender_seasons\\\"] = df[\\\"Gender\\\"]\\n df.loc[df.Gravid, \\\"gender_seasons\\\"] = \\\"f_gra\\\"\\n\\n # add features\\n df[\\\"Age_To_Weight\\\"] = df[\\\"Annuli\\\"] / df[\\\"Weight\\\"]\\n\\n # Calcuate Number of recaptures\\n df_captures = df[[\\\"ID\\\", \\\"Date\\\"]].groupby(\\\"ID\\\").count()\\n df_captures.columns = [\\\"recapture_count\\\"]\\n df_captures.reset_index(inplace=True)\\n df = pd.merge(df, df_captures, how=\\\"outer\\\", on=\\\"ID\\\")\\n\\n # recalculate annuli\\n df_min = pd.pivot_table(\\n df[df.Annuli > 0],\\n values=[\\\"Date\\\", \\\"Annuli\\\"],\\n index=[\\\"ID\\\"],\\n aggfunc={\\\"Date\\\": min, \\\"Annuli\\\": min},\\n )\\n df_min.columns = [\\\"annuli_min\\\", \\\"date_min\\\"]\\n df_min.reset_index(inplace=True)\\n\\n df = pd.merge(df, df_min, how=\\\"outer\\\", on=\\\"ID\\\")\\n df[\\\"year\\\"] = df.Date.map(lambda x: x.year)\\n df[\\\"year_min\\\"] = df.date_min.map(lambda x: x.year)\\n df[\\\"Annuli_orig\\\"] = df.Annuli\\n df.Annuli = df.year - df.year_min + df.annuli_min\\n df.Annuli = np.nan_to_num(df.Annuli)\\n df[\\\"Annuli\\\"] = pd.to_numeric(df[\\\"Annuli\\\"], downcast=\\\"integer\\\")\\n\\n # Annuli Buckets\\n buckets = 5\\n interval = int(df[\\\"Annuli\\\"].max() / buckets)\\n buckets = [i for i in range(0, df[\\\"Annuli\\\"].max() + interval, interval)]\\n labels = [\\\"'{0} - {1}'\\\".format(i, i + interval) for i in buckets]\\n df[\\\"Annuli_Group\\\"] = pd.cut(\\n df.Annuli, buckets, labels=labels[:-1], include_lowest=True\\n )\\n\\n return df\",\n \"def generate_time_series_df(eviction_df):\\n evictions_by_month = get_counts_by_month(eviction_df, \\\"month\\\", \\\"total-eviction-filings\\\")\\n timeseries_df = evictions_by_month\\n return timeseries_df\",\n \"def apply_all_transformations(df):\\n df[\\\"age_quantile\\\"] = compute_age_quantile(df, 5)\\n df[\\\"competition_open\\\"] = compute_competition_open(df)\\n df[\\\"count\\\"] = df.groupby(\\\"store\\\")[[\\\"date\\\"]].transform(\\\"cumcount\\\")\\n df[\\\"open_lag_1\\\"] = compute_open_lag(df)\\n df[\\\"open_lag_2\\\"] = compute_open_lag(df, 2)\\n df[\\\"open_lag_3\\\"] = compute_open_lag(df, 3)\\n df[\\\"open_lead_1\\\"] = compute_open_lead(df)\\n df[\\\"open_lead_2\\\"] = compute_open_lead(df, -2)\\n df[\\\"open_lead_3\\\"] = compute_open_lead(df, -3)\\n df[\\\"promo_lag_1\\\"] = compute_promo_lag(df)\\n df[\\\"promo_lag_2\\\"] = compute_promo_lag(df, 2)\\n df[\\\"promo_lag_3\\\"] = compute_promo_lag(df, 3)\\n df[\\\"promo_lead_1\\\"] = compute_promo_lead(df)\\n df[\\\"promo_lead_2\\\"] = compute_promo_lead(df, -2)\\n df[\\\"promo_lead_3\\\"] = compute_promo_lead(df, -3)\\n df[\\\"school_holiday_lag_1\\\"] = compute_school_holiday_lag(df)\\n df[\\\"school_holiday_lag_2\\\"] = compute_school_holiday_lag(df, 2)\\n df[\\\"school_holiday_lag_3\\\"] = compute_school_holiday_lag(df, 3)\\n df[\\\"school_holiday_lead_1\\\"] = compute_school_holiday_lead(df)\\n df[\\\"school_holiday_lead_2\\\"] = compute_school_holiday_lead(df, -2)\\n df[\\\"school_holiday_lead_3\\\"] = compute_school_holiday_lead(df, -3)\\n df[\\\"state_holiday_lag_1\\\"] = compute_state_holiday_lag(df)\\n df[\\\"state_holiday_lag_2\\\"] = compute_state_holiday_lag(df, 2)\\n df[\\\"state_holiday_lag_3\\\"] = compute_state_holiday_lag(df, 3)\\n df[\\\"state_holiday_lead_1\\\"] = compute_state_holiday_lead(df)\\n df[\\\"state_holiday_lead_2\\\"] = compute_state_holiday_lead(df, -2)\\n df[\\\"state_holiday_lead_3\\\"] = compute_state_holiday_lead(df, -3)\\n df[\\\"store_day_of_week\\\"] = compute_store_day_of_week(df)\\n df[\\\"store_month\\\"] = compute_store_month(df)\\n df[\\\"store_school_holiday\\\"] = compute_store_school_holiday(df)\\n df[\\\"store_state_holiday\\\"] = compute_store_state_holiday(df)\\n df[\\\"store_year\\\"] = compute_store_year(df)\\n df[\\\"zscore\\\"] = compute_zscore(df, window=150)\\n\\n return df\",\n \"def preprocess_data_pandas(raw_data_file: str, features_file: str, cols_to_save: List[str]) -> None:\\n\\n df = pd.read_csv(raw_data_file)\\n\\n df.sort_values(by=[\\\"id\\\", \\\"loan_date\\\"], inplace=True)\\n df.reset_index(drop=True, inplace=True)\\n\\n df[\\\"loan_date\\\"] = pd.to_datetime(df['loan_date'], errors='coerce')\\n df[\\\"birthday\\\"] = pd.to_datetime(df['birthday'], errors='coerce')\\n df[\\\"job_start_date\\\"] = pd.to_datetime(df['job_start_date'], errors='coerce')\\n\\n df_grouped_by_id = df.groupby('id')\\n\\n # Feature nb_previous_loans\\n df[\\\"nb_previous_loans\\\"] = df_grouped_by_id[\\\"loan_date\\\"].rank(method=\\\"first\\\") - 1\\n\\n # Feature avg_amount_loans_previous\\n df[\\\"avg_amount_loans_previous\\\"] = df_grouped_by_id[\\\"loan_amount\\\"].transform(lambda x: x.expanding().mean())\\n\\n # Feature age\\n df['age'] = (pd.to_datetime('today').normalize() - df['birthday']).dt.days // 365\\n\\n # Feature years_on_the_job\\n df['years_on_the_job'] = (pd.to_datetime('today').normalize() - df['job_start_date']).dt.days // 365\\n\\n # Feature flag_own_car\\n df['flag_own_car'] = df.flag_own_car.apply(lambda x: 0 if x == 'N' else 1)\\n\\n df = df[cols_to_save]\\n df.to_csv(features_file, index=False)\",\n \"def create_feats_and_preds(price_df, feat_days, pred_days):\\n\\n # create shifted percent features\\n df_feats = price_n_days_out(price_df, days=feat_days)\\n df_help = df_feats.copy()[['ticker', 'prediction_date', f'price_{feat_days}_out']]\\n df_help.columns = ['ticker', 'date', 'close']\\n df_preds = price_n_days_out(df_help, days=pred_days)\\n\\n # do some cleaning\\n full_df = pd.merge(df_feats, df_preds, left_on=['ticker', 'prediction_date'], right_on=['ticker', 'date'])\\n full_df.columns = ['ticker', 'past_date', 'past_close', 'current_date', 'current_price',\\n 'percent_change_feat', 'date_y', 'close_y', 'prediction_date',\\n 'price_5_out_y', 'percent_change_pred']\\n full_df = full_df[['ticker', 'past_date', 'current_date', 'prediction_date',\\n 'percent_change_feat', 'percent_change_pred']]\\n return full_df\",\n \"def ml_pred_post(ticker=None, stock_last_day=None, train_pred_df=None, val_pred_df=None, test_pred_df=None, \\nX_features=None, y_features=None, n_past_days=90, n_future_days=5, loss='mean_squared_error', lr=0.01, epochs=12, batch_size=32, RMSE_train_pred=None, RMSE_val_pred=None):\\n\\n try:\\n train_pred_df_dict = train_pred_df.to_dict(orient='records')\\n val_pred_df_dict = val_pred_df.to_dict(orient='records')\\n test_pred_df_dict = test_pred_df.to_dict(orient='records')\\n \\n n_X_features = len(X_features)\\n n_y_features = len(y_features)\\n\\n pred_post_to_upload = {\\n 'Stock': ticker,\\n 'Datetime': stock_last_day,\\n 'X_features': X_features,\\n 'y_features': y_features,\\n 'n_past_days': n_past_days,\\n 'n_future_days': n_future_days,\\n 'ML Model': {\\n 'model': 'LSTM',\\n 'parameters': {\\n 'layers': {'LSTM_1 units': 64,\\n 'LSTM_1 input_shape': (n_past_days, n_X_features),\\n 'LSTM_2 units': 32,\\n 'Dropout': 0.2,\\n 'Dense units': n_future_days*n_y_features\\n },\\n 'compile': {\\n 'optimizer': 'Adam',\\n 'loss': str(loss),\\n 'lr': lr\\n },\\n 'fit': {\\n 'epochs': epochs,\\n 'batch_size': batch_size\\n } \\n },\\n\\n },\\n 'RMSE_train_pred': float(RMSE_train_pred),\\n 'RMSE_val_pred': float(RMSE_val_pred),\\n 'train_pred': train_pred_df_dict,\\n 'val_pred': val_pred_df_dict,\\n 'test_pred': test_pred_df_dict\\n }\\n return pred_post_to_upload\\n\\n except Exception as e:\\n print('Something went wrong...')\\n print(f'str(e) = {str(e)}')\\n # print(f'repr(e) = {repr(e)}')\\n\\n # return pred_post_to_upload\",\n \"def _predict(self):\\n bull_wr = self.bull_win_rate\\n self.df = self.df.assign(bull_wr=bull_wr)\",\n \"def lagged_features(df, lags):\\n df_list = []\\n for lag in lags:\\n df_shifted = df.shift(lag)\\n df_shifted.columns = [x + \\\"_lag\\\" + str(lag) for x in df_shifted.columns]\\n df_list.append(df_shifted)\\n fea = pd.concat(df_list, axis=1)\\n return fea\",\n \"def prepareInputs(df: \\\"pd.dataFrame\\\") -> \\\"pd.dataFrame\\\":\\n \\n # 1. Exclude missing values\\n df = df[df[\\\"POL_STATUS\\\"].notnull()]\\n \\n # 2. Clean the target variable\\n df = df[df[\\\"POL_STATUS\\\"] != \\\"Unknown\\\"]\\n df[\\\"lapse\\\"] = np.where(df[\\\"POL_STATUS\\\"] == \\\"Lapsed\\\", 1, 0)\\n \\n # 3. Create dummy variables for categorical variables\\n categorical_cols = [\\\"CLAIM3YEARS\\\", \\\"BUS_USE\\\", \\\"AD_BUILDINGS\\\",\\n \\\"APPR_ALARM\\\", \\\"CONTENTS_COVER\\\", \\\"P1_SEX\\\",\\n \\\"BUILDINGS_COVER\\\", \\\"P1_POLICY_REFUSED\\\", \\n \\\"APPR_ALARM\\\", \\\"APPR_LOCKS\\\", \\\"FLOODING\\\",\\n \\\"NEIGH_WATCH\\\", \\\"SAFE_INSTALLED\\\", \\\"SEC_DISC_REQ\\\",\\n \\\"SUBSIDENCE\\\", \\\"LEGAL_ADDON_POST_REN\\\", \\n \\\"HOME_EM_ADDON_PRE_REN\\\",\\\"HOME_EM_ADDON_POST_REN\\\", \\n \\\"GARDEN_ADDON_PRE_REN\\\", \\\"GARDEN_ADDON_POST_REN\\\", \\n \\\"KEYCARE_ADDON_PRE_REN\\\", \\\"KEYCARE_ADDON_POST_REN\\\", \\n \\\"HP1_ADDON_PRE_REN\\\", \\\"HP1_ADDON_POST_REN\\\",\\n \\\"HP2_ADDON_PRE_REN\\\", \\\"HP2_ADDON_POST_REN\\\", \\n \\\"HP3_ADDON_PRE_REN\\\", \\\"HP3_ADDON_POST_REN\\\", \\n \\\"MTA_FLAG\\\", 'OCC_STATUS', 'OWNERSHIP_TYPE',\\n 'PROP_TYPE', 'PAYMENT_METHOD', \\\"P1_EMP_STATUS\\\",\\n \\\"P1_MAR_STATUS\\\"\\n ]\\n \\n for col in categorical_cols:\\n dummies = pd.get_dummies(df[col], \\n drop_first = True,\\n prefix = col\\n )\\n df = pd.concat([df, dummies], 1)\\n \\n # 4. Create age features\\n df[\\\"age\\\"] = (datetime.strptime(\\\"2013-01-01\\\", \\\"%Y-%m-%d\\\") - pd.to_datetime(df[\\\"P1_DOB\\\"])).dt.days // 365\\n df[\\\"property_age\\\"] = 2013 - df[\\\"YEARBUILT\\\"]\\n df[\\\"cover_length\\\"] = 2013 - pd.to_datetime(df[\\\"COVER_START\\\"]).dt.year\\n \\n # 5. Impute missing value\\n df[\\\"RISK_RATED_AREA_B_imputed\\\"] = df[\\\"RISK_RATED_AREA_B\\\"].fillna(df[\\\"RISK_RATED_AREA_B\\\"].mean())\\n df[\\\"RISK_RATED_AREA_C_imputed\\\"] = df[\\\"RISK_RATED_AREA_C\\\"].fillna(df[\\\"RISK_RATED_AREA_C\\\"].mean())\\n\\n return df\",\n \"def alspostprocess(data, prediction, features, user_features, movie_features, n_features=10):\\r\\n \\r\\n\\r\\n data['ALS'] = prediction[data.loc[:, 'userID']-1, data.loc[:, 'movieID']-1]\\r\\n features.append('ALS')\\r\\n \\r\\n total_features = len(movie_features)\\r\\n if n_features>total_features:\\r\\n n_features = total_features\\r\\n \\r\\n for i in range(n_features):\\r\\n data[\\\"UserFeature{}\\\".format(i)] = user_features[data.loc[:, 'userID']-1, i]\\r\\n features.append(\\\"UserFeature{}\\\".format(i))\\r\\n data[\\\"MovieFeature{}\\\".format(i)] = movie_features[i, data.loc[:, 'movieID']-1]\\r\\n features.append(\\\"MovieFeature{}\\\".format(i))\\r\\n return data, features\",\n \"def rank_pre_extract(self, mention_data, predictions):\\n mdata = pd.DataFrame(mention_data)\\n\\n\\n\\n\\n pass\",\n \"def lagger(\\n dataset:pd.DataFrame, n_lags:int,\\n price_columns : Union[str,List[str]]) -> pd.DataFrame:\\n from toolz.curried import reduce\\n df = reduce(\\n lambda df, lag: df.assign(**{col + '_' +str(lag): dataset[[col]].shift(lag).values for col in price_columns}),\\n range(1, n_lags + 1),\\n dataset[price_columns])\\n\\n result = df.assign(**{col: dataset[col] for col in dataset.drop(price_columns, axis=1).columns}).fillna(0)\\n return result[sorted(result.columns)]\",\n \"def samples_timesteps_features(dataframe, columns, start_date, timesteps=72, \\n steps_ahead=24, window_days=100, train_percent=80.):\\n \\n def overlap_windows(dataset, timesteps, steps_ahead):\\n \\\"\\\"\\\" Create overlaping window of time-series data\\n \\n Parameters\\n ----------\\n dataset: pd.DataFrame\\n time-series pandas dataset\\n timesteps: int\\n number of time steps from the past for creating output arrays\\n steps_ahead: int\\n number of time steps into the future for making predictions\\n \\n Returns\\n -------\\n X, y: np.array\\n input and output 3-d arrays of overlaping time windows\\n \\\"\\\"\\\"\\n X = []; y = []\\n \\n start = 0\\n for i in range(len(dataset)):\\n # Define the end of the input sequence\\n in_end = start + timesteps\\n out_end = in_end + steps_ahead\\n # Ensure that there is enough data\\n if out_end <= len(dataset):\\n X.append(dataset[start:in_end, :])\\n # First column holds load values\\n y.append(dataset[in_end:out_end, 0])\\n # Move along one time step\\n start += 1\\n \\n # Convert list to np.array\\n X = np.asarray(X)\\n y = np.asarray(y)\\n \\n return X, y\\n\\n\\n data = dataframe.copy()\\n \\n if window_days*24 > data.values.shape[0]:\\n raise ValueError('Variable window_days has too large value: {}*24h = {} > {}, which is more than there is data!'.format(window_days, window_days*24, \\n data.values.shape[0]))\\n \\n # Training period\\n # ---------------\\n train_percent = train_percent/100.\\n st = pd.to_datetime(start_date) # start date\\n et = st + dt.timedelta(days=int(train_percent*window_days)) # end date\\n train = data.loc[st:et].values\\n \\n # Standardize and transform training data set\\n mean_std_values = {}\\n for i, column in enumerate(columns):\\n # Calculate mean and standard deviation only\\n # from the training data set values\\n mu = train[:,i].mean() # axis=0\\n sd = train[:,i].std()\\n mean_std_values[column] = (mu, sd)\\n # Standardize training data\\n train[:,i] = (train[:,i] - mu)/sd\\n \\n # Create overlapping windows with training data\\n X_train, y_train = overlap_windows(train, timesteps, steps_ahead)\\n \\n # Testing / Validation period\\n # ---------------------------\\n sv = et \\n ev = sv + dt.timedelta(days=int((1-train_percent)*window_days)+1)\\n test = data.loc[sv:ev].values\\n \\n # Transform testing/validation data set\\n for i, column in enumerate(columns):\\n # Use mean and standard deviation from the\\n # training data set\\n mu = mean_std_values[column][0]\\n sd = mean_std_values[column][1]\\n # Standardize test data\\n test[:,i] = (test[:,i] - mu)/sd\\n \\n # Create overlaping windows with test data\\n X_test, y_test = overlap_windows(test, timesteps, steps_ahead)\\n \\n return mean_std_values, X_train, y_train, X_test, y_test\",\n \"def preprocess_raw_data(df):\\n def convert_date_to_datetime(_df):\\n return _df.assign(Date=pd.to_datetime(_df['Date']))\\n\\n def fill_missing_miles_with_zero(_df):\\n return _df.fillna({'Miles': 0})\\n\\n def filter_dates_prior_to_today(_df):\\n return _df[_df['Date'] < datetime.datetime.today()]\\n\\n def calculate_rolling_averages(_df):\\n _df['MA_10day'] = _df['Miles'].rolling(window=10).mean().fillna(0)\\n _df['MA_30day'] = _df['Miles'].rolling(window=30).mean().fillna(0)\\n return _df.sort_values('Date')\\n\\n pipeline = [\\n convert_date_to_datetime,\\n fill_missing_miles_with_zero,\\n filter_dates_prior_to_today,\\n calculate_rolling_averages,\\n ]\\n for func in pipeline:\\n df = func(df)\\n\\n df['date_str_label'] = df['Date'].dt.strftime('%b-%d')\\n\\n return df\",\n \"def predict(self):\\n # format data\\n df = self.normalize(self.daily)\\n x = df.index.astype(np.int64).values.reshape(-1, 1)\\n y = self.normalize(df[['Adj Close']]).values\\n\\n # format time\\n one_day_time = 86400000000000\\n x_tomorrow = x[-1] + one_day_time\\n x_incl_tomorrow = np.append(x, [x_tomorrow], axis=0)\\n dates = pd.to_datetime(x_incl_tomorrow.reshape(-1))\\n\\n # average the predictions\\n lin_reg = self.linear_regression(x, y, x_tomorrow, x_incl_tomorrow, dates)\\n knn = self.knn(x, y, x_tomorrow, x_incl_tomorrow, dates)\\n tomorrow_norm = [(lin_reg + knn) / 2]\\n today_norm = [df['Adj Close'][-1]]\\n tomorrow = round((tomorrow_norm[0] * self.daily['Adj Close'][0]), 2)\\n today = self.daily['Adj Close'][-1]\\n percent_gain = round((((tomorrow / today) - 1) * 100), 2)\\n percent_gain_int = abs(int(round(percent_gain, 0)))\\n\\n if percent_gain > 0:\\n self.debug += '\\\\nExpected price gain: {} %, buys + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\\n self.buys += percent_gain_int\\n else:\\n self.debug += '\\\\nExpected price gain: {} %, sells + {}, predicted close is {}'.format(percent_gain, percent_gain_int, tomorrow)\\n self.sells += percent_gain_int\\n\\n # plots dotted line connecting stock today with tomorrow's prediction\\n predicting_line = np.append(today_norm, tomorrow_norm, axis=0)\\n\\n if self.will_plot:\\n self.ax.plot(dates[-2:], predicting_line, color='cyan', dashes=([1, 1, 1, 1]))\\n self.ax.plot(pd.to_datetime(x_tomorrow), tomorrow_norm, marker='o', markersize=3, color=\\\"cyan\\\")\",\n \"def preprocess(self, X):\\n X = X.copy()\\n predictor_subset = self.predictor_subset.copy()\\n if 'all' in predictor_subset:\\n predictor_subset = add_all_predictors(predictor_subset, X.columns)\\n \\n use_temporal = 'temporal' in predictor_subset\\n if use_temporal:\\n X_temporal = get_temporal_predictors(\\n X['TIMESTAMP_END']\\n )\\n predictor_subset.remove('temporal')\\n\\n X = X[predictor_subset]\\n\\n if use_temporal:\\n X = pd.concat([X, X_temporal], axis=1)\\n\\n if 'WD' in predictor_subset:\\n X = process_wind_direction_predictor(X)\\n\\n return X\",\n \"def prob():\\n # Get decision score for our example that came with the request\\n data = flask.request.json\\n #data[]\\n year = data[\\\"year\\\"]\\n Month = data[\\\"month\\\"]\\n Day = data[\\\"day\\\"]\\n species = data[\\\"species\\\"]\\n lat = data[\\\"lattitude\\\"]\\n lng = data[\\\"longitude\\\"]\\n\\n # find the closest trap to the clicked location\\n closest_trap = find_closest_trap(lat,lng,trap_stations)\\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\\n \\n # add 0 in front of month\\n month = \\\"0\\\"+ Month if len(Month) == 1 else Month\\n day = \\\"0\\\"+ Day if len(Day) == 1 else Day\\n date = year + \\\"-\\\" + month + \\\"-\\\" + day\\n trap[\\\"date\\\"] = date\\n\\n # convert datetime to str to match differing formats\\n weather.station = weather.station.astype(str)\\n trap.station = trap.station.astype(int).astype(str)\\n weather.date = weather.date.astype(str)\\n\\n # merge trap & weather dataframes\\n merged = trap.merge(weather,on=[\\\"date\\\",\\\"station\\\"],how=\\\"inner\\\")\\n\\n # convert back datetime\\n merged.date = pd.to_datetime(merged.date)\\n\\n # extract features from datetime and dewpoint\\n merged['month'] = month\\n merged['year'] = year\\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\\n # add risk feature if month is between june and sept (peak virus season)\\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\\n\\n # Reorder columns & Assign to X\\n merged2 = merged.drop([\\\"date\\\", \\\"year\\\", \\\"trap\\\",\\\"species\\\"],1)\\n cols = [\\\"month\\\",\\\"week\\\",\\\"longitude\\\",\\\"latitude\\\",\\\"tmax\\\",\\\"tmin\\\",\\\"tavg\\\",\\\"depart\\\",\\\"dewpoint\\\",\\n \\\"wetbulb\\\",\\\"heat\\\",\\\"cool\\\",\\\"sunrise\\\",\\\"sunset\\\",\\\"preciptotal\\\",\\\"resultspeed\\\",\\\"avgspeed\\\",\\n \\\"month\\\",\\\"dry\\\",\\\"risk\\\"]\\n X = merged2[cols]\\n \\n # predict probability\\n pred = loaded_model.predict_proba(X)\\n prob = round(pred[0][1]*100,2)\\n return flask.jsonify(prob)\",\n \"def features_past_generation(features_creation_function,\\n days,\\n feature_names_prefix,\\n data,\\n indices):\\n matches_outcomes=[]\\n for i,match_indice in enumerate(indices):\\n match=data.iloc[match_indice,:]\\n past_matches=data[(data.Date<match.Date)&(data.Date>=match.Date-datetime.timedelta(days=days))]\\n match_features_outcome_1=features_creation_function(1,match,past_matches)\\n match_features_outcome_2=features_creation_function(2,match,past_matches)\\n matches_outcomes.append(match_features_outcome_1)\\n matches_outcomes.append(match_features_outcome_2)\\n if i%100==0:\\n print(str(i)+\\\"/\\\"+str(len(indices))+\\\" matches treated. \\\"+ features_creation_function.__name__ + str(days))\\n train=pd.DataFrame(matches_outcomes)\\n train.columns=[feature_names_prefix + \\\"_\\\" + str(days) +\\\"_\\\" +str(i) for i in range(len(train.columns))]\\n \\n \\n \\n return train\",\n \"def prepare_data(self):\\r\\n annual_df = self.annual_df\\r\\n coef_df = self.coef_df\\r\\n quarter_df = self.quarter_df\\r\\n # historical_df = self.historical_df\\r\\n Event_Buffer = self.Event_Buffer\\r\\n\\r\\n Tot_Prod = coef_df[\\\"Product\\\"].nunique()\\r\\n # Tot_Week = coef_df[\\\"wk\\\"].nunique()\\r\\n Tot_Week = 52\\r\\n\\r\\n EDLP_Events = list(annual_df[\\\"RP_Events\\\"])\\r\\n Min_EDLP_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\\r\\n ]\\r\\n Max_EDLP_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in EDLP_Events\\r\\n ]\\r\\n\\r\\n TPR_Events = list(annual_df[\\\"TPR_Events\\\"])\\r\\n Min_TPR_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\\r\\n ]\\r\\n Max_TPR_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in TPR_Events\\r\\n ]\\r\\n\\r\\n Target_EDLP_Spend = [i for i in annual_df[\\\"PPG_RP_Spend\\\"]]\\r\\n Target_TPR_Spend = [i for i in annual_df[\\\"PPG_TPR_Spend\\\"]]\\r\\n Target_Trade_Spend = [i for i in annual_df[\\\"PPG_Total_Spend\\\"]]\\r\\n\\r\\n Mapping = {}\\r\\n Prod_Ind = coef_df[\\\"Product\\\"][0:Tot_Prod]\\r\\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\\r\\n Mapping[j] = i\\r\\n Mapping_reverse = {i: j for j, i in Mapping.items()}\\r\\n\\r\\n constants = [i for i in coef_df[\\\"constant\\\"]]\\r\\n\\r\\n Cat_Coef = coef_df[\\\"Catalogue\\\"][0:Tot_Prod]\\r\\n\\r\\n Disp_Coef = coef_df[\\\"Display\\\"][0:Tot_Prod]\\r\\n\\r\\n Base_Price_stg1 = [i for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg1 = []\\r\\n for pr in range(Tot_Prod):\\r\\n Intercepts_stg1.append(\\r\\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\\r\\n )\\r\\n\\r\\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg2 = [\\r\\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\\r\\n ] # noqa\\r\\n\\r\\n EDLP_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Regular\\\") == 1]]\\r\\n )\\r\\n TPR_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Promoted\\\") == 1]]\\r\\n )\\r\\n\\r\\n # ################################ Available EDLP Interactions pairs ##############################\\r\\n\\r\\n EDLP = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Regular\\\") > 1\\r\\n ]\\r\\n EDLP_Interactions = []\\r\\n for i in EDLP:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n EDLP_Interactions.append(temp)\\r\\n\\r\\n # ###################################### Available TPR Interactions pairs #########################\\r\\n\\r\\n TPR = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Promoted\\\") > 1\\r\\n ]\\r\\n TPR_Interactions = []\\r\\n for i in TPR:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n TPR_Interactions.append(temp)\\r\\n\\r\\n # ###################################### EDLP_Interaction_Coef_Values ############################\\r\\n\\r\\n EDLP_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Regular\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ###################################### TPR_Interaction_Coef_Values #############################\\r\\n\\r\\n TPR_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Promoted\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ##################################### Loading Pantry Loading Coefficients #######################\\r\\n\\r\\n Pantry_1 = list(coef_df[\\\"Pantry_Loading_1\\\"])\\r\\n Pantry_1 = [\\r\\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\\r\\n ]\\r\\n Pantry_2 = list(coef_df[\\\"Pantry_Loading_2\\\"])\\r\\n Pantry_2 = [\\r\\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\\r\\n ]\\r\\n\\r\\n # TE_Coeff = np.array(Promo_df[[\\\"TE_Promo\\\",\\\"TE_NoPromo\\\"]])\\r\\n self.Tot_Prod = Tot_Prod\\r\\n self.Tot_Week = Tot_Week\\r\\n self.EDLP_Events = EDLP_Events\\r\\n self.Min_EDLP_Events = Min_EDLP_Events\\r\\n self.Max_EDLP_Events = Max_EDLP_Events\\r\\n self.TPR_Events = TPR_Events\\r\\n self.Min_TPR_Events = Min_TPR_Events\\r\\n self.Max_TPR_Events = Max_TPR_Events\\r\\n\\r\\n self.Target_EDLP_Spend = Target_EDLP_Spend\\r\\n self.Target_TPR_Spend = Target_TPR_Spend\\r\\n self.Target_Trade_Spend = Target_Trade_Spend\\r\\n self.Mapping = Mapping\\r\\n self.Mapping_reverse = Mapping_reverse\\r\\n self.constants = constants\\r\\n self.EDLP_Coef = EDLP_Coef\\r\\n self.TPR_Coef = TPR_Coef\\r\\n\\r\\n self.EDLP_Interactions = EDLP_Interactions\\r\\n self.TPR_Interactions = TPR_Interactions\\r\\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\\r\\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\\r\\n self.Pantry_1 = Pantry_1\\r\\n self.Pantry_2 = Pantry_2\\r\\n\\r\\n self.Base_Price_stg1 = Base_Price_stg1\\r\\n self.Intercepts_stg1 = Intercepts_stg1\\r\\n self.Base_Price_stg2 = Base_Price_stg2\\r\\n self.Intercepts_stg2 = Intercepts_stg2\\r\\n\\r\\n self.Cat_Coef = Cat_Coef\\r\\n self.Disp_Coef = Disp_Coef\",\n \"def create_predictors(y): # pragma: no cover\\n lags = y[-1:-4:-1]\\n\\n return lags\",\n \"def preprocess(dataframe_csvpath, cols_x, cols_y, window_in, window_out, data_div_frac, popu_size):\\n \\n #Loading .CSV file and creating dataframe\\n df = pd.read_csv(dataframe_csvpath) \\n len_ser = len(df[df['Series_No'] == 1])\\n\\n #randomly shuffle different series\\n permute = np.random.permutation(range(1, len(set(df['Series_No']))))\\n train_series_seq = permute[: int(len(set(df['Series_No'])) * data_div_frac)]\\n test_series_seq = permute[int( len(set(df['Series_No'])) * data_div_frac):]\\n \\n #taking relevent columns from dataframe \\n df_x = df[cols_x]\\n df_y = df[cols_y]\\n \\n #Innitialize empty lists which are later to be appended\\n train_seq, test_seq = [], []\\n x_test = []\\n y_true =[]\\n \\n #Creating time series data\\n for series_no in train_series_seq:\\n \\n #new dataframe variable assignment for particular series drom df_x, df_y\\n series_df_x = df_x[df_x['Series_No'] == series_no]\\n series_df_y = df_x[df_y['Series_No'] == series_no]\\n \\n #converting into numpy arrays\\n array_x = np.array(series_df_x)\\n array_y = np.array(series_df_y)\\n \\n #for loop to append to x_train y_train arrays according to window_in, window_out\\n for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked\\n arrayx = array_x.copy()\\n x = arrayx [idx:idx + window_in, : len(cols_x) - 1]\\n #print(x)\\n x[:,0:3] = x[:,0:3] / popu_size\\n #print(x)\\n arrayy = array_y.copy()\\n y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]\\n y = y / popu_size\\n train_seq.append((x, y)) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]\\n #y_train.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])\\n #print(train_seq)\\n\\n #repeat for test sequence\\n for series_no in test_series_seq:\\n \\n #new dataframe variable assignment for particular series drom df_x, df_y\\n series_df_x = df_x[df_x['Series_No'] == series_no]\\n series_df_y = df_x[df_y['Series_No'] == series_no]\\n \\n #converting into numpy arrays\\n array_x = np.array(series_df_x)\\n array_y = np.array(series_df_y)\\n \\n #for loop to append to x_train y_train arrays according to window_in, window_out\\n for idx in range(len(series_df_x) - window_in - window_out + 1): #'len(series_df_x) - window_in - window_out + 1' needs to be checked\\n arrayx = array_x.copy()\\n x = arrayx[idx:idx + window_in, : len(cols_x) - 1]\\n x[:,0:3] = x[:,0:3] / popu_size\\n x_test.append(x)\\n arrayy = array_y.copy()\\n y = arrayy[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1]\\n y = y / popu_size\\n y_true.append(y)\\n test_seq.append((x, y))\\n \\n \\n #test_seq.append((array_x[idx:idx + window_in, : len(cols_x) - 1], array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])) #out col_x and col_y has last item 'Series number' so to remove that [, : len(cols_x)]\\n #y_test.append(array_y[idx + window_in :idx + window_in + window_out, : len(cols_y) - 1])\\n\\n \\n win_len_per_ser = len_ser - window_in - window_out + 1\\n \\n return np.array(train_seq), np.array(test_seq), len_ser, win_len_per_ser, np.array(x_test), np.array(y_true)\",\n \"def predictions(dataframe):\\n # Select Features\\n features = dataframe[['rain', 'precipi', 'Hour', 'meantempi']]\\n\\n # Add UNIT to features using dummy variables\\n dummy_units = pandas.get_dummies(dataframe['UNIT'], prefix='unit')\\n features = features.join(dummy_units)\\n\\n # Values\\n values = dataframe['ENTRIESn_hourly']\\n m = len(values)\\n\\n features, mu, sigma = normalize_features(features)\\n features['ones'] = np.ones(m) # Add a column of 1s (y intercept)\\n\\n # Convert features and values to numpy arrays\\n features_array = np.array(features)\\n values_array = np.array(values)\\n\\n # Set values for alpha, number of iterations.\\n alpha = 0.1 # please feel free to change this value\\n num_iterations = 75 # please feel free to change this value\\n\\n # Initialize theta, perform gradient descent\\n theta_gradient_descent = np.zeros(len(features.columns))\\n theta_gradient_descent, cost_history = gradient_descent(features_array,\\n values_array,\\n theta_gradient_descent,\\n alpha,\\n num_iterations)\\n\\n plot = None\\n # -------------------------------------------------\\n # Uncomment the next line to see your cost history\\n # -------------------------------------------------\\n # plot = plot_cost_history(alpha, cost_history)\\n #\\n # Please note, there is a possibility that plotting\\n # this in addition to your calculation will exceed\\n # the 30 second limit on the compute servers.\\n\\n predicted = np.dot(features_array, theta_gradient_descent)\\n return predicted, plot\",\n \"def process_data(dataframe):\\n\\n # add a new column for just the date in date format\\n dataframe = createDateColumn(dataframe)\\n # get the frequency of each date\\n frequency = dataframe['Date Merged'].value_counts()\\n # converting to df and assigning new names to the columns\\n df_value_counts = pd.DataFrame(frequency)\\n df_value_counts = df_value_counts.reset_index()\\n # change column names\\n df_value_counts.columns = ['dates', 'counts']\\n # delete the the row with None\\n dateFreq = df_value_counts.loc[df_value_counts[\\\"dates\\\"] != \\\"None\\\"]\\n\\n # 1. Create a graph for number of PRs merged over time\\n numPRMerged_graph(dateFreq)\\n # 2. Create a graph for avg PR merge time\\n avgMergetime(dataframe)\\n # 3. A table with PR info for each month\\n getMonthlyPRinfo(dataframe)\",\n \"def predict(df,x):\\n ml,g,sl2,sm2,sd2= x\\n _,in_dic = rl.build_data_strucutre(df,'length_box_um',1)\\n pred_mat = rl.prediction_total(ml,g,sl2,sm2,in_dic['reind_v'],in_dic['dat_v'],in_dic['s'],in_dic['S'],in_dic['dt'],in_dic['lane_ID_v'],in_dic['val_v'],1,sd2,nproc=10)\\n df = rl.merge_df_pred(df,pred_mat)\\n return df\",\n \"def applyTimeLag(dataset, lags, delta):\\n \\n # need to shift the y value back a day so we're predicting\\n # next day price, rather than today!\\n dataset.Return_Out = dataset.Return_Out.shift(-1)\\n \\n maxLag = max(lags)\\n\\n columns = dataset.columns[::(2*max(delta)-1)]\\n for column in columns:\\n for lag in lags:\\n newcolumn = column + str(lag)\\n dataset[newcolumn] = dataset[column].shift(lag)\\n\\n return dataset.iloc[maxLag:-1,:]\",\n \"def predict(self, **kwargs):\\n\\n logging.debug(\\n \\\"Call predict() with parameters. \\\"\\n \\\"Forecast 1 step only, kwargs:{kwargs}\\\".format(kwargs=kwargs)\\n )\\n\\n X_test = self.df[-self.step :][self.feature_names]\\n X_test = self.scaler.transform(X_test)\\n y_predict = self.model.predict(X_test)\\n poly_now = self.y_train_season_obj[-1]\\n first_occ = np.where(self.y_train_season_obj == poly_now)\\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\\n now = self.df[\\\"y\\\"][-self.step :]\\n return (now - poly_now) - y_predict + polynext\",\n \"def prepare_training_data(consumption_series, lag):\\r\\n # scale training data\\r\\n scaler = MinMaxScaler(feature_range=(-1, 1))\\r\\n consumption_vals = scaler.fit_transform(consumption_series.values.reshape(-1, 1))\\r\\n \\r\\n # convert consumption series to lagged features\\r\\n consumption_lagged = create_lagged_features(consumption_vals, lag=lag)\\r\\n\\r\\n # X, y format taking the first column (original time series) to be the y\\r\\n X = consumption_lagged.drop('consumption', axis=1).values\\r\\n y = consumption_lagged.consumption.values\\r\\n \\r\\n # keras expects 3 dimensional X\\r\\n X = X.reshape(X.shape[0], 1, X.shape[1])\\r\\n \\r\\n return X, y, scaler\",\n \"def prepDataFrameForPreprocessor(df, drop_backers_count = True, drop_staff_pick = True):\\n #df = pd.read_csv('data/df_clean.csv')\\n df = dropCols(df,drop_backers_count,drop_staff_pick) #drop unnecessary columns\\n df = dateTimeUpdate(df) #create time delta columns, add these and drop the original timesptamps\\n #df.drop(df.columns[df.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)\\n df = df[(df['state']=='failed') | (df['state']=='successful')]\\n \\n #labelencode y from string to int\\n label_encoder = preprocessing.LabelEncoder()\\n label_encoder.fit([\\\"failed\\\",\\\"successful\\\"])\\n \\n df[\\\"state\\\"] = label_encoder.transform(df.state)\\n \\n return df\",\n \"def create_lag_features_with_time_feature(df = None, cols = None, time = None, n = 5, fillna = True):\\n # assert\\n assert(df is not None and cols is not None)\\n # set attributes\\n cols_to_rename = cols\\n print(cols_to_rename)\\n if type(n) == list:\\n shift_range = n\\n elif type(n) == int:\\n shift_range = range(1, n+1)\\n else:\\n print(\\\"type of n is flase, set it to default: 5\\\")\\n shift_range = range(1, 6)\\n # try to get the new features\\n for month_shift in tqdm(shift_range):\\n train_shift = df.copy()\\n train_shift[time] = train_shift[time] + month_shift\\n foo = lambda x: '{}_lag_{}'.format(x, month_shift) if x in cols_to_rename else x\\n train_shift = train_shift.rename(columns=foo)\\n df = pd.merge(all_data, train_shift, on=index_cols, how='left').fillna(0)\\n del train_shift\\n gc.collect()\\n return df\",\n \"def _predict(self):\\n bull_wr = self.bull_win_rate+np.where(self.df['bullAmount']>self.df['bearAmount'], 1, -1)*self.p_adjustment\\n self.df = self.df.assign(bull_wr=bull_wr)\",\n \"def make_predictions(df):\\n t_labels = get_labels(\\\"labels_pca\\\")\\n # clean data\\n df = clean_data(df)\\n # engineer data\\n df = engineer_features(df)\\n # predict\\n with open(\\\"model.pkl\\\",\\\"r\\\") as mdl:\\n model = pickle.load(mdl)\\n mdl.close()\\n predictions = model.predict(df[t_labels])\\n return predictions\",\n \"def anomaly_detect(data):\\n reconstruction_dict = {}\\n for location, dataframe in data.items():\\n model_dir_path = os.path.join(THIS_DIR, './models')\\n print(location)\\n samples = len(dataframe.index.codes[0])\\n timesteps = len(dataframe.columns.levels[1])\\n np_data_o1 = dataframe[['occupancy1']].values\\n np_data_o2 = dataframe[['occupancy2']].values\\n np_data_diff = np.abs(np_data_o1 - np_data_o2)\\n scaler = MinMaxScaler()\\n np_data_o1 = scaler.fit_transform(np_data_o1)\\n np_data_o2 = scaler.fit_transform(np_data_o2)\\n np_data_diff = scaler.fit_transform(np_data_diff)\\n np_data = np.stack((np_data_o1, np_data_o2, np_data_diff), axis=-1)\\n print(str(np_data.shape[0]) + ' weeks processing') \\n\\n ae = LstmAutoEncoder()\\n\\n # fit the data and save model into model_dir_path\\n ae.fit(np_data[:, :, :], model_dir_path=model_dir_path, std_dev_threshold=1.5)\\n\\n # load back the model saved in model_dir_path detect anomaly\\n ae.load_model(model_dir_path)\\n anomaly_information = ae.anomaly(np_data[:, :, :])\\n reconstruction_error = {}\\n if ae.threshold == 0.0:\\n continue\\n else:\\n reconstruction_error['threshold'] = ae.threshold\\n years = list(dataframe.index.get_level_values(0))\\n weeks = list(dataframe.index.get_level_values(1))\\n for idx, (is_anomaly, dist) in enumerate(anomaly_information):\\n reconstruction_error[str(years[idx]) + ', ' +str(weeks[idx])] = [is_anomaly, dist]\\n if is_anomaly == True:\\n print(location + ' year ' + str(years[idx]) + ', week ' + str(weeks[idx]) + ' is anomalous')\\n reconstruction_dict[location] = reconstruction_error\\n\\n return reconstruction_dict\",\n \"def pre_processing_(data_df , serialized_objects):\\n max_recency_acc_dig = serialized_objects['max_recency_acc_dig'] # These values are taken from trained model values\\n max_recency_dig_2yr = serialized_objects['max_recency_dig_2yr'] # These values are taken from trained model values\\n max_acc_recency_mf = serialized_objects['max_acc_recency_mf'] #These are values imported in training dataset. Same values needs to be used to impute missing values in unseen data\\n\\n data_df = data_df.na.fill({\\n 'recency_acc_dig' : max_recency_acc_dig, # Filling missing values\\n 'recency_dig_2yr' : max_recency_dig_2yr,\\n 'acc_recency_mf' : max_acc_recency_mf\\n })\\n\\n freq_acc_upg_2yrs_split = [-float('inf'), 0, 1, 2, float('inf')]\\n bucketizer_freq_acc_upg_2yrs = Bucketizer(splits=freq_acc_upg_2yrs_split, inputCol='freq_acc_upg_acc_2yrs', outputCol='freq_acc_upg_acc_2yrs_bkt')\\n data_df = bucketizer_freq_acc_upg_2yrs.setHandleInvalid('keep').transform(data_df) # Binning the freq_acc_upg_acc_2yrs column\\n\\n tot_purchase_split = [-float('inf'), 0, 1, 2, 3, float('inf')]\\n bucketizer_tot_purchase = Bucketizer(splits=tot_purchase_split, inputCol='tot_accsry_purchse', outputCol='tot_accsry_purchse_bkt')\\n data_df = bucketizer_tot_purchase.setHandleInvalid('keep').transform(data_df) # Binning the tot_accsry_purchse column\\n\\n del_cols_new = ['freq_acc_upg_acc_2yrs', 'tot_accsry_purchse']\\n data_df = data_df.drop(*del_cols_new) # Dropping the older continuous columns\\n return data_df\",\n \"def preprocess_data(df, cat_columns, cf_columns):\\n \\n #define cat and cf columns, convert cat to dummies\\n df_cat = pd.get_dummies(df[cat_columns], columns = cat_columns, drop_first=True, dtype=float)\\n df_cat.rename(mapper= {\\\"sex_2\\\": \\\"sex_female\\\", \\\"education_2\\\":\\\"education_university\\\", \\\"education_3\\\": \\\"education_high_school\\\", \\\"education_4\\\":\\\"education_others\\\",\\\"marital_status_2\\\":\\\"marital_status_single\\\", \\\"marital_status_3\\\": \\\"marital_status_others\\\"}, axis = 1, inplace = True)\\n df_cf = df[cf_columns]\\n X = pd.concat([df_cf, df_cat], axis = 1)\\n y = df[['default_payment_next_month']]\\n print(\\\"dummy variables created\\\")\\n \\n #train-test split \\n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\\n print(\\\"split done\\\")\\n \\n #resample the train sets\\n smote = SMOTE(sampling_strategy = \\\"not majority\\\", random_state = 42)\\n X_train_rs, y_train_rs = smote.fit_sample(X_train, y_train)\\n print('original class distribution:')\\n print(y[\\\"default_payment_next_month\\\"].value_counts())\\n print('synthetic sample class distribution:')\\n print(pd.Series(y_train_rs).value_counts()) \\n return X, X_train_rs, X_test, y_train_rs, y_test\",\n \"def build_prophet_preds(data_url: str,\\n changepoint_scale: float,\\n holidays_scale: float,\\n seasonality_scale: float,\\n growth ='logistic') -> DataFrame:\\n\\n # read in the data\\n df = pd.read_csv(data_url, parse_dates=['date'])\\n\\n ### PART 1: USE PROPHET TO FIT TOP-LEVEL FORECAST ON ALL CASES\\n\\n # get total number of cases for each day\\n cases = df.groupby('date')['positiveIncrease'].sum().reset_index()\\n cases.rename({'date': 'ds', 'positiveIncrease': 'y'}, axis=1, inplace=True)\\n\\n # variables we'll use a few times\\n cap = 1000000\\n floor = 0\\n\\n # add cap + floor for logistic growth model\\n cases['cap'] = cap\\n cases['floor'] = floor\\n\\n # initialize -- parameters were set via cross validation -- see notebook for covid modeling for more detail\\n mod = Prophet(growth='logistic',\\n changepoint_prior_scale = changepoint_scale,\\n holidays_prior_scale = holidays_scale,\\n seasonality_prior_scale = seasonality_scale)\\n\\n mod.add_country_holidays(country_name='US')\\n\\n print(f\\\"Fitting Model with parameters: changepoint_scale = {mod.changepoint_prior_scale}, holidays_scale: {mod.holidays_prior_scale}, seasonality_scale: {mod.seasonality_prior_scale}\\\")\\n mod.fit(cases)\\n\\n # make df for future predictions -- seven days from most recent date in data set\\n dates = pd.date_range(df['date'].max() + pd.DateOffset(days=1), periods=7).tolist()\\n df_vals = [(date, cap, floor) for date in dates]\\n future = pd.DataFrame(df_vals, columns=['ds', 'cap', 'floor'])\\n\\n # get top level predictions for each day in our forecast\\n top_level_preds = mod.predict(future)[['ds', 'yhat', 'yhat_upper', 'yhat_lower']]\\n\\n ### PART 2: PROJECT TOP LEVEL PREDICTIONS DOWN TO ALL 50 STATES!\\n\\n # create new df with date/state pairs for each state and date for forecast dates\\n states = df['state'].unique().tolist()\\n test_df_vals = [(date, state) for date in dates for state in states]\\n test_df = pd.DataFrame(test_df_vals, columns=['date', 'state']).sort_values(by='date')\\n\\n # get 7 day moving average of proportions for each state in our dataset, as of last day of recorded data\\n train_grp = df.groupby('date')['positiveIncrease'].sum()\\n train_cases = df.merge(train_grp, on='date', how='left')\\n train_cases.rename({'positiveIncrease_y': 'totalCases'}, axis=1, inplace=True)\\n train_cases['proportion'] = train_cases['positiveIncrease_x'] / train_cases['totalCases']\\n train_cases.set_index(['state', 'date'], inplace=True)\\n train_cases.sort_index(level=[0, 1], inplace=True)\\n prop_moving_avgs = train_cases.groupby(level=0)['proportion'].rolling(7).mean()\\n final_props = prop_moving_avgs.groupby(level=0).last()\\n\\n # merge proportions w/ preds_df on each unique date, proportion values\\n test_df = test_df.merge(final_props, left_on='state', right_index=True, how='left')\\n test_df = test_df.merge(top_level_preds, left_on='date', right_on='ds', how='left')\\n test_df.dropna(inplace=True)\\n\\n # this loop takes each of our top level forecasted values, and multiplies them by the proportion\\n # for each state on each day\\n for col in ['yhat', 'yhat_upper', 'yhat_lower']:\\n test_df[col] = test_df['proportion'] * test_df[col]\\n\\n # don't need these columns\\n test_df.drop(['ds', 'proportion'], axis=1, inplace=True)\\n\\n # re-organize df to get a column for upper, lower, middle prediction for each state on each day\\n test_df = test_df.pivot(index='state', columns='date', values=['yhat', 'yhat_upper', 'yhat_lower'])\\n\\n # following lines are to format test_df to something more readable in the database\\n\\n # get unique dates in test_df, in ascending order\\n times = sorted(list(set(time for label, time in test_df.columns if 'yhat' in label)))\\n # rename test_df columns to make them more semantic\\n columns = [f'day{i}_{ending}' for ending in ['pred', 'pred_upper', 'pred_lower'] for i in range(1, len(times) + 1)]\\n test_df.columns = columns\\n\\n # note what date day 1, 2, 3, etc actually are\\n for idx, time in enumerate(times, start=1):\\n test_df[f\\\"day{idx}_date\\\"] = time\\n\\n # popout out the state index one more time\\n test_df.reset_index(inplace=True)\\n\\n # these last lines add a final line to test_df for total values for the entire usa\\n num_vals = test_df.select_dtypes(include=np.number).sum()\\n time_vals = test_df.select_dtypes(include=np.datetime64).iloc[0]\\n index_vals = num_vals.index.tolist() + time_vals.index.tolist()\\n index_vals.insert(0, 'state')\\n\\n series_vals = num_vals.tolist() + time_vals.tolist()\\n series_vals.insert(0, 'USA')\\n\\n test_df = test_df.append(pd.Series(series_vals, index=index_vals), ignore_index=True)\\n test_df['model'] = 'Prophet'\\n test_df['dt'] = np.datetime64('now')\\n\\n return test_df\",\n \"def predict(self, requested_day):\\n\\n df = load_data()\\n\\n # preprocess\\n df = preprocess(df)\\n df = filter_by_country(df, self.country_code)\\n\\n # separate cases from data\\n dates, Y = separate(df)\\n\\n # normalize Y\\n Y = normalize(Y)\\n\\n # apply look_back and generate needed samples\\n X, _ = apply_lookback(Y, look_back=self.look_back)\\n\\n # reshape to fit the model\\n X = reshape(X)\\n dates = reshape(dates)\\n dates = dates[self.look_back:]\\n\\n # unite samples (X) with dates\\n # a date of a sample corresponds to the future prediction value (Y)\\n united_samples = unite_dates_samples(dates.reshape(-1, 1),\\n X.reshape(-1, self.look_back))\\n\\n last_day = requested_day\\n predicted = 0\\n\\n for step in range(self.look_forward):\\n sample, last_day = get_sample(united_samples, last_day)\\n sample = sample.reshape(1, 1, self.look_back)\\n\\n predicted = self.model.predict(sample.astype(np.float32))\\n\\n united_samples, last_day = append_sample(united_samples, predicted,\\n self.look_back,\\n last_day)\\n\\n # date of the prediction\\n last_day = change_date(last_day, delta_days=-1)\\n\\n # chosen starting date\\n start_avail_day = change_date(last_day,\\n delta_days=-self.look_back)\\n\\n # convert to a real number of COVID-19 cases\\n predicted = denormalize(predicted)[0, 0]\\n predicted = int(predicted)\\n\\n prediction_info = {\\n 'prediction_date': last_day,\\n 'starting_date': start_avail_day,\\n 'prediction_new_cases': predicted\\n }\\n\\n return prediction_info, united_samples\",\n \"def extractlag(player, stat4lag, lag ):\\n\\n db_name = \\\"NHLseasonML_seasonstats.db\\\"\\n \\n # connect to our database that will hold everything\\n conn = sqlite3.connect(db_name)\\n\\n with conn:\\n # get the cursor so we can do stuff\\n cur = conn.cursor()\\n\\n # Notice that the stats extracted are hard-coded...\\n cur.execute(\\\"SELECT seasonId, points, goals, ppPoints, shots, timeOnIcePerGame, assists, gamesplayed, playerId \\\\\\n FROM s_skater_summary \\\\\\n WHERE seasonId NOT IN (20172018) \\\\\\n AND playerId=?\\\", [player])\\n\\n data = cur.fetchall()\\n \\n if len(data) > 0: # only lag if some data is retreived\\n\\n # import data into a dataframe\\n df = pd.DataFrame(data)\\n\\n # name the columns of df\\n df.columns = ('year', 'points', 'goals', 'ppPoints', 'shots', 'timeOnIcePerGame', 'assists', 'games', 'id')\\n # ensure the results are sorted by year, latest at the top:\\n df = df.sort_values(['year'],ascending = False) # this step was not necessary for seasonML1 - results were already sorted!\\n # create a dataframe of shifted values - these are lagged w.r.t. the original dataframe\\n dfshift = df.shift(lag)\\n # name the columns of the shifted df\\n dfshift.columns = ('yearlag', 'pointslag', 'goalslag', 'ppPointslag', 'shotslag', 'timeOnIcePerGamelag', 'assistslag', 'gameslag', 'idlag')\\n\\n # find the index of the column desired for lagging\\n columnindex = df.columns.get_loc(stat4lag)\\n\\n # append the appropriate column of the shifted df to the end of the original df\\n df = df.join(dfshift.iloc[:,columnindex]).iloc[lag:,:]\\n\\n #return df # may consider changing to return an array\\n return np.array(df)\\n \\n else: # return NaNs of appropriate shape in case no data is retreived from database\\n \\n # create an empty array\\n temp = np.empty((1,6))\\n # fill it with NaNs\\n temp.fill(np.nan)\\n # convert to a Dataframe\\n df = pd.DataFrame(temp)\\n # name these columns to match typical output\\n df.columns = ('year', 'points', 'goals', 'ppPoints', 'shots', 'timeOnIcePerGame', 'assists', 'games', 'id','pointslag')\\n \\n #return df\\n return np.array(df)\",\n \"def predict_sales() -> str:\\r\\n # HTML tables and list that are filled depending on the user input.\\r\\n html_gr_month_table = app.config[\\\"table_monthly_growth\\\"]\\r\\n html_gr_week_table = app.config[\\\"table_weekly_growth\\\"]\\r\\n html_csv_list = \\\"\\\"\\r\\n # Empty filename and path to sales forecast graph; also dep. on user input.\\r\\n graph_filename = \\\"\\\"\\r\\n graph_filepath = \\\"'File has not been created yet!'\\\"\\r\\n # Contains HTML form data inputted by the user submitted using POST.\\r\\n response = request.form\\r\\n num_months_input = response.get(\\\"num_months_input\\\")\\r\\n csv_filename_input = response.get(\\\"csv_filename_input\\\")\\r\\n # True if user inputted num of past months for prediction and uploaded CSV.\\r\\n if num_months_input is not None and csv_filename_input is not None:\\r\\n # Loads data from CSV into dataframe & removes rows with missing values\\r\\n file_name = csv_filename_input\\r\\n dataframe = load_csv_clean_to_dataframe(file_name)\\r\\n try:\\r\\n # Replaces index numbers of df with datetimes to get a time series.\\r\\n dataframe = convert_to_time_series(dataframe)\\r\\n except KeyError as error:\\r\\n error_message = (\\\"The following column name is missing \\\"\\r\\n \\\"in the data: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Calculates Quantity ordered averages for every past week.\\r\\n column_to_avg = \\\"Quantity ordered\\\"\\r\\n average_by = \\\"W\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\\r\\n # Calculates Quantity ordered averages for every past month.\\r\\n average_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_month = average_data(dataframe, column_to_avg,\\r\\n average_by)\\r\\n # Calculates past Quantity ordered average growth rates.\\r\\n growth_rates_per_week = past_avgs_per_week.pct_change()\\r\\n growth_rates_per_month = past_avgs_per_month.pct_change()\\r\\n # Creates HTML table containing past monthly sales growth rates.\\r\\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\\r\\n app.config[\\\"table_monthly_growth\\\"] = html_gr_month_table\\r\\n # Creates HTML table containing past weekly sales growth rates.\\r\\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\\r\\n app.config[\\\"table_weekly_growth\\\"] = html_gr_week_table\\r\\n # Filters dataframe for beer types.\\r\\n dunkel = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Dunkel\\\"]\\r\\n pilsner = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Pilsner\\\"]\\r\\n red_helles = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Red Helles\\\"]\\r\\n # Monthly basis;\\r\\n # calculates dunkel's Quantity ordered sums for each month.\\r\\n column_to_sum = \\\"Quantity ordered\\\"\\r\\n sum_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\\r\\n # Calculates pilsner's Quantity ordered sums for each month.\\r\\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\\r\\n # Calculates red_helles' Quantity ordered sums for each month.\\r\\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\\r\\n all_order_sum_month = {\\\"dunkers\\\": dunk_order_sum_per_month,\\r\\n \\\"pilsner\\\": pils_order_sum_per_month,\\r\\n \\\"red_helles\\\": redh_order_sum_per_month}\\r\\n # Clears monthly sales forecasts from the dictionary.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"] = {}\\r\\n # Hyperparameters (hp) for seasonal ARIMA model.\\r\\n order = app.config[\\\"order\\\"]\\r\\n # Sets num_months_in. as num of past periods to be considered by model.\\r\\n app.config[\\\"seasonal_order\\\"][3] = int(num_months_input)\\r\\n seasonal_order = app.config[\\\"seasonal_order\\\"]\\r\\n # Fits ARIMA model and forecasts monthly sales for each beer type.\\r\\n try:\\r\\n for beer_type in all_order_sum_month:\\r\\n model = define_and_fit_model(all_order_sum_month[beer_type],\\r\\n False, order, seasonal_order)\\r\\n # Forecasts 12 months into the future with the fitted model.\\r\\n forecast = model.get_forecast(steps=12)\\r\\n # Stores monthly sales forecasts in dictionary by beer_type.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"][beer_type] = forecast\\r\\n # Plots actual and predicted num of beers sold and saves plot.\\r\\n plot_size = (15, 10)\\r\\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\\r\\n plot_size, beer_type)\\r\\n except ValueError as error:\\r\\n error_message = (\\\"Wrong hyperparameters are set, the model \\\"\\r\\n + \\\"can not be fitted: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Gets filepath of this module and thus filepath to graph (png file).\\r\\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\\r\\n graph_filename = \\\"monthly_forecast_and_past.png\\\"\\r\\n try:\\r\\n # Opens plotted graph.\\r\\n img = Image.open(graph_filename)\\r\\n img.show()\\r\\n except (FileNotFoundError, PermissionError) as error:\\r\\n app.config[\\\"logger\\\"].error(error)\\r\\n # Finds names of all uploaded CSV files in folder where this module is.\\r\\n all_uploaded_csv = []\\r\\n for csv_file in glob.glob(\\\"*.csv\\\"):\\r\\n all_uploaded_csv.append(csv_file)\\r\\n # Creates HTML drop-down list containing all uploaded CSV filenames.\\r\\n html_csv_list = update_csv_list(all_uploaded_csv)\\r\\n return (\\\"\\\"\\\"<style>\\r\\n h1, h2, h3 {\\r\\n font-family: arial, sans-serif;\\r\\n }\\r\\n table {\\r\\n font-family: arial, sans-serif;\\r\\n border-collapse: collapse;\\r\\n width: 100%;\\r\\n }\\r\\n td, th {\\r\\n border: 1px solid #dddddd;\\r\\n text-align: left;\\r\\n padding: 8px;\\r\\n }\\r\\n tr:nth-child(even) {\\r\\n background-color: #dddddd;\\r\\n }\\r\\n </style>\\r\\n <h2>Predict sales</h2>\\r\\n <form action=\\\"/predict_sales\\\" method=\\\"POST\\\">\\r\\n Please select an uploaded CSV file that should be used for the \\r\\n sales forecast (if the list is empty, no CSV file has been \\r\\n uploaded yet; to upload a file, please go back to the \\r\\n tracking screen).<br>\\r\\n <select name=\\\"csv_filename_input\\\">\\\"\\\"\\\"\\r\\n + html_csv_list\\r\\n + \\\"\\\"\\\"</select><br><br>\\r\\n Please enter how many <b>months</b> of past data should be\\r\\n used for the sales forecast:<br>\\r\\n (Note: If the number of past periods is higher than the number\\r\\n of periods in the past sales data, the nonexistent periods are\\r\\n recorded as 0 sales, which, if too many periods are 0,\\r\\n makes the prediction zero.\\r\\n <br>\\r\\n <input type=\\\"number\\\" name=\\\"num_months_input\\\" min=\\\"2\\\"\\r\\n required=\\\"required\\\">\\r\\n <br><br>\\r\\n <input type=\\\"submit\\\" value=\\\"Predict sales\\\">\\r\\n </form>\\r\\n In case the graph is not displayed, please open the file \\\"\\\"\\\"\\r\\n + graph_filename + \\\" manually using the path: \\\"\\r\\n + graph_filepath\\r\\n + \\\"\\\"\\\"<form action=\\\"/\\\" method=\\\"POST\\\">\\r\\n <input type=\\\"hidden\\\">\\r\\n <br>\\r\\n <input type=\\\"submit\\\" value=\\\"Go back to tracking screen\\\">\\r\\n </form> \\r\\n <h3>Average sales growth rates of the past months</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Monthly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_month_table + \\\"</table>\\\"\\r\\n + \\\"\\\"\\\"<h3>Average sales growth rates of the past weeks</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Weekly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_week_table + \\\"</table>\\\")\",\n \"def generate_ml_matrix(full_df, pred_ticker, feat_days):\\n\\n feature_tickers = [i for i in full_df.ticker.unique() if i != pred_ticker]\\n dfml = full_df[full_df.ticker == pred_ticker].drop('ticker', axis=1)\\n dfml.rename({'percent_change_feat': f'{pred_ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\\n dfml.rename({'percent_change_pred': f'{pred_ticker}_percent_change_pred'}, axis=1, inplace=True)\\n for ticker in feature_tickers:\\n help_df = full_df[full_df.ticker == ticker][['past_date', 'current_date', 'prediction_date', 'percent_change_feat']]\\n help_df.rename({'percent_change_feat': f'{ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\\n dfml = pd.merge(dfml, help_df,\\n left_on=['past_date', 'current_date', 'prediction_date'],\\n right_on=['past_date', 'current_date', 'prediction_date'],\\n how='left')\\n return dfml\",\n \"def monthly_viewed(df):\\n df = convert_to_datetime(df)\\n today = datetime.date.today()\\n this_month_start = today - timedelta(days=30)\\n last_month_start = today - timedelta(days=60)\\n month_per_min = []\\n lastmonth_per_min = []\\n thismonth_viewed = []\\n lastmonth_viewed = []\\n for index, row in df.iterrows():\\n if row['session_start'].date() >= this_month_start:\\n per_min = get_cards_per_min(row)\\n month_per_min.append(per_min)\\n thismonth_viewed.append(row['total_looked_at'])\\n if last_month_start <= row['session_start'].date() < this_month_start:\\n per_min = get_cards_per_min(row)\\n lastmonth_per_min.append(per_min)\\n lastmonth_viewed.append(row['total_looked_at'])\\n month_viewed_result = total_viewed(thismonth_viewed, lastmonth_viewed)\\n month_viewed_result['total_viewed_monthly'] = month_viewed_result.pop('total_viewed')\\n return month_viewed_result\",\n \"def make_auto_regressive_dataset(df, autoreg_window, log=True, deaths=True, cases=False, predict_deaths=True):\\n\\n assert (deaths == True or cases == True)\\n feature_array = []\\n ys = []\\n _cases = list(df['cases'])\\n _deaths = list(df['deaths'])\\n for i in range(len(_cases)):\\n for j in range(len(_cases[i]) - (autoreg_window + 1)):\\n if predict_deaths:\\n contains_event = sum(_deaths[i][j:j + autoreg_window + 1]) > 0\\n else:\\n contains_event = sum(_cases[i][j:j + autoreg_window + 1]) > 0\\n if contains_event > 0:\\n cases_window = _cases[i][j:j + autoreg_window]\\n if log:\\n cases_window = [np.log(v + 1) for v in cases_window]\\n deaths_window = _deaths[i][j:j + autoreg_window]\\n if log:\\n deaths_window = [np.log(v + 1) for v in deaths_window]\\n if predict_deaths:\\n y_val = _deaths[i][j + autoreg_window + 1]\\n else:\\n y_val = _cases[i][j + autoreg_window + 1]\\n if log:\\n y_val = np.log(y_val + 1)\\n features = []\\n if deaths == True:\\n features.extend(deaths_window)\\n if cases == True:\\n features.extend(cases_window)\\n feature_array.append(features)\\n ys.append(y_val)\\n return feature_array, ys\",\n \"def generate_admission_prediction():\\n query = \\\"SELECT * FROM admissions_data\\\"\\n result = general_db_func.query_database(query)\\n admission_data = pd.DataFrame(result)\\n admission_data.rename(columns={0: \\\"year\\\", 1: \\\"university\\\", 2: \\\"admissions\\\"}, inplace=True)\\n admission_data = admission_data.sort_values(by=[\\\"university\\\", \\\"year\\\"], ascending=True)\\n admission_data = admission_data.groupby(\\\"university\\\")\\n admission_data = [admission_data.get_group(x) for x in admission_data.groups]\\n\\n for uni in admission_data:\\n uni_name = list(uni[\\\"university\\\"])[0]\\n uni = uni.drop(columns=[\\\"university\\\"])\\n uni.set_index(uni.year, inplace=True)\\n\\n historic_points = generate_admission_year_points()\\n historic_admissions = fill_none_admissions(uni[\\\"admissions\\\"].tolist())\\n\\n historic_data = (historic_points, historic_admissions)\\n\\n try:\\n # Generate autoregressive model\\n model = AR(uni[\\\"admissions\\\"])\\n model_fit = model.fit()\\n\\n # predict next 2 years\\n prediction_points = generate_admission_year_points(future=True)\\n predicted_admissions = model_fit.predict(len(uni[\\\"year\\\"]), len(uni[\\\"year\\\"])+1).to_list()\\n predicted_data = (prediction_points, predicted_admissions)\\n insert_predicted_admission(uni_name, historic_data, predicted_data)\\n except ValueError as e:\\n print(e)\",\n \"def _forecast(model, predict_steps):\\n\\n pred_uc = model.get_forecast(steps=predict_steps)\\n\\n # Produce the forecasted tables\\n predicted_mean_df = pred_uc.predicted_mean.reset_index()\\n predicted_mean_df.columns = ['Date', 'Predicted_Mean']\\n return predicted_mean_df\",\n \"def time_to_n_previous(df, cols, dummy_col, generated_feature_name, params={'n':1,'fillna':np.nan}):\\n n = int(params['n'])\\n if n > 0:\\n n = -n\\n params['n'] = n\\n return time_to_n_next(df, cols, dummy_col, generated_feature_name, params)\",\n \"def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \\r\\n \\r\\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\\r\\n \\r\\n if dropnan==False:\\r\\n series.replace(pd.np.nan,0,inplace=True)\\r\\n \\r\\n # Dropping other variables:\\r\\n if dropVars:\\r\\n index = list(np.arange(series.shape[1]-2,\\r\\n series.shape[1]-len(predictors)-2,\\r\\n -1))\\r\\n \\r\\n labels = [item for idx,item in enumerate(series.columns) \\r\\n if idx in index]\\r\\n \\r\\n #print(\\\"Eliminando variáveis: {}\\\".format(labels))\\r\\n series.drop(labels,axis=1,inplace=True) \\r\\n \\r\\n return series\",\n \"def tag_data(df):\\n fault_dic = get_fault_info()\\n #nan_cols = check_nan_value(df)\\n #df.drop(columns=nan_cols, inplace=True)\\n df.dropna(axis=1, how='all', inplace=True)\\n df.reset_index(drop=True, inplace=True)\\n df[\\\"tag\\\"] = 0\\n df[\\\"flag\\\"] = 0\\n df[\\\"30_day\\\"] = 0\\n for row in df.itertuples():\\n number = getattr(row, \\\"serial_number\\\")\\n manufacturer = getattr(row, \\\"manufacturer\\\")\\n model = getattr(row, \\\"model\\\")\\n key = tuple([manufacturer, model, number]) #get keys to look up in fault_dic\\n if key in fault_dic:\\n fault_date = fault_dic[key][\\\"date\\\"]\\n date = str(getattr(row, \\\"dt\\\"))\\n days_diff = (pd.to_datetime(fault_date) - pd.to_datetime(date)).days\\n if days_diff >= 0 and days_diff <= 30:\\n df.loc[row[0], \\\"tag\\\"] = fault_dic[key][\\\"tag\\\"] + 1\\n if days_diff == 0:\\n df.loc[row[0], \\\"flag\\\"] = 1\\n if days_diff == 30:\\n df.loc[row[0], \\\"30_day\\\"] = 1\\n correct_column_type(df) \\n return df\",\n \"def _predict(cls, model, is_log_transformed,\\n raw_actual, interpolated_actual,\\n training_end=None, seasonal_feature_scoring=None, pred_date=None, order_of_diff=None,\\n training_tail=None, ext_training_features=None, pred_len=None, freq=None,\\n include_holidays_exog=None):\\n\\n import numpy as np\\n import pandas as pd\\n import scipy.stats as st\\n from numpy.linalg import LinAlgError\\n import math\\n\\n alpha = cls._sig_level\\n alpha_extreme = cls._sig_level_extreme\\n\\n include_holidays_exog = include_holidays_exog if ext_training_features else 0\\n\\n index = pd.date_range(start=training_end, end=pred_date, freq=freq)[1:] # Holidays are always daily.\\n\\n de_obj = DataExploration()\\n pred_exog = de_obj._get_exog_data(pred_date, pred_date, index) if include_holidays_exog else None\\n\\n if pred_exog is not None and set(pred_exog.columns.values) != set(ext_training_features):\\n missing_col_list = list(set(ext_training_features) - set(pred_exog.columns.values))\\n common_cols = list(set(ext_training_features).intersection(set(pred_exog.columns.values)))\\n temp_df = pred_exog[common_cols]\\n missing_feat_df = pd.DataFrame(np.zeros([len(pred_exog), len(missing_col_list)]),\\n columns=missing_col_list, index=pred_exog.index.values)\\n pred_exog = pd.concat([temp_df, missing_feat_df], axis=1)\\n pred_exog = pred_exog[ext_training_features]\\n\\n freq = \\\"1\\\" + freq if not any(char.isdigit() for char in freq) else freq\\n\\n forecast_ndays = int((pred_date - pd.Timestamp(training_end)) / pd.Timedelta(freq))\\n model_freshness = forecast_ndays / float(pred_len)\\n\\n try:\\n if forecast_ndays > pred_len:\\n raise ValueError('Current trained model object expired')\\n\\n float_min = 1e-10\\n\\n # set exogenous (holiday) variables for input data\\n if include_holidays_exog:\\n pred_exog = pred_exog.loc[pd.Timestamp(training_end) + pd.Timedelta(freq): pred_date]\\n else:\\n pred_exog = None\\n\\n if seasonal_feature_scoring:\\n if not include_holidays_exog:\\n pred_exog = seasonal_feature_scoring[:forecast_ndays]\\n else:\\n pred_exog['fourier_feature'] = seasonal_feature_scoring[:forecast_ndays]\\n\\n forecast = list(model.forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog))\\n interpolated_training_data = list(zip(*training_tail))[1]\\n\\n for order in list(reversed(range(order_of_diff))):\\n training_data_diff = np.diff(interpolated_training_data,\\n order) if order > 0 else interpolated_training_data\\n\\n forecast_diff_mean = [training_data_diff[-1]]\\n forecast_diff_ci = []\\n\\n for i in range(forecast_ndays):\\n forecast_diff_mean.append(forecast_diff_mean[-1] + forecast[0][i])\\n forecast_diff_ci.append([forecast_diff_mean[-1] -\\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i]),\\n forecast_diff_mean[-1] +\\n (st.norm.ppf(1 - (alpha / 2.0)) * forecast[1][i])])\\n forecast[0] = forecast_diff_mean[1:]\\n forecast[2] = forecast_diff_ci\\n\\n if is_log_transformed:\\n transformed_back_forecast = np.exp(forecast[0][-1] + ((forecast[1][-1] ** 2) / 2.0)) - 1\\n transformed_back_std_err = np.sqrt((np.exp(forecast[1][-1] ** 2) - 1) * (np.exp((2 * forecast[0][-1]) +\\n (forecast[1][\\n -1] ** 2))))\\n transformed_back_CILower = transformed_back_forecast - \\\\\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\\\\n if transformed_back_std_err != 0 else transformed_back_forecast\\n transformed_back_CIUpper = transformed_back_forecast + \\\\\\n st.norm.ppf(1 - (alpha / 2.0), 0, transformed_back_std_err) \\\\\\n if transformed_back_std_err != 0 else transformed_back_forecast\\n transformed_back_interpolated_actual = float(np.exp(interpolated_actual) - 1)\\n if np.sum(np.isnan(forecast[0][-1])) or np.isnan(forecast[1][-1]):\\n raise ValueError('Predicted null value')\\n\\n if is_log_transformed:\\n zscore = (transformed_back_interpolated_actual -\\n transformed_back_forecast) / max(float(transformed_back_std_err), float_min)\\n\\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\\n if math.isnan(anomaly_probability) or math.isnan(transformed_back_CILower) \\\\\\n or math.isnan(transformed_back_CIUpper):\\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN under log transform')\\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\\n\\n result = {'Success': True,\\n 'IsLogTransformed': is_log_transformed,\\n 'LogTransformedAdjustedActual': interpolated_actual,\\n 'LogTransformedPrediction': float(forecast[0][-1]),\\n 'LogTransformedStdErr': float(forecast[1][-1]),\\n 'LogTransformedCILower': float(forecast[2][-1][0]),\\n 'LogTransformedCIUpper': float(forecast[2][-1][1]),\\n 'AdjustedActual': transformed_back_interpolated_actual,\\n 'Prediction': float(transformed_back_forecast) if not float(\\n transformed_back_forecast) == float('inf') else 0.0,\\n 'StdErr': float(transformed_back_std_err) if not float(\\n transformed_back_std_err) == float('inf') else 0.0,\\n 'CILower': float(transformed_back_CILower) if not float(\\n transformed_back_CILower) == float('-inf') else 0.0,\\n 'CIUpper': float(transformed_back_CIUpper) if not float(\\n transformed_back_CIUpper) == float('inf') else 0.0,\\n 'ConfLevel': float(1.0 - alpha) * 100,\\n 'ExogenousHolidays': include_holidays_exog,\\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\\n 'ModelFreshness': model_freshness}\\n\\n else:\\n zscore = (interpolated_actual - forecast[0][-1]) / max(float(forecast[1][-1]), float_min)\\n\\n anomaly_probability = (2 * st.norm(0, 1).cdf(abs(zscore))) - 1\\n if math.isnan(anomaly_probability) or math.isnan(forecast[2][-1][0]) or math.isnan(forecast[2][-1][1]):\\n raise ValueError('Either Anomaly probability or CILower or CIUpper is NaN')\\n\\n down_anomaly_probability = 1 - st.norm(0, 1).cdf(zscore)\\n up_anomaly_probability = st.norm(0, 1).cdf(zscore)\\n\\n result = {'Success': True,\\n 'IsLogTransformed': is_log_transformed,\\n 'AdjustedActual': interpolated_actual,\\n 'Prediction': float(forecast[0][-1]) if not float(\\n forecast[0][-1]) == float('inf') else 0.0,\\n 'StdErr': float(forecast[1][-1]) if not float(\\n forecast[1][-1]) == float('inf') else 0.0,\\n 'CILower': float(forecast[2][-1][0]) if not float(\\n forecast[2][-1][0]) == float('-inf') else 0.0,\\n 'CIUpper': float(forecast[2][-1][1]) if not float(\\n forecast[2][-1][1]) == float('inf') else 0.0,\\n 'ConfLevel': float(1.0 - alpha) * 100,\\n 'ExogenousHolidays': include_holidays_exog,\\n 'IsAnomaly': bool(anomaly_probability > 1 - alpha),\\n 'IsAnomalyExtreme': bool(anomaly_probability > 1 - alpha_extreme),\\n 'AnomalyProbability': 1 if raw_actual is None else float(anomaly_probability),\\n 'DownAnomalyProbability': 1 if raw_actual is None else float(down_anomaly_probability),\\n 'UpAnomalyProbability': 1 if raw_actual is None else float(up_anomaly_probability),\\n 'ModelFreshness': model_freshness}\\n\\n except (LinAlgError, ValueError, LADStructuralError) as e:\\n result = {'Success': False,\\n 'AdjustedActual': interpolated_actual,\\n 'ErrorMessage': str(e)}\\n\\n return result\",\n \"def _basis_function_n_step_prediction(self, X, y, steps_ahead, forecast_horizon):\\n if len(y) < self.max_lag:\\n raise ValueError(\\n \\\"Insufficient initial condition elements! Expected at least\\\"\\n f\\\" {self.max_lag} elements.\\\"\\n )\\n\\n if X is not None:\\n forecast_horizon = X.shape[0]\\n else:\\n forecast_horizon = forecast_horizon + self.max_lag\\n\\n yhat = super()._basis_function_n_step_prediction(\\n X, y, steps_ahead, forecast_horizon\\n )\\n return yhat.reshape(-1, 1)\",\n \"def forc_model(self):\\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\\n for add in self.X.address1.unique():\\n add_mask = self.X.address1 == add\\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\\n top_index = foo.index[0]\\n clust = int(foo.cluster.values[0])\\n # get values from last visit for store\\n base_input = foo[self.model_mask_cols].values[0]\\n base_actual = self.y[top_index]\\n lag2_val = base_input[lag1_loc]\\n lag1_val = base_actual\\n\\n for i in range(1, self.num_periods + 1):\\n model = self.model_list[clust]\\n inputs = base_input\\n inputs[lag1_loc] = lag1_val\\n inputs[lag2_loc] = lag2_val\\n \\n pred = model.predict(inputs.reshape(1, -1))\\n self._update_cust_table(add, i, pred)\\n \\n lag2_val = lag1_val\\n lag1_val = pred\",\n \"def FE_add_lagged_targets_by_date_category(train2, target_col, date_col, category_col, test2):\\r\\n train2 = copy.deepcopy(train2)\\r\\n test2 = copy.deepcopy(test2)\\r\\n new_col = target_col+'_array'\\r\\n train2[new_col] = 0\\r\\n i = 0\\r\\n train2.reset_index(drop=False, inplace=True) # index column will be called 'index'\\r\\n train2 = train2.set_index(pd.to_datetime(train2.pop(date_col)))\\r\\n train2.sort_index(inplace=True)\\r\\n for index, each_train in tqdm(train2.iterrows(), total=train2.shape[0]):\\r\\n before_grp = train2.loc[train2.index < index]\\r\\n prim = each_train[category_col]\\r\\n mask = (before_grp[category_col] == prim)\\r\\n if len(before_grp.loc[mask]) > 0:\\r\\n price_val = before_grp.loc[mask,target_col].values.tolist()\\r\\n else:\\r\\n price_val = before_grp[target_col].values.tolist()\\r\\n #price_val = f\\\"{price_val}\\\"\\r\\n price_val = np.mean(price_val)\\r\\n #print(' i = ', i+1, price_val)\\r\\n train2.iloc[i,-1] = price_val\\r\\n i += 1\\r\\n if not isinstance(test2, str):\\r\\n test2[new_col] = 0\\r\\n i = 0\\r\\n test2.reset_index(drop=False, inplace=True) # index column will be called 'index'\\r\\n test2 = test2.set_index(pd.to_datetime(test2.pop(date_col)))\\r\\n test2.sort_index(inplace=True)\\r\\n for index1, each_test in tqdm(test2.iterrows(), total=test2.shape[0]):\\r\\n before_grp = train2.loc[train2.index < index1]\\r\\n prim = each_test[category_col]\\r\\n mask = (before_grp[category_col] == prim)\\r\\n if len(before_grp.loc[mask]) > 0:\\r\\n price_val = before_grp.loc[mask,target_col].values.tolist()\\r\\n else:\\r\\n price_val = before_grp[target_col].values.tolist()\\r\\n price_val = np.mean(price_val)\\r\\n test2.iloc[i,-1] = price_val\\r\\n i += 1\\r\\n return train2.set_index('index'), test2.set_index('index')\",\n \"def create_date_data(gt_id,\\n target_horizon,\\n experiment,\\n date_features=[\\\"mei\\\", \\\"mjo\\\",\\n \\\"pca_sst_2010\\\", \\\"pca_icec_2010\\\",\\n \\\"pca_wind_hgt_10_2010\\\",\\n \\\"pca_wind_hgt_100_2010\\\",\\n \\\"pca_wind_hgt_500_2010\\\",\\n \\\"pca_wind_hgt_850_2010\\\"]):\\n\\n time_start = time.time()\\n\\n # --------\\n # Prepare experiment cache directory and saved file names\\n # --------\\n\\n # Name of cache directory for storing non-submission-date specific\\n # intermediate files\\n cache_dir = os.path.join('results', experiment, 'shared',\\n '{}_{}'.format(gt_id, target_horizon))\\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\\n\\n # if cache_dir doesn't exist, create it\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n\\n # Filenames for data file to be stored in cache_dir\\n date_data_file = os.path.join(\\n cache_dir, \\\"date_data-{}_{}.h5\\\".format(gt_id, target_horizon))\\n\\n # --------\\n # Creates date_data dataframe.\\n # --------\\n # Get number of days between start date of observation period used for prediction\\n # (2 weeks behind) and start date of target period (2 or 4 weeks ahead)\\n start_deltas = [get_start_delta(target_horizon, gt_id)\\n for gt_id in date_features]\\n\\n # Load masked date features\\n print \\\"Loading date features\\\"\\n date_data = get_date_features(gt_ids=date_features, gt_shifts=start_deltas,\\n first_year=get_first_year(gt_id))\\n\\n print \\\"Loading additional date features\\\"\\n t = time.time()\\n if 'mjo' in date_features:\\n # Add cosine and sine transforms of MJO phase\\n mjo_phase_name = 'phase_shift'+str(get_start_delta(target_horizon, 'mjo'))\\n date_data['cos_'+mjo_phase_name] = np.cos((2*np.pi*date_data[mjo_phase_name])/8)\\n date_data['sin_'+mjo_phase_name] = np.sin((2*np.pi*date_data[mjo_phase_name])/8)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n # Save date features to disk\\n print \\\"Saving date features to \\\"+date_data_file\\n t = time.time()\\n date_data.to_hdf(date_data_file, key=\\\"data\\\", mode=\\\"w\\\")\\n subprocess.call(\\\"chmod a+w \\\"+date_data_file, shell=True)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n print \\\"Finished generating date_data matrix.\\\"\\n print \\\"Total time elapsed: {}s\\\".format(time.time()-time_start)\\n return list(date_data)\",\n \"def create_features(energy_data, label=None):\\n energy_data['date'] = energy_data.index\\n energy_data['hour'] = energy_data['Datetime'].dt.hour\\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\\n energy_data['month'] = energy_data['Datetime'].dt.month\\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\\n energy_data['year'] = energy_data['Datetime'].dt.year\\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\\n\\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\\n \\\"pjme_8_hrs_mean\\\", \\\"pjme_12_hrs_mean\\\", \\\"pjme_24_hrs_mean\\\", \\\"pjme_4_hrs_std\\\",\\n \\\"pjme_8_hrs_std\\\", \\\"pjme_12_hrs_std\\\", \\\"pjme_24_hrs_std\\\",\\n \\\"pjme_4_hrs_max\\\", \\\"pjme_8_hrs_max\\\", \\\"pjme_12_hrs_max\\\", \\\"pjme_24_hrs_max\\\",\\n \\\"pjme_4_hrs_min\\\", \\\"pjme_8_hrs_min\\\", \\\"pjme_12_hrs_min\\\", \\\"pjme_24_hrs_min\\\"]]\\n if label:\\n label = energy_data[label]\\n return features, label\\n return features\",\n \"def add_all_features(df):\\n df.reset_index(drop=True, inplace=True)\\n df = target_indicators(df)\\n df = momentum_indicators(df)\\n df = trend_indicators(df)\\n df = volatility_indicators(df)\\n df = volume_indicators(df)\\n df = special_indicators(df)\\n return df\",\n \"def preprocess_feature(df):\",\n \"def predict_XGB():\\n #read future games\\n future_mstr = pd.read_csv(FG_filename)\\n #extract features and prepare X for predictions\\n features = future_mstr.columns[6:]\\n X_new = future_mstr[features]\\n #load saved model\\n xgb = joblib.load(XG_filename)\\n #make predictions\\n XG_preds = xgb.predict(X_new)\\n future_mstr['Predictions'] = XG_preds\\n #return the relevant prediction information\\n cols = ['Gamecode', 'Date', 'Team', 'Opp', 'Predictions']\\n prediction_mstr_all = future_mstr[cols]\\n \\n return prediction_mstr_all\",\n \"def create_my_dataset(df, predict_window):\\n\\n x = []\\n y = []\\n for i in range(predict_window, df.shape[0]):\\n x.append(df[i-predict_window:i,0])\\n y.append(df[i,0])\\n #convert data to numpy array\\n x = np.array(x)\\n y = np.array(y)\\n \\n return x,y\",\n \"def _preprocess(self):\\n\\n self.df = self.df[(self.df['days_b_screening_arrest'] <= 30)\\n & (self.df['days_b_screening_arrest'] >= -30)\\n & (self.df['is_recid'] != -1)\\n & (self.df['c_charge_degree'] != 'O')\\n & (self.df['score_text'] != 'N/A')]\\n\\n self.df['c_jail_out'] = pd.to_datetime(self.df['c_jail_out'])\\n self.df['c_jail_in'] = pd.to_datetime(self.df['c_jail_in'])\\n self.df['length_of_stay'] = (self.df['c_jail_out']\\n - self.df['c_jail_in'])\\n\\n self.df['score_factor'] = np.where(self.df['score_text']\\n != 'Low',\\n 'HighScore', 'LowScore')\\n self.df['y_pred'] = (self.df['score_factor'] == 'HighScore')\",\n \"def embed_vectors_1d(self,lag,embed,predict):\\n\\n tsize = self.X.shape[0]\\n t_iter = tsize-predict-(lag*(embed-1))\\n\\n features = np.zeros((t_iter,embed))\\n targets = np.zeros((t_iter,predict))\\n\\n for ii in range(t_iter):\\n\\n end_val = ii+lag*(embed-1)+1\\n\\n part = self.X[ii : end_val]\\n\\n features[ii,:] = part[::(lag)]\\n targets[ii,:] = self.X[end_val:end_val+predict]\\n return features, targets\",\n \"def _add_lags(self, X, y=None, extrapolate=1, update_features_df=False):\\n\\n # Add lag target to the features if required\\n # This will create an additional feature for each sample i.e. the previous value of y \\n if y is not None and self.model.lag_target:\\n X[\\\"previous_y\\\"] = y.shift(1)\\n \\n if update_features_df:\\n # Check the target's data type\\n dt = 'float' if is_numeric_dtype(y.iloc[:,0]) else 'str'\\n # Set the preprocessing feature strategy for the lag targets\\n if self.model.estimator_type == 'classifier':\\n fs = 'one hot encoding' \\n elif self.model.scale_lag_target and not self.model.scale_target:\\n fs = 'scaling'\\n else:\\n fs = 'none'\\n self.model.scale_lag_target\\n # Update feature definitions for the model\\n self.model.features_df.loc['previous_y'] = [self.model.name, 'previous_y', 'feature', dt, fs, '']\\n\\n if self.model.lags:\\n # Add the lag observations\\n X = utils.add_lags(X, lag=self.model.lags, extrapolate=extrapolate, dropna=True, suffix=\\\"t\\\")\\n \\n if update_features_df:\\n # Duplicate the feature definitions by the number of lags\\n self.model.features_df = pd.concat([self.model.features_df] * (self.model.lags+extrapolate))\\n # Set the new feature names as the index of the feature definitions data frame\\n self.model.features_df['name'] = X.columns\\n self.model.features_df = self.model.features_df.set_index('name', drop=True)\\n\\n if self.model.debug:\\n self._print_log(11, data=X)\\n\\n return X\",\n \"def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\\n\\n dataframe['ema6'] = ta.EMA(dataframe, timeperiod=9)\\n dataframe['ema24'] = ta.EMA(dataframe, timeperiod=18)\\n\\n dataframe['ema11'] = ta.EMA(dataframe, timeperiod=32)\\n dataframe['ema25'] = ta.EMA(dataframe, timeperiod=64)\\n\\n dataframe['ema'] =dataframe['ema6']-dataframe['ema24']\\n dataframe['ema2'] = dataframe['ema11'] - dataframe['ema25']\\n\\n dataframe['ema']= dataframe['ema']*0.6 + dataframe['ema2']*0.5\\n dataframe['ema2'] = ta.SMA(dataframe['ema'], timeperiod=29)\\n\\n return dataframe\",\n \"def create_regressor_attributes(df, attribute, list_of_prev_t_instants) :\\n \\n list_of_prev_t_instants.sort()\\n start = list_of_prev_t_instants[-1] \\n end = len(df)\\n df['datetime'] = df.index\\n df.reset_index(drop=True)\\n\\n df_copy = df[start:end]\\n df_copy.reset_index(inplace=True, drop=True)\\n\\n for attribute in attribute :\\n foobar = pd.DataFrame()\\n\\n for prev_t in list_of_prev_t_instants :\\n new_col = pd.DataFrame(df[attribute].iloc[(start - prev_t) : (end - prev_t)])\\n new_col.reset_index(drop=True, inplace=True)\\n new_col.rename(columns={attribute : '{}_(t-{})'.format(attribute, prev_t)}, inplace=True)\\n foobar = pd.concat([foobar, new_col], sort=False, axis=1)\\n\\n df_copy = pd.concat([df_copy, foobar], sort=False, axis=1)\\n \\n df_copy.set_index(['datetime'], drop=True, inplace=True)\\n return df_copy\",\n \"def __init__(self, pandas_dataframe, dates_column, target_column, regressors=None, train_test_split=0.66, seed=7,\\n look_back=1, look_forward=1, interval=0):\\n data = pd.DataFrame(index=pandas_dataframe[dates_column].values, data=pandas_dataframe[target_column].values)\\n # Calculate the training set size\\n train_size = int(len(data)*train_test_split)\\n # Scale the data pre-train/test split\\n scaler = MinMaxScaler(feature_range=(0, 1))\\n self.scaler = scaler\\n data = scaler.fit_transform(data)\\n # Get the time series as stationary (for the given interval, if 0 don't make it a series of 0)\\n if interval > 0:\\n data = difference(data, interval)\\n # Map the series to a supervised problem (values for days 1-n with regressors for these days to predict days\\n # n + 1 ... n + k\\n x, y = timeseries_to_supervised(data, look_back=look_back, look_forward=look_forward)\\n # Split train and test\\n self.x_train, self.y_train = x[:train_size], y[:train_size]\\n self.x_test, self.y_test = x[train_size:], y[train_size:]\\n # Use regressors if required\\n if regressors is not None:\\n self.x_train, self.x_test = add_regressors(self.x_train, self.x_test, regressors, pandas_dataframe,\\n dates_column, look_forward, look_back)\\n # Set last attributes\\n self.seed = seed\\n self.look_back = look_back\\n self.look_forward = look_forward\\n self.regressors = regressors\",\n \"def count_in_previous_n_time_unit(df, cols, dummy_col, generated_feature_name, params=None): \\n group_cols = cols[:-1]\\n calc_col = cols[-1]\\n n = int(params['n'])\\n encodings = df[group_cols[0]].copy()\\n if len(group_cols) > 1:\\n for c in group_cols[1 : ]:\\n encodings = encodings * (10 ** (int(np.log(df[c].max() + 1) / np.log(10)) + 1)) + df[c]\\n encodings = encodings.values\\n times = df[calc_col].values\\n dict_count = defaultdict(int)\\n result = []\\n bound = 0\\n for cur in range(len(encodings)):\\n while abs(times[cur] - times[bound]) > n:\\n dict_count[encodings[bound]] -= 1\\n bound += 1\\n result.append(dict_count[encodings[cur]])\\n dict_count[encodings[cur]] += 1\\n r = pd.DataFrame(result, columns=[generated_feature_name], dtype=utils.set_type(result, 'uint'))\\n del encodings, times, dict_count, result, bound, n\\n gc.collect()\\n module_logger.debug('feature generated: {}'.format(generated_feature_name))\\n return r\",\n \"def pre_get_data(df):\\n\\n df_len = len(df.iloc[0, :]) - 1\\n\\n select_cols = []\\n\\n for i in range(df_len): #Get Columns that contain number values\\n\\n if type(df.iloc[0, i + 1]) is np.float64:\\n if math.isnan(df.iloc[0, i + 1]) == False:\\n select_cols.append(i + 1)\\n elif type(df.iloc[0, i + 1]) is np.float:\\n if math.isnan(df.iloc[0, i + 1]) == False:\\n select_cols.append(i + 1)\\n\\n\\n res_df = df.iloc[:, select_cols]\\n\\n list_pop = list(res_df)\\n list_res = ['B_F1_Bool_Result', 'Event_Date', 'B_WClass']\\n list_pop.pop()\\n\\n for item in list_pop:\\n if \\\"F1\\\" in item:\\n aa = item\\n bb = aa.replace(\\\"F1\\\", \\\"F2\\\")\\n if bb in list_pop:\\n cc = aa.replace(\\\"F1\\\", \\\"F12\\\")\\n df[cc] = df[aa] - df[bb]\\n list_res.append(cc)\\n\\n elif \\\"F2\\\" not in item:\\n list_res.append(item)\\n\\n\\n\\n bw = df['B_WClass']\\n i = -1\\n j = df.columns.get_loc('B_WClass')\\n\\n for item in bw:\\n i = i + 1\\n if item != item:\\n df.iloc[i, j] = np.nan\\n else:\\n df.iloc[i, j] = get_weight[item]\\n\\n df['B_WClass'] = df['B_WClass'].astype(float)\\n res_df = df[list_res]\\n\\n return res_df\",\n \"def decide_input(yt, max_lag_period, forecast_period): \\n correlations = list()\\n yt_mean = np.mean(yt)\\n\\n #correlations of time series yt at lag k (yt-1...yt-max_lag_period+1)\\n for k in range(1, max_lag_period + 2):\\n yt_k = shift(yt, k, cval=np.NaN)\\n a = yt-yt_mean\\n a = np.nan_to_num(a)\\n b = yt_k-yt_mean\\n b = np.nan_to_num(b)\\n print(np.dot(a, b))\\n r_k = np.sum(np.dot(a, b))\\n correlations.append(r_k)\\n \\n print(\\\"CORRELATIONS\\\", correlations)\\n \\n s = list()\\n muls = list()\\n for i in range(0, len(correlations)-1):\\n for j in range(0, len(correlations)-1):\\n if(j != i):\\n muls.append(abs(correlations[i-j]))\\n \\n s.append(abs(correlations[i + forecast_period])/abs(np.prod(muls)))\\n \\n print(s)\\n lag_periods = np.argpartition(s, -max_lag_period)[-max_lag_period:] + 1\\n \\n print(\\\"lag_periods\\\", lag_periods)\\n return lag_periods\",\n \"def generate_features(df):\\n df_new = pd.DataFrame()\\n \\n # 6 original features\\n df_new['open'] = df['open']\\n df_new['open_1'] = df['open'].shift(1)\\n df_new['close_1'] = df['close'].shift(1)\\n df_new['high_1'] = df['high'].shift(1)\\n df_new['low_1'] = df['low'].shift(1)\\n df_new['volume_1'] = df['volume'].shift(1)\\n \\n # 50 original features\\n # average price\\n df_new['avg_price_5'] = df['close'].rolling(window=5).mean().shift(1)\\n df_new['avg_price_30'] = df['close'].rolling(window=21).mean().shift(1)\\n df_new['avg_price_90'] = df['close'].rolling(window=63).mean().shift(1)\\n df_new['avg_price_365'] = df['close'].rolling(window=252).mean().shift(1)\\n \\n # average price ratio\\n df_new['ratio_avg_price_5_30'] = df_new['avg_price_5'] / df_new['avg_price_30']\\n df_new['ratio_avg_price_905_'] = df_new['avg_price_5'] / df_new['avg_price_90']\\n df_new['ratio_avg_price_5_365'] = df_new['avg_price_5'] / df_new['avg_price_365']\\n df_new['ratio_avg_price_30_90'] = df_new['avg_price_30'] / df_new['avg_price_90']\\n df_new['ratio_avg_price_30_365'] = df_new['avg_price_30'] / df_new['avg_price_365']\\n df_new['ratio_avg_price_90_365'] = df_new['avg_price_90'] / df_new['avg_price_365'] \\n \\n \\n # average volume\\n df_new['avg_volume_5'] = df['volume'].rolling(window=5).mean().shift(1)\\n df_new['avg_volume_30'] = df['volume'].rolling(window=21).mean().shift(1)\\n df_new['avg_volume_90'] = df['volume'].rolling(window=63).mean().shift(1)\\n df_new['avg_volume_365'] = df['volume'].rolling(window=252).mean().shift(1)\\n \\n #average volume ratio\\n df_new['ratio_avg_volume_5_30'] = df_new['avg_volume_5'] / df_new['avg_volume_30']\\n df_new['ratio_avg_volumee_5_90'] = df_new['avg_volume_5'] / df_new['avg_volume_90'] \\n df_new['ratio_avg_volume_5_365'] = df_new['avg_volume_5'] / df_new['avg_volume_365']\\n df_new['ratio_avg_volume_30_90'] = df_new['avg_volume_30'] / df_new['avg_volume_90']\\n df_new['ratio_avg_volume_30_365'] = df_new['avg_volume_30'] / df_new['avg_volume_365']\\n df_new['ratio_avg_volume_90_365'] = df_new['avg_volume_90'] / df_new['avg_volume_365'] \\n \\n \\n # standard deviation of prices\\n df_new['std_price_5'] = df['close'].rolling(window=5).std().shift(1)\\n df_new['std_price_30'] = df['close'].rolling(window=21).std().shift(1)\\n df_new['std_price_90'] = df['close'].rolling(window=63).std().shift(1) \\n df_new['std_price_365'] = df['close'].rolling(window=252).std().shift(1)\\n \\n # standard deviation ratio of prices \\n df_new['ratio_std_price_5_30'] = df_new['std_price_5'] / df_new['std_price_30']\\n df_new['ratio_std_price_5_90'] = df_new['std_price_5'] / df_new['std_price_90']\\n df_new['ratio_std_price_5_365'] = df_new['std_price_5'] / df_new['std_price_365']\\n df_new['ratio_std_price_30_90'] = df_new['std_price_30'] / df_new['std_price_90'] \\n df_new['ratio_std_price_30_365'] = df_new['std_price_30'] / df_new['std_price_365'] \\n df_new['ratio_std_price_90_365'] = df_new['std_price_90'] / df_new['std_price_365'] \\n \\n \\n # standard deviation of volumes\\n df_new['std_volume_5'] = df['volume'].rolling(window=5).std().shift(1)\\n df_new['std_volume_30'] = df['volume'].rolling(window=21).std().shift(1)\\n df_new['std_volume_90'] = df['volume'].rolling(window=63).std().shift(1)\\n df_new['std_volume_365'] = df['volume'].rolling(window=252).std().shift(1)\\n \\n #standard deviation ratio of volumes\\n df_new['ratio_std_volume_5_30'] = df_new['std_volume_5'] / df_new['std_volume_30']\\n df_new['ratio_std_volume_5_90'] = df_new['std_volume_5'] / df_new['std_volume_90']\\n df_new['ratio_std_volume_5_365'] = df_new['std_volume_5'] / df_new['std_volume_365'] \\n df_new['ratio_std_volume_30_90'] = df_new['std_volume_30'] / df_new['std_volume_90']\\n df_new['ratio_std_volume_30_365'] = df_new['std_volume_30'] / df_new['std_volume_365']\\n df_new['ratio_std_volume_90_365'] = df_new['std_volume_90'] / df_new['std_volume_365'] \\n \\n # return\\n df_new['return_1'] = ((df['close'] - df['close'].shift(1)) / df['close'].shift(1)).shift(1)\\n df_new['return_5'] = ((df['close'] - df['close'].shift(5)) / df['close'].shift(5)).shift(1)\\n df_new['return_30'] = ((df['close'] - df['close'].shift(21)) / df['close'].shift(21)).shift(1)\\n df_new['return_90'] = ((df['close'] - df['close'].shift(63)) / df['close'].shift(63)).shift(1) \\n df_new['return_365'] = ((df['close'] - df['close'].shift(252)) / df['close'].shift(252)).shift(1)\\n \\n #average of return\\n df_new['moving_avg_5'] = df_new['return_1'].rolling(window=5).mean()\\n df_new['moving_avg_30'] = df_new['return_1'].rolling(window=21).mean()\\n df_new['moving_avg_90'] = df_new['return_1'].rolling(window=63).mean()\\n df_new['moving_avg_365'] = df_new['return_1'].rolling(window=252).mean()\\n \\n # the target\\n df_new['close'] = df['close']\\n df_new = df_new.dropna(axis=0)\\n return df_new\",\n \"def gain_loss_month_daily(current_data):\\n months = current_data.groupby(pd.Grouper(freq='MS'))\\n losses_monthly = pd.DataFrame(0, columns=current_data.columns, index=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\\n for mo in months:\\n # Per month per year, sum daily gains/losses\\n monthly_sum = np.zeros((mo[1].shape[1],))\\n for i in range(mo[1].shape[0] - 1):\\n # loop over all days in month, except last day\\n monthly_sum = monthly_sum + (mo[1].iloc[i+1] - mo[1].iloc[i])\\n mon_str = str(mo[0].month_name()[:3])\\n for index, val in monthly_sum.items():\\n if val < 0:\\n losses_monthly.loc[mon_str, index] = losses_monthly.loc[mon_str, index] + 1\\n\\n return losses_monthly\",\n \"def series_to_supervised(data, n_in=1, n_out=1, dropnan=True, stride=None, dates=False, leaks=True):\\n df = pd.DataFrame(data)\\n \\n time = None\\n if 'date' in df.columns:\\n time = 'date'\\n elif 'time' in df.columns:\\n time = 'time'\\n if time != None:\\n df = df.drop([time], axis=1)\\n \\n if 'leak' in df.columns:\\n df = df.drop(['leak'], axis=1) \\n n_vars = df.shape[1]\\n times_column = list()\\n if dates and time != None:\\n times_column = data[time]\\n del data\\n \\n cols, names, pivots = list(), list(), list()\\n \\n # input sequence (t-n, ... t-1)\\n for i in range(n_in, 0, -1):\\n cols.append(df.shift(i))\\n names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\\n\\t# forecast sequence (t, t+1, ... t+n)\\n for i in range(0, n_out):\\n cols.append(df.shift(-i))\\n if i == 0:\\n names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\\n else:\\n names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\\n\\t# put it all together\\n agg = pd.concat(cols, axis=1)\\n \\n agg.columns = names\\n\\n #stride - delete windows\\n if stride != None:\\n indexes_to_drop = list()\\n for i in range(stride, agg.shape[0], stride):\\n print(\\\"index\\\", i)\\n pivots += [i]\\n \\n onset = 0\\n offset = pivots[0]\\n for i in range(0, len(pivots)):\\n print(\\\"onset\\\", onset)\\n print(\\\"offset\\\", offset)\\n to_drop = [ x for x in range(onset,offset)]\\n indexes_to_drop += to_drop\\n try:\\n onset = pivots[i] + 1\\n offset = pivots[i+1]\\n \\n except IndexError:\\n onset = pivots[i] + 1\\n offset = agg.shape[0]\\n to_drop = [ x for x in range(onset,offset)]\\n indexes_to_drop += to_drop\\n \\n \\n \\n print(\\\"indexes_to_drop\\\", indexes_to_drop)\\n \\n agg.drop(df.index[indexes_to_drop], inplace=True)\\n \\\"\\\"\\\"\\n if dates and time!=None:\\n agg[time] = times_column\\n \\\"\\\"\\\" \\n # drop rows with NaN values \\n if dropnan:\\n agg.dropna(inplace=True)\\n \\n\\n return agg\",\n \"def prepare_reg_data_for_prediction(dataframe, model_dict, user_keyword, task_name):\\r\\n parent_dir = Path.cwd().parent\\r\\n pickle_dir = parent_dir.joinpath('default_results', 'pickle_files_feat_eng')\\r\\n\\r\\n feature_X_user,affect_dataframe, affect_index_dataframe = df, df, df\\r\\n emo_X_test_dict = {}\\r\\n affect_index_dict ={}\\r\\n\\r\\n for emotion, model_prop in model_dict.items():\\r\\n #Get the data with the emotion class\\r\\n if user_keyword == 'validation':\\r\\n affect_dataframe = dataframe[dataframe['Affect Dimension'] == 1]\\r\\n affect_index_list = dataframe.index[dataframe['Affect Dimension'] == 1].tolist()\\r\\n else:\\r\\n affect_dataframe = dataframe[dataframe[emotion] == 1]\\r\\n affect_index_list = dataframe.index[dataframe[emotion] == 1].tolist()\\r\\n test_tweets = affect_dataframe.iloc[:, [0, 1, 2]]\\r\\n\\r\\n #Perform preprocessing, feature extraction and transformation for the tweets to be predicted\\r\\n print(emotion, test_tweets.shape)\\r\\n if test_tweets.empty == False:\\r\\n preprocessed_X_user = Preprocessor.perform(test_tweets, emotion, user_keyword, task_name)\\r\\n feature_X_user = Feature_Transformer.perform(preprocessed_X_user, emotion, user_keyword, task_name)\\r\\n vectorizer = Dictionaries.vectorizer_dict[model_prop[2]]\\r\\n\\r\\n #Fit transform the vectorizer with the corresponding preprocessed training data\\r\\n if os.path.exists(pickle_dir.joinpath(emotion + '_r_train_preprocess_df.pkl')):\\r\\n preprocess_train_df = pd.read_pickle(pickle_dir.joinpath(emotion + '_r_train_preprocess_df.pkl'))\\r\\n train_vect = vectorizer.fit_transform(preprocess_train_df['preprocessed_text'].values)\\r\\n train_vect_df = pd.DataFrame(train_vect.toarray(), columns=vectorizer.get_feature_names())\\r\\n print(emotion, 'train-shape', train_vect_df.shape, sep='\\\\n')\\r\\n else:\\r\\n #If the file doesnt exist, exit the program with instructions\\r\\n print('\\\\nRequired files does not exist.\\\\n\\\\n Please, train the models first by running > Modelling.py')\\r\\n sys.exit(1)\\r\\n\\r\\n # Use the same vectorizer to transform test data and then perform the feature union\\r\\n vector_X = vectorizer.transform(preprocessed_X_user['preprocessed_text'].values)\\r\\n test_vect_df = pd.DataFrame(vector_X.toarray(), columns=vectorizer.get_feature_names())\\r\\n X_test = pd.DataFrame(pd.concat([test_vect_df, feature_X_user], axis=1)) #####?\\r\\n emo_X_test_dict[emotion] = X_test\\r\\n affect_index_dict[emotion] = affect_index_list\\r\\n else:\\r\\n emo_X_test_dict[emotion] = pd.DataFrame\\r\\n affect_index_dict[emotion] = []\\r\\n\\r\\n return emo_X_test_dict, affect_index_dict\",\n \"def build_predictions() -> List[str]:\\n\\n with open(os.path.join(UTILS_PATH, STREETS_FOR_PREDICTIONS_FILE)) as f:\\n streets_for_predictions = f.read().split('\\\\n')[:-1]\\n\\n street_to_id = {street: ind for ind, street in enumerate(streets_for_predictions)}\\n\\n yesterday = datetime.today() - timedelta(days=7) # shift for 7 days\\n last_month = set([(yesterday - timedelta(days=x)).strftime(DAY_FORMAT) for x in range(30)])\\n\\n with open(os.path.join(UTILS_PATH, CURRENT_FILENAMES_FILE), \\\"r\\\") as f:\\n FILENAMES = json.load(f)\\n\\n filename = FILENAMES[CRASHES_AFTER_ETL]\\n conn = sql.connect(f\\\"{METABASE_DATA_PATH}{filename}.db\\\")\\n query = f\\\"{SELECT_DATETIME_STREETS_QUERY} '{filename}'\\\"\\n df = pd.read_sql_query(query, conn)\\n\\n df[DATETIME] = df[DATETIME].apply(lambda x: x.split(\\\"T\\\")[0])\\n df = df[df[STREET_NAME].isin(streets_for_predictions)]\\n df = df.reset_index(drop=True)\\n\\n df = df[df[DATETIME].isin(last_month)]\\n df[DATETIME] = pd.to_datetime(df[DATETIME])\\n df = df.reset_index(drop=True)\\n\\n uniq_dates = sorted(set(df[DATETIME]))\\n date_to_id = {date: idd for idd, date in enumerate(uniq_dates)}\\n\\n X = np.zeros((len(street_to_id), len(date_to_id)), dtype=np.uint32)\\n\\n for row in df.iterrows():\\n info = row[1]\\n date = info[DATETIME]\\n street = info[STREET_NAME]\\n\\n date_id = date_to_id[date]\\n street_id = street_to_id[street]\\n\\n X[street_id, date_id] += 1\\n\\n model = CatBoostRegressor()\\n model.load_model(os.path.join(UTILS_PATH, PREDICTOR_FILE))\\n\\n y_pred = model.predict(X)\\n top_10_streets_inds = np.argsort(y_pred)[::-1][:10]\\n top_10_streets = [streets_for_predictions[ind] for ind in top_10_streets_inds]\\n\\n logging.info(\\\"Predictions successfully made.\\\")\\n\\n return top_10_streets\",\n \"def get_perf(self) :\\n self.train()\\n self.df_true = self.df_true[self.length:]\\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)\",\n \"def generate_dataframe(forecast, observed):\\n dataframe = pd.DataFrame(columns=COLUMNS, dtype=str)\\n\\n # Write cumulative forecasts.\\n forecast_date_str = FORECAST_DATE.strftime(\\\"%Y-%m-%d\\\")\\n for cum_week in sorted(forecast.keys()):\\n target_end_date = FIRST_WEEK + ((cum_week - 1) * datetime.timedelta(7)) \\n target_end_date_str = target_end_date.strftime(\\\"%Y-%m-%d\\\")\\n # Terminate the loop after 8 weeks of forecasts.\\n if cum_week >= 8:\\n break\\n \\n # Skip forecasts before the forecast date.\\n if target_end_date <= FORECAST_DATE:\\n continue\\n\\n # Write a row for \\\"weeks ahead\\\" if forecast end day is a Saturday.\\n if target_end_date >= FIRST_WEEK and target_end_date.weekday() == 5:\\n target = str(cum_week) + \\\" wk ahead cum death\\\"\\n for state_id in forecast[cum_week].keys():\\n for quantile in forecast[cum_week][state_id].keys():\\n val = observed[(FORECAST_DATE - datetime.timedelta(1)).strftime(\\\"%Y-%m-%d\\\")][state_id]\\n for i in range(1, cum_week + 1):\\n val += forecast[i][state_id][quantile]\\n if quantile == \\\"point\\\":\\n dataframe = dataframe.append(\\n generate_new_row(\\n forecast_date=forecast_date_str,\\n target=target,\\n target_end_date=target_end_date_str,\\n location=str(state_id),\\n type=\\\"point\\\",\\n quantile=\\\"NA\\\",\\n value=val\\n ), ignore_index=True)\\n else:\\n dataframe = dataframe.append(\\n generate_new_row(\\n forecast_date=forecast_date_str,\\n target=target,\\n target_end_date=target_end_date_str,\\n location=str(state_id),\\n type=\\\"quantile\\\",\\n quantile=quantile,\\n value=val\\n ), ignore_index=True)\\n \\n # Write incident forecasts.\\n forecast_date_str = FORECAST_DATE.strftime(\\\"%Y-%m-%d\\\")\\n for cum_week in sorted(forecast.keys()):\\n target_end_date = FIRST_WEEK + ((cum_week - 1) * datetime.timedelta(7)) \\n target_end_date_str = target_end_date.strftime(\\\"%Y-%m-%d\\\")\\n # Terminate the loop after 8 weeks of forecasts.\\n if cum_week >= 8:\\n break\\n \\n # Skip forecasts before the forecast date.\\n if target_end_date <= FORECAST_DATE:\\n continue\\n\\n if target_end_date >= FIRST_WEEK and target_end_date.weekday() == 5:\\n target = str(cum_week) + \\\" wk ahead inc death\\\"\\n for state_id in forecast[cum_week].keys():\\n for quantile in forecast[cum_week][state_id].keys():\\n if quantile == \\\"point\\\":\\n dataframe = dataframe.append(\\n generate_new_row(\\n forecast_date=forecast_date_str,\\n target=target,\\n target_end_date=target_end_date_str,\\n location=str(state_id),\\n type=\\\"point\\\",\\n quantile=\\\"NA\\\",\\n value=forecast[cum_week][state_id][quantile]\\n ), ignore_index=True)\\n else:\\n dataframe = dataframe.append(\\n generate_new_row(\\n forecast_date=forecast_date_str,\\n target=target,\\n target_end_date=target_end_date_str,\\n location=str(state_id),\\n type=\\\"quantile\\\",\\n quantile=quantile,\\n value=forecast[cum_week][state_id][quantile]\\n ), ignore_index=True)\\n \\n return dataframe\",\n \"def get_idx_lag(idx_start, ar_iteration, forecast_cycle, input_k):\\n return idx_start + (forecast_cycle * ar_iteration) + input_k\",\n \"def gain_loss_whole_month(current_data):\\n months = current_data.groupby(pd.Grouper(freq='MS'))\\n losses_monthly = pd.DataFrame(0, columns=current_data.columns, index=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\\n for mo in months:\\n # Per month per year, subtract final value from starting value\\n diff = (mo[1].iloc[-1] - mo[1].iloc[0]).rename(mo[0])\\n mon_str = str(diff.name.month_name()[:3])\\n for index, val in diff.items():\\n if val < 0:\\n losses_monthly.loc[mon_str, index] = losses_monthly.loc[mon_str, index] + 1\\n\\n return losses_monthly\",\n \"def preprocess(df):\\n \\n # drop the following columns - irrelevant now\\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\\n , 'tmdbId', 'popularity', 'year']\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n # drop all of the language columns\\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\\\"lan\\\"]\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n\\n # loop through the columns we want to aggregate\\n for col_type in [\\n \\\"original_language_\\\"\\n , \\\"prod_comp_cntry_\\\"\\n , \\\"prod_comp_names_\\\"\\n , \\\"writers_\\\"\\n , \\\"actors_\\\"\\n , \\\"genres_\\\"\\n , \\\"director_\\\"\\n ]:\\n # create a dictionary of each unique value and its frequency\\n val_freq = {}\\n for col in df.columns:\\n if col.startswith(col_type):\\n val_freq[col] = df[col].sum()\\n\\n # create a dataframe from this dictionary; sort by count\\n counts = pd.DataFrame.from_dict(\\n val_freq\\n , orient='index'\\n , columns=['count']\\n ).sort_values('count', ascending=False)\\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\\n\\n # handle special case of production company country\\n if col_type == \\\"prod_comp_cntry_\\\":\\n DROP_COLUMNS = [col for col in counts.index][3:]\\n\\n # handle special case of directors\\n elif col_type == \\\"director_\\\":\\n DIRECTOR_COLS = [col for col in df.columns\\n if col.startswith(\\\"director_\\\")\\n and col!=\\\"director_pop\\\"]\\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\\n DROP_COLUMNS = DIRECTOR_COLS\\n\\n # handle special case of actors\\n elif col_type == \\\"actors_\\\":\\n ACTORS_COLS = [col for col in df.columns if \\\"actors\\\" in col]\\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\\n DROP_COLUMNS = ACTORS_COLS\\n\\n # handle all the other cases\\n else:\\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\\n\\n\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n ##########################################################################\\n # adjust the data for inflation\\n CPI_tf = df['CPIAUCSL'].max()\\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n # no longer need CPI data\\n df.drop('CPIAUCSL', axis=1, inplace=True)\\n \\n ########################################################################## \\n # add in useful features about the cast and crew \\n df['cast_crew_sum_pop'] = (\\n df['director_pop']\\n + df['avg_actor_pop']\\n + df['avg_writer_pop']\\n )\\n df['cast_crew_product_pop'] = (\\n df['director_pop']\\n * df['avg_actor_pop']\\n * df['avg_writer_pop']\\n )\\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\\n df = df.query('10000 <= revenue').copy()\\n df = df.query('100000 <= budget').copy()\\n df.drop('sum_actor_pop', axis=1, inplace=True)\\n df.drop('min_writer_pop', axis=1, inplace=True)\\n\\n # code to transform columns\\n for col in [\\n \\\"budget\\\", \\\"director_pop\\\", \\\"avg_writer_pop\\\"\\n , \\\"max_writer_pop\\\", \\\"avg_actor_pop\\\", \\\"max_actor_pop\\\"\\n , \\\"min_actor_pop\\\", 'cast_crew_sum_pop'\\n , 'cast_crew_product_pop'\\n ]:\\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\\n df.drop(col, axis=1, inplace=True)\\n \\n return df\",\n \"def dataset_extract_features_from_date(dataset,date_feature): \\n dataset['dayofmonth'] = dataset[date_feature].dt.day\\n dataset['dayofyear'] = dataset[date_feature].dt.dayofyear \\n dataset['dayofweek'] = dataset[date_feature].dt.dayofweek\\n dataset['month'] = dataset[date_feature].dt.month\\n dataset['year'] = dataset[date_feature].dt.year\\n dataset['weekofyear'] = dataset[date_feature].dt.weekofyear\\n dataset['is_month_start'] = (dataset[date_feature].dt.is_month_start).astype(int)\\n dataset['is_month_end'] = (dataset[date_feature].dt.is_month_end).astype(int)\\n return dataset\",\n \"def preprocess(data,scale):\\n ##log_transformation\\n #data['log_sale_price'] = np.log(data['sale_price'])\\n #data['log_lot_area'] = np.log(data['lot_area'])\\n #data['house_age'] = data['year_sold']- data['year_built']\\n \\n y = data['stay']\\n \\n #sales['log_sale_price'] = np.log(sales['sale_price'])\\n #sales['log_lot_area'] = np.log(sales['lot_area'])\\n #sales['house_age'] = sales['year_sold']- sales['year_built']\\n data_dummy = data.copy()\\n \\n #dummy coding\\n data_scale = pd.get_dummies(data_dummy).drop(columns = ['stay'])\\n\\n \\n #scale the value\\n if scale == True:\\n S = StandardScaler().fit(data_scale)\\n data_scale = S.transform(data_scale)\\n \\n return y, data_scale\",\n \"def _assess(self):\\n # get eruptions\\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\\n # check if data file exists\\n self.exists = os.path.isfile(self.file)\\n if not self.exists:\\n t0 = datetime(2011,1,1)\\n t1 = datetime(2011,1,2)\\n self.update(t0,t1)\\n # check date of latest data in file\\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def prepare_X_array(Y, y_var, X_vars, predef_pcas, X_var_definitions, \\n X_clxp_definitions, include_persistence=False):\\n \\n from sklearn.preprocessing import StandardScaler #, Imputer\\n from sklearn.impute import SimpleImputer\\n from sklearn.utils import resample\\n \\n # An empty dataframe containing only dates as an index variable,\\n # and probably persistence variables\\n X = Y[y_var].to_dataframe().drop(columns=y_var)\\n if(include_persistence):\\n X['LCL_'+y_var+'-1'] = np.roll(Y[y_var], 1)\\n X['LCL_'+y_var+'-2'] = np.roll(Y[y_var], 2)\\n X['LCL_'+y_var+'-1'][0:1] = Y[y_var].values[0]\\n X['LCL_'+y_var+'-2'][0:2] = Y[y_var].values[0]\\n \\n # Variables from Climate Explorer (https://climexp.knmi.nl/)\\n for i,vr in enumerate(X_clxp_definitions): \\n index = read_monthly_indices_from_CLIMEXP(vr)\\n \\n index = index.resample(time='3M').mean()\\n index = index.groupby('time.season') - index.groupby('time.season').mean('time')\\n \\n X = pd.merge(X, index.to_dataframe(), left_index=True, right_index=True, how='left')\\n \\n for lag in X_clxp_definitions[vr]:\\n X[vr+'-'+str(lag)] = np.roll(X[vr], lag)\\n X[vr+'-'+str(lag)][0:lag] = X[vr][0]\\n \\n X = X.drop(columns=vr)\\n \\n # Remove unnecessary columns\\n drops = ['season','season_x','season_y','month_x','month_y','month',y_var]\\n for dr in drops:\\n try: X = X.drop(columns=dr)\\n except: pass\\n\\n # Variables from gridded datasets\\n X_pcas, X_eigs, X_errs = {}, {}, {}\\n for i,vr in enumerate(X_var_definitions):\\n \\n trn_yrs = X_var_definitions[vr][3]\\n all_yrs = X_var_definitions[vr][4]\\n\\n trn_idx = bool_index_to_int_index(np.isin(X_vars[i]['time.year'], trn_yrs))\\n all_idx = bool_index_to_int_index(np.isin(X_vars[i]['time.year'], all_yrs))\\n \\n try:\\n cps = predef_pcas[vr].transform(X_vars[i].values)\\n _, pca, svl = predef_pcas[vr], -99, -99\\n print('Predefined PCA model used')\\n except:\\n #_, pca, svl = apply_PCA(X_vars[i].values[trn_idx],X_var_definitions[vr][1])\\n cps, pca, svl = apply_PCA(X_vars[i].values,X_var_definitions[vr][1])\\n #cps = pca.transform(X_vars[i].values)\\n print('PCAs calculated from data')\\n \\n # Sampling uncertainty according to North et al. (1982)\\n eig = svl**2 \\n N = X_vars[i].values.shape[0]\\n sampling_err = eig*(2/N)**(0.5)\\n \\n X_pcas[vr] = pca; X_eigs[vr] = eig; X_errs[vr] = sampling_err\\n \\n for cpn in range(X_var_definitions[vr][1]):\\n vrb_name = vr+str(cpn+1)\\n X.loc[:,vrb_name] = cps[:,cpn]\\n \\n \\n for lag in X_var_definitions[vr][2]:\\n X[vrb_name+'-'+str(lag)] = np.roll(X[vrb_name], lag)\\n X[vrb_name+'-'+str(lag)][0:lag] = X[vrb_name][0]\\n \\n X = X.drop(columns=vrb_name)\\n \\n # Fill (potential) holes with mean values and normalize to N(0,1) \\n X[:] = SimpleImputer().fit_transform(X.values)\\n X[:] = StandardScaler().fit_transform(X.values)\\n\\n return X, X_pcas, X_eigs, X_errs\",\n \"def run():\\n\\n df = read_input() # the parameters\\n df = add_time_period(df) # a feature\\n df = is_holiday(df) # a feature\\n df = scale_continous(df) # continous feature transformation\\n df = encode_dummy(df) # categorical feature transformation\\n df = order_columns(df) # ordering model inputs\\n model = load_model() # the multiple linear regression model\\n prediction = int(model.predict(df)) # form a prediction\\n return prediction # return the prediction\",\n \"def add_change_features(df):\\n # copy input for comparison of outputs\\n df_copy = df.copy()\\n\\n # calculate interval change features\\n df_copy[\\\"Duration_Start\\\"] = (\\n df_copy[\\\"Schedule_Start\\\"] - df_copy[\\\"Design_Start\\\"]\\n ).dt.days\\n df_copy[\\\"Duration_End\\\"] = (\\n df_copy[\\\"Schedule_End\\\"] - df_copy[\\\"Design_Start\\\"]\\n ).dt.days\\n df_copy[\\\"Schedule_Change\\\"] = (\\n df_copy[\\\"Duration_End\\\"] - df_copy[\\\"Duration_Start\\\"]\\n )\\n df_copy[\\\"Budget_Change\\\"] = df_copy[\\\"Budget_End\\\"] - df_copy[\\\"Budget_Start\\\"]\\n\\n # define schedule change ratio\\n df_copy[\\\"Schedule_Change_Ratio\\\"] = (\\n df_copy[\\\"Schedule_Change\\\"] / df_copy[\\\"Duration_Start\\\"]\\n )\\n # define budget change ratio\\n df_copy[\\\"Budget_Change_Ratio\\\"] = (\\n df_copy[\\\"Budget_Change\\\"] / df_copy[\\\"Budget_Start\\\"]\\n )\\n\\n # define project metrics\\n df_copy[\\\"Budget_Abs_Per_Error\\\"] = (\\n df_copy[\\\"Budget_Start\\\"] - df_copy[\\\"Budget_End\\\"]\\n ).abs() / df_copy[\\\"Budget_End\\\"]\\n\\n df_copy[\\\"Budget_Rel_Per_Error\\\"] = (\\n df_copy[\\\"Budget_Start\\\"] - df_copy[\\\"Budget_End\\\"]\\n ).abs() / df_copy[\\\"Budget_Start\\\"]\\n\\n df_copy[\\\"Duration_End_Ratio\\\"] = (\\n df_copy[\\\"Duration_End\\\"] / df_copy[\\\"Duration_Start\\\"]\\n )\\n df_copy[\\\"Budget_End_Ratio\\\"] = (\\n df_copy[\\\"Budget_End\\\"] / df_copy[\\\"Budget_Start\\\"]\\n )\\n\\n # previously titled 'Mark Metric'\\n df_copy[\\\"Duration_Ratio_Inv\\\"] = (\\n df_copy[\\\"Duration_Start\\\"] / df_copy[\\\"Duration_End\\\"]\\n ) - 1\\n df_copy[\\\"Budget_Ratio_Inv\\\"] = (\\n df_copy[\\\"Budget_Start\\\"] / df_copy[\\\"Budget_End\\\"]\\n ) - 1\\n\\n return df_copy\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6364175","0.63334864","0.6267178","0.59978694","0.5987615","0.5957174","0.59301317","0.57890916","0.5736458","0.5725275","0.5689226","0.56639534","0.5641206","0.56295705","0.5626032","0.5600229","0.5582438","0.55348295","0.5518111","0.55100405","0.55089504","0.54968107","0.5489425","0.5479703","0.5453522","0.54415196","0.5408405","0.53857356","0.53754324","0.53715855","0.5355832","0.5347779","0.53469324","0.53123045","0.5308327","0.53072506","0.53015345","0.5294409","0.5294167","0.52826357","0.5278891","0.5268675","0.52636445","0.5257213","0.52554417","0.5244676","0.52084076","0.5207043","0.5201949","0.51970464","0.51936936","0.51843214","0.5171538","0.51707786","0.51696986","0.51535076","0.51517254","0.51451194","0.5142371","0.5132657","0.5124101","0.51203483","0.5110203","0.5102311","0.509621","0.50857615","0.50839174","0.50697184","0.50647813","0.50644976","0.5061948","0.5058297","0.5055593","0.5049912","0.5046184","0.504353","0.504017","0.5039982","0.50346214","0.50336087","0.50309116","0.5030764","0.50155634","0.50061876","0.50049466","0.50016546","0.5001327","0.4987963","0.4987476","0.49870557","0.49867472","0.4983297","0.4973247","0.49690127","0.49670437","0.49609393","0.49535605","0.49506104","0.49500987","0.49490795"],"string":"[\n \"0.6364175\",\n \"0.63334864\",\n \"0.6267178\",\n \"0.59978694\",\n \"0.5987615\",\n \"0.5957174\",\n \"0.59301317\",\n \"0.57890916\",\n \"0.5736458\",\n \"0.5725275\",\n \"0.5689226\",\n \"0.56639534\",\n \"0.5641206\",\n \"0.56295705\",\n \"0.5626032\",\n \"0.5600229\",\n \"0.5582438\",\n \"0.55348295\",\n \"0.5518111\",\n \"0.55100405\",\n \"0.55089504\",\n \"0.54968107\",\n \"0.5489425\",\n \"0.5479703\",\n \"0.5453522\",\n \"0.54415196\",\n \"0.5408405\",\n \"0.53857356\",\n \"0.53754324\",\n \"0.53715855\",\n \"0.5355832\",\n \"0.5347779\",\n \"0.53469324\",\n \"0.53123045\",\n \"0.5308327\",\n \"0.53072506\",\n \"0.53015345\",\n \"0.5294409\",\n \"0.5294167\",\n \"0.52826357\",\n \"0.5278891\",\n \"0.5268675\",\n \"0.52636445\",\n \"0.5257213\",\n \"0.52554417\",\n \"0.5244676\",\n \"0.52084076\",\n \"0.5207043\",\n \"0.5201949\",\n \"0.51970464\",\n \"0.51936936\",\n \"0.51843214\",\n \"0.5171538\",\n \"0.51707786\",\n \"0.51696986\",\n \"0.51535076\",\n \"0.51517254\",\n 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with an exogenous variable, CASANF0URN, which which represents unemployment rate in San Francisco from the previous year."},"document":{"kind":"string","value":"def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\n columns=['month_year', 'predicted_evictions'])\n\n now = datetime.datetime.now()\n\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\n model.fit()\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\n\n predicted = y_hat.Eviction_Notice.values.tolist()\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\n temp_df['month_year']=months_list\n\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\n predictions_df = predictions_df[1:]\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\n\n return predictions_df, y_hat"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def arimax_by_month_train_test(model_data,future_data,months_ahead):\n transformed_df = arima_by_zip_data_transform(model_data)\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\n\n y_train = transformed_df\n\n\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]\n\n now = datetime.datetime.now()\n this_month = pd.to_datetime(str(now.year)+ '-'+ str(now.month) + '-'+str(1))\n y_test=y_test[(y_test.Month_Year>this_month)&(y_test.Month_Year<(this_month+pd.offsets.MonthBegin(months_ahead+1)))].sort_values('Month_Year')\n months_list = np.asarray(y_test.Month_Year)\n\n y_test = y_test.set_index(['Month_Year'],inplace=False)\n\n\n for i,row in y_train.Eviction_Notice.iteritems():\n if row>(y_train.Eviction_Notice.mean()+ y_train.Eviction_Notice.std()*3):\n y_train.Eviction_Notice.iloc[i]=y_train.Eviction_Notice.mean()\n\n y_train = y_train[['Month_Year','Eviction_Notice','CASANF0URN']].set_index(['Month_Year'],inplace=False)\n\n return y_train, y_test,months_list","def __init__(self):\r\n \r\n #self.max_stocks = 100\r\n self.max_stocks = 200\r\n \"\"\" cv_factor determines what portion of stocks to put in cross validation set and what portion\r\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\r\n set. cv_factor = 3 means every third stock goes into cross validation set \"\"\"\r\n self.cv_factor = 2 \r\n \"\"\" future_day is how many training days in the future we train for. Setting future_day = 25\r\n means we are measuring how the stock does 25 days out \"\"\"\r\n self.future_day = 25\r\n \"\"\" The train_dates are the dates for training and cross validation\"\"\"\r\n self.train_dates = []\r\n first_train_date = dateutl.days_since_1900('2001-01-01')\r\n num_train_dates = 10\r\n train_date_increment = 60\r\n self.train_dates.append(first_train_date)\r\n for iday in range(1,num_train_dates):\r\n last_train_date = self.train_dates[iday-1]\r\n self.train_dates.append(last_train_date + train_date_increment)\r\n \"\"\"self.train_dates[1] -= 1 \"\"\"\r\n \r\n \"\"\" test_dates are the dates we are using for testing \"\"\"\r\n self.test_dates = []\r\n first_test_date = dateutl.days_since_1900('2010-01-01')\r\n num_test_dates = 10\r\n test_date_increment = 60\r\n self.test_dates.append(first_test_date) \r\n for iday in range(1,num_test_dates):\r\n last_test_date = self.test_dates[iday-1]\r\n self.test_dates.append(last_test_date + test_date_increment)\r\n \"\"\"self.test_dates[1] -= 1\r\n self.test_dates[3] += 1\r\n self.test_dates[4] += 3\r\n self.test_dates[5] += 4\r\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\"\"\"\r\n \"\"\"train_history_days and train_increment set how many historical days we use to\r\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\r\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\r\n as input features \"\"\"\r\n self.train_days = 21\r\n self.train_increment = 5\r\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\r\n 'cmo','mfi','natr','roc','stoch','uo']\r\n \"\"\" output is just a boolean about calling the output function to write out \r\n appropriate X and y matricies. The default is False meaning do not write out\r\n matricies \"\"\"\r\n self.output = False","def get_pred_score(file_name, kpi, start_train, end_train, periods=365):\n # set names for columns in order\n names = ['date', 'handle_time', 'handle_time_forecast',\n 'volume', 'volume_forecast']\n df = pd.read_csv('./data/'+file_name, names=names, header=1, index_col=0)\n\n df['date'] = pd.to_datetime(df['date'])\n df.index = df['date']\n\n # create column for aht\n df['aht'] = df['handle_time']/df['volume']\n df['aht_forecast'] = df['handle_time_forecast']/df['volume_forecast']\n\n # df = df[(df['aht'] > 200) & (df['aht'] < 1500)]\n\n df1 = df.copy()\n\n # create business unit name\n bu = file_name[:6]\n\n # # remove outliers if it helps the model\n # df = remove_outliers(df, bu)\n\n # remove holidays\n df = df[~df.index.isin(h.iloc[:,0].tolist())]\n\n # remove weekends\n df = df[~df.index.weekday.isin([5,6])]\n\n # # plot data in a notebook\n # plot data\n # plot_time_vol(df)\n\n # create training\n df = create_training_data(df, kpi, start_train, end_train)\n\n \n # display(df.head())\n m,future = create_forecast(df, periods=int(periods))\n forecast = m.predict(future)\n\n # # plot forecast in a notebook\n # plot_forecast(forecast)\n\n # set forecast beginning and end date\n f = forecast.copy()\n end_test = df1.index.max()\n f = f[(forecast['ds'] > end_train) & (f['ds'] <= end_test)]\n\n # create validation dataset\n df2 = df1.copy()\n df2['ds'] = pd.to_datetime(df2.index.date) \n\n # set start date of validation data equal to June 1st, 2020 or any other date\n df2 = df2[df2['ds'] > end_train]\n\n # remove weekends and holidays from data\n df2 = df2[~df2.index.isin(h.iloc[:,0].tolist())]\n df2 = df2[~df2.index.weekday.isin([5,6])]\n os.makedirs('./preds/'+kpi, exist_ok=True)\n\n future_forecast=forecast[['ds', 'yhat_lower', 'yhat', 'yhat_upper']][forecast['ds']>datetime.datetime.now()]\n\n f[['ds', 'yhat_lower', 'yhat', 'yhat_upper']].to_csv('./preds/'+kpi+'/'+bu+'.csv')\n forecast.to_csv('./preds/analysis/'+kpi+'_'+bu+'.csv')\n\n # Validate test data, it must match for scoring\n mae = evaluate_model(f,df2, kpi, metric='mae')\n\n mae.update({'kpi': kpi, 'start_train':start_train, 'end_train': end_train, 'end_test': end_test}) \n\n curr = pd.read_csv('scores/'+kpi+'_score.csv',index_col=0)\n new = pd.DataFrame(mae, index=[bu])\n\n new.to_csv('./scores/current/'+bu+'_'+kpi+'.csv')\n \n if bu not in curr.index:\n curr = pd.concat([curr, new], 0)\n # create log everytime a score is superceded\n elif new.loc[bu,'prophet'] < curr.loc[bu,'prophet']:\n data = pd.concat([curr.loc[[bu],:],new.loc[[bu],:]],0)\n data.to_csv('./scores/logs/'+bu+'_'+kpi+'_'+datetime.datetime.now()\n .strftime(\"%b %d %Y %H:%M:%S\").replace(' ', '_')+'.csv')\n curr.update(new)\n curr.to_csv('./scores/'+kpi+'_score.csv')\n\n return 'error with metric'\n print('\\ntraining',file_name, 'on', kpi, 'start train:',start_train, \n 'end train:', end_train, 'end_test:', )","def dataDescription(train):\n print(train.describe())\n # No NaNs\n print(train.head())\n # 5 columns: store number, department number, date, weekly sales, isHoliday\n\n # Lets view the weekly sales, grouped by date\n fig, ax = plt.subplots()\n salesByDate = train.groupby('Date').mean().Weekly_Sales\n salesByDate.plot()\n plt.show()\n fig, ax = plt.subplots()\n autocorrelation_plot(salesByDate)\n plt.show()\n # Lets plot all store's sales by date on the same graph\n salesByStore = train.groupby(by=['Store', 'Date']).mean().Weekly_Sales\n salesByStore.unstack(level=0).plot()\n # Lets plot all department's sales by date on the same graph\n salesByStore = train.groupby(by=['Dept', 'Date']).mean().Weekly_Sales\n salesByStore.unstack(level=0).plot()\n # It looks like all stores have peaks at the same times, however just a\n # single department. Could this be the holiday department?\n fig, ax = plt.subplots()\n holidayDates = train.groupby('Date').mean().IsHoliday\n holidayDates.plot()\n plt.show()\n # Very few holiday days...\n # What are the average sales on holidays vs not holidays\n holidaySales = train[train['IsHoliday'] == True].Weekly_Sales.mean()\n nonHolidaySales = train[train['IsHoliday'] == False].Weekly_Sales.mean()\n print(\"The mean weekly sales on holidays is {:.2f} \".format(holidaySales) +\n \"and then {:.2f} for non-holidays.\".format(nonHolidaySales))\n\n # Lets looks at how complete the data is. Create a series with all possible\n # dates in. Compare with each store and department.\n trainDates = pd.to_datetime(train.Date)\n trainDates = pd.DatetimeIndex(trainDates.unique())\n# First confirm there are no missing dates in the whole range.\n# trainDatesTest = pd.date_range(trainDates.min(),\n# trainDates.max(), freq='7D')\n# (trainDates == trainDatesTest).all()\n stores = np.unique(train['Store'])\n depts = np.unique(train['Dept'])\n missingDates = defaultdict(int)\n for store in stores:\n for dept in depts:\n trainThis = train[train['Store'] == store]\n trainThis = trainThis[trainThis['Dept'] == dept]\n missing = len(trainDates) - len(pd.DatetimeIndex(trainThis.Date))\n missingDates[missing] += 1\n\n # The majority miss nothing. 314 stores don't have certain departments\n # - as can be expected!\n fig, ax = plt.subplots()\n ax.bar(list(missingDates.keys()), missingDates.values())","def form_data(stocks, init_param):\r\n \r\n rs = stocks[1].rsi\r\n ts = stocks[1].tsi\r\n a = 1\r\n \r\n \r\n for date in init_param.train_dates:\r\n try:\r\n training_data\r\n except NameError:\r\n training_data = LearningData()\r\n training_data.construct(stocks, date, init_param.future_day, init_param.features)\r\n else:\r\n training_data.append(stocks, date, init_param.future_day, init_param.features)\r\n \r\n for date in init_param.test_dates:\r\n try:\r\n test_data\r\n except NameError:\r\n test_data = LearningData()\r\n test_data.construct(stocks, date, init_param.future_day, init_param.features)\r\n else:\r\n test_data.append(stocks, date, init_param.future_day, init_param.features)\r\n \r\n #reference_date = dateutl.days_since_1900('1991-01-01')\r\n #test_data.construct(stocks,[reference_date, day_history, init_param.future_day])\r\n \r\n return training_data, test_data","def sts_predict_canteen_values(full_df, prediction_df, future=True):\n\n df = full_df.copy()\n df = df.filter([\"Canteen\"])\n test_period = prediction_df.shape[0]\n\n # The days between last row in dataset and today needs to be calculated\n if future is True:\n # Finding days between end date of dataset and today\n date_today = datetime.now()\n end_date = df.index[-1]\n future_test_period = test_period + (date_today - end_date).days - 1\n\n # The whole dataframe is now the train set\n train = df\n # Creating a new dataframe containing all dates between end date and today + future days from prediction_df\n date_df = pd.DataFrame()\n date_df[\"date\"] = pd.date_range(\n (end_date + timedelta(1)).date(),\n periods=future_test_period,\n freq=\"D\",\n )\n date_df.index = pd.to_datetime(date_df.pop(\"date\"))\n # Setting the new df as test set\n test = date_df\n else:\n # If future = False, the test period is the last part of the full_df, train is everything up until this point\n train = df.iloc[:-test_period]\n test = df.iloc[-test_period:]\n\n # Make prediction\n _, predictions = prediction(train, test)\n return predictions.iloc[-test_period:]","def run_trainig_and_testing(company = \"FB\", training_window = 60,\n trainig_period_y=8, testing_period_y=1,\n prediction_day=dt.datetime.now() ):\n # Load data for model\n training_data, testing_data = fetch_data(company, trainig_period_y, testing_period_y, prediction_day)\n # prepare data\n # scaler is object which will scale whole series values to defined feature_range interval\n scaler = MinMaxScaler(feature_range=(0,1))\n # we are insterested only in the closing values\n # optionaly we can use AdjClose -> adjusted for splits\n scaled_data = scaler.fit_transform(training_data['Close'].values.reshape(-1,1))\n\n # x is our learning data\n x_train, y_train = create_training_tensors(training_window, scaled_data)\n\n # build the model\n model = create_model((x_train.shape[1], 1))\n model.fit(x_train, y_train, epochs=5, batch_size=32)\n\n # for future we can save the model here and load for future\n\n \"\"\"\n TESTING MODEL ACCURACY\n \"\"\"\n\n # load test data\n\n tst_start_dt = dt.datetime(2020,1,1)\n tst_end_dt = dt.datetime(2021,1,1)\n\n\n actual_prices = testing_data['Close'].values\n total_dataset = pd.concat((training_data['Close'], testing_data['Close']), axis=0)\n\n model_inputs = total_dataset[len(total_dataset) - len(testing_data) - training_window:].values\n model_inputs = model_inputs.reshape(-1,1)\n model_inputs = scaler.fit_transform(model_inputs)\n\n # make prediction on test data\n\n x_test, y_test = create_training_tensors(training_window, model_inputs)\n\n predicted_prices = model.predict(x_test)\n # predicted prices are scaled to (0-1) so we need to rescale them to actual prices\n predicted_prices = scaler.inverse_transform(predicted_prices)\n\n plot_actual_vs_predicted(predicted_prices,actual_prices, company,\n x_labels=[date.strftime(\"%d-%m-%y\") for date in testing_data.index] )\n\n # predict next day\n real_data = [model_inputs[len(model_inputs) + 1 - training_window: len(model_inputs)+1, 0]]\n real_data = np.array(real_data)\n real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))\n print(scaler.inverse_transform(real_data[-1]))\n\n prediction = model.predict(real_data)\n prediction = scaler.inverse_transform(prediction)\n print(f\"Next day value is: {prediction}\")\n\n print(\"===================================\")\n print(model.summary())\n print(\"===================================\")\n print(\"Evaluating model\")\n result = model.evaluate(x_test, y_test)\n print(dict(zip(model.metrics_names, result)))\n\n\n # TODO\n # experiment with next days predictions","def _forecast_train(stan_fit, p):\n \n train_n = stan_fit.data['N']\n t = np.arange(train_n) / train_n\n train_changepoints = stan_fit.data['s']\n \n A = (t[:, None] > train_changepoints) * 1. # t x s\n \n X = fourier_series(t, p=p, fourier_order=stan_fit.data['n_fourier']) \n \n samples = stan_fit.extract(permuted=True)\n \n samples_n = len(samples['lp__'])\n \n y_hat = np.zeros(shape=(samples_n, len(t)))\n mu_trend = np.zeros(shape=(samples_n, len(t)))\n mu_s = np.zeros(shape=(samples_n, len(t)))\n \n for iteration in range(samples_n):\n \n k = samples['k'][iteration]\n m = samples['m'][iteration]\n deltas = samples['delta'][iteration]\n beta = samples['beta'][iteration]\n sigma = samples['sigma'][iteration]\n \n gamma = -train_changepoints * deltas;\n mu_trend[iteration,:] = k + np.matmul(A, deltas) * t + (m + np.matmul(A, gamma))\n mu_s[iteration,:] = np.matmul(X, beta)\n \n mu = mu_trend[iteration] + mu_s[iteration]\n \n y_hat[iteration,:] = np.random.normal(mu, sigma)\n \n return y_hat, mu_trend, mu_s, samples, samples_n, train_n, train_changepoints","def predic_subscription():\r\n \r\n df_test=pd.read_csv(request.files.get(\"file\"),sep=';')\r\n def data_cleaning(bank_loan):\r\n categorical_value=[feature for feature in bank_loan if bank_loan[feature].dtype =='O']\r\n for feature in categorical_value:\r\n temp=bank_loan[feature].unique()\r\n temp_df={i:j for j,i in enumerate(temp,0)}\r\n bank_loan[feature]=bank_loan[feature].map(temp_df)\r\n return bank_loan\r\n df_test=data_cleaning(df_test)\r\n \r\n scale=StandardScaler()\r\n scaled_data=scale.fit_transform(df_test)\r\n prediction=subscribe_pre.predict(scaled_data)\r\n return str(list(prediction))","def prophet_train(data): \n \n model = Prophet(interval_width=.95, changepoint_prior_scale=6, yearly_seasonality=True, \n seasonality_prior_scale=1, weekly_seasonality=False, daily_seasonality=False)\n model.add_seasonality(name='monthly', period=120, fourier_order=4)\n \n model.fit(data)\n \n future_dates = model.make_future_dataframe(periods=12, freq='MS')\n forecast = model.predict(future_dates)\n forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n results = forecast[forecast['ds']>='2018-01-01']\n return(results)","def train(self, training_data):\n # load and preprocess\n super(Forecast, self).train(training_data)\n # remove NaNs\n self.historical_data = self.historical_data.loc[~self.historical_data.isnull().any(axis=1)]\n # project timestamps into vector space\n if self.timestamp_column in self.historical_data.columns:\n self.use_timestamp = True\n ts = self.historical_data.set_index(self.timestamp_column).index\n self.epoch = min(ts)\n self.epoch_span = float((max(ts) - self.epoch).total_seconds())\n time_features = make_time_features(ts,\n index=self.historical_data.index,\n epoch=self.epoch,\n epoch_span=self.epoch_span)\n self.historical_data = pd.concat([self.historical_data, time_features], axis=1)\n self.historical_data.drop(self.timestamp_column, axis=1, inplace=True)\n # leave all other variables independent\n self.independent_variables = [name for name in self.historical_data.columns\n if name not in self.dependent_variables]\n\n self.model.fit(self.historical_data[self.independent_variables],\n self.historical_data[self.dependent_variables])\n\n # release historical data to save on memory\n # note that python garbage collection is not instantaneous\n LOG.warn(\"Releasing building load forecast training data. The agent will not be able to retrain on this data\")\n self.historical_data = None","def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\n df = self.get_prep_data()\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\n print(f'----train----')\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----validation----')\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----test----')\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n self.set_train(train)\n self.set_validation(val)\n self.set_test(test)\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n train_y = train['target']\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n val_y = val['target']\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n test_y = test['target']\n self.set_train_X(train_X)\n self.set_train_y(train_y)\n self.set_val_X(val_X)\n self.set_val_y(val_y)\n self.set_test_X(test_X)\n self.set_test_y(test_y)","def _stratify_training_data(\n self,\n train_ds: xr.Dataset,\n target_variable: str,\n pred_months: int,\n expected_length: Optional[int],\n ) -> None:\n\n min_date = self._get_datetime(train_ds.time.values.min())\n max_date = self._get_datetime(train_ds.time.values.max())\n\n cur_pred_year, cur_pred_month = max_date.year, max_date.month\n\n # for every month-year create & save the x, y datasets for training\n cur_min_date = max_date\n while cur_min_date >= min_date:\n # each iteration count down one month (02 -> 01 -> 12 ...)\n arrays, cur_min_date = self._stratify_xy(\n ds=train_ds,\n year=cur_pred_year,\n target_variable=target_variable,\n target_month=cur_pred_month,\n pred_months=pred_months,\n expected_length=expected_length,\n )\n if arrays is not None:\n self._save(\n arrays,\n year=cur_pred_year,\n month=cur_pred_month,\n dataset_type=\"train\",\n )\n cur_pred_year, cur_pred_month = cur_min_date.year, cur_min_date.month","def train_test_dataset(self, train_rate=0.8):\n point_date = int(len(self.y) * train_rate)\n y_to_train = self.y[:point_date]\n y_to_val = self.y[point_date:]\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\n date_val = self.y.index[point_date]\n return y_to_train, y_to_val, predict_date, date_val","def set_training_data(self):\n # Optional training data period\n # TODO: add training data period feature to training data query\n if not self.training_period == None:\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\"%Y-%m-%d\")\n print(f\"Training data start date: {training_period_date}\")\n # Extract queried data from Athena\n #athena = athena_connect.Athena()\n #features_df = athena.pandas_read_athena(self.training_data_sql)\n with open('feature_sql.txt', 'w') as f:\n print(self.training_data_sql, file=f) \n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\n features_df.fillna(0, inplace=True)\n print(features_df.shape)\n features_df = features_df[max(self.feature_minutes_list):]\n print(features_df.shape)\n # Remove infinity string\n features_df.replace({'Infinity': 0}, inplace=True)\n # Convert all object fields to numeric except date fields\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\n self.training_df = features_df","def application_train_test(num_rows=None, nan_as_category=False):\n df = pd.read_csv('application_train.csv', nrows=num_rows)\n test_df = pd.read_csv('application_test.csv', nrows=num_rows)\n print(\"Train samples: {}, test samples: {}\".format(len(df), len(test_df)))\n print(\"Train df shape: {}, test df shape: {}\".format(df.shape,\n test_df.shape))\n df = df.append(test_df).reset_index()\n print(\"df shape: {} - After reset index\".format(df.shape))\n # Optional: Remove 4 applications with XNA CODE_GENDER (train set)\n df = df[df['CODE_GENDER'] != 'XNA']\n\n # Categorical features with Binary encode (0 or 1; two categories)\n for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\n df[bin_feature], uniques = pd.factorize(df[bin_feature])\n # print(uniques)\n # Categorical features with One-Hot encode\n print(\"df shape: {} - After binary encode 3 columns\".format(df.shape))\n\n df, cat_cols = one_hot_encoder(df, nan_as_category)\n # print(cat_cols)\n print(\"df shape: {} - After one-hot encoding other columns\".format(df.shape))\n\n # NaN values for DAYS_EMPLOYED: 365.243 -> nan\n df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace=True)\n # 5 new features (percentages)\n df['DAYS_EMPLOYED_PERC'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']\n df['INCOME_CREDIT_PERC'] = df['AMT_INCOME_TOTAL'] / df['AMT_CREDIT']\n df['INCOME_PER_PERSON'] = df['AMT_INCOME_TOTAL'] / df['CNT_FAM_MEMBERS']\n df['ANNUITY_INCOME_PERC'] = df['AMT_ANNUITY'] / df['AMT_INCOME_TOTAL']\n df['PAYMENT_RATE'] = df['AMT_ANNUITY'] / df['AMT_CREDIT']\n print(\"df shape: {} - After adding 5 manual features\".format(df.shape))\n del test_df\n gc.collect()\n return df","def foo(username):\n year = 2018\n cal = Calendar(-1)\n training_list = []\n path = users_folder_file_path + username\n\n weekdays = ['Sunday', 'Monday', 'Tuesday',\n 'Wednesday', 'Thursday', 'Friday', 'Saturday']\n mydate = date.today()\n list_month = []\n for i in range(12):\n list_month.append(mydate.strftime(\"%B\"))\n mydate = mydate + relativedelta(months=+1)\n\n logged_training = []\n\n with open(path + '/logged_training.csv') as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n next(readCSV, None)\n year_t = 0\n month_t = 0\n day_t = 0\n count = 0\n for row in readCSV:\n if row[0] != '' and row[1] != '':\n if year_t == int(row[0].split('-')[0]) and \\\n month_t == int(row[0].split('-')[1]) and \\\n day_t == int(row[0].split('-')[2]):\n\n logged_training[count-1][0] = \\\n logged_training[count-1][0] + float(row[1])\n\n else:\n logged_training.append(\n [float(row[1]), int(row[0].split('-')[0]),\n int(row[0].split('-')[1]), int(row[0].split('-')[2])])\n\n year_t = int(row[0].split('-')[0])\n month_t = int(row[0].split('-')[1])\n day_t = int(row[0].split('-')[2])\n count += 1\n\n if len(logged_training) > 0:\n dates = [training[1:] for training in logged_training]\n dates = [datetime.strptime('-'.join(str(x) for x in date1),\n '%Y-%m-%d') for date1 in dates]\n last_day = max(dates)\n else:\n last_day = datetime.now().replace(hour=0,\n minute=0,\n second=0,\n microsecond=0)\n\n with open(path + '/planned_training.csv') as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n next(readCSV, None)\n for row in readCSV:\n if datetime.strptime(row[2], '%Y-%m-%d') > last_day:\n training_list.append([row[0],\n int(row[2].split('-')[0]),\n int(row[2].split('-')[1]),\n int(row[2].split('-')[2])])\n try:\n if not year:\n year = date.today().year\n cal_list = [\n cal.monthdatescalendar(year, i+1)\n for i in range(12)\n if i >= date.today().month - 1]\n cal_list2 = [\n cal.monthdatescalendar(year, i + 1)\n for i in range(12)\n if i < date.today().month - 1]\n\n cal_list3 = cal_list + cal_list2\n\n except IOError:\n abort(404)\n else:\n race_miles = training_list[-1][0]\n if username == 'alex' or username == 'Diane':\n generate_mileage_line(username)\n\n return render_template('new_alex.html',\n weekdays=weekdays,\n list_month=list_month,\n username=username,\n year=year,\n cal=cal_list3,\n training_list=training_list[:-1],\n logged_training=logged_training,\n race_date=training_list[-1],\n race_miles=race_miles)\n else:\n generate_mileage_line(username)\n return render_template('new_.html',\n weekdays=weekdays,\n list_month=list_month,\n username=username,\n year=year,\n cal=cal_list3,\n training_list=training_list[:-1],\n logged_training=logged_training,\n race_date=training_list[-1],\n race_miles=race_miles)\n abort(404)","def naive_forecasting(x_test, y_test):\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred","def forecast(dataloader):\n model = NBeats.load_from_checkpoint(os.path.join(DIR, 'model/nbeats.ckpt'))\n raw_predictions = model.predict(dataloader, mode=\"raw\", return_x=False)\n y = np.array(raw_predictions['prediction'].reshape(-1, 1))\n df = pd.DataFrame(data=y, columns=['prediction'])\n df.to_csv(os.path.join(DIR, 'data/nbeats_forecast.csv'), index=False)\n return y","def load_data(\n ticker:str,T_test:int,file_name_monthly:str,\n file_name_quarterly:str,y_col='Lucro/Prejuízo do Período'\n )-> Tuple[np.array,np.array,np.array,np.array]:\n\n df_quarterly = read_ticker(ticker)\n\n if file_name_monthly is None and file_name_quarterly is None:\n # if no monthly data, set df_monthly as empty df\n df_monthly = pd.DataFrame() \n df_all = df_quarterly\n elif file_name_quarterly is None:\n df_monthly = read_ipea(file_name_monthly,time_freq='monthly')\n # TODO remove this\n df_monthly = df_monthly.iloc[:,:120]\n df_all = pd.concat([df_monthly,df_quarterly],join='inner',axis=1)\n else:\n df_monthly = read_ipea(file_name_monthly,time_freq='monthly')\n # TODO remove this\n df_monthly = df_monthly.iloc[:,:120]\n if file_name_quarterly != None:\n ipea_quarterly = read_ipea(file_name_quarterly,time_freq='quarterly')\n # TODO remove this\n ipea_quarterly = ipea_quarterly.iloc[:,:10]\n df_quarterly = pd.concat([ipea_quarterly,df_quarterly],join='inner',axis=1)\n df_all = pd.concat([df_monthly,df_quarterly],join='inner',axis=1)\n \n\n y_all = df_all.loc[:,y_col].copy()\n X_all = df_all.drop(columns=y_col)\n\n X_train = X_all.iloc[:-T_test,:]\n X_test = X_all.iloc[-T_test:,:]\n\n y_train = y_all[:-T_test]\n y_test = y_all[-T_test:]\n\n number_variables = {\n \"quarterly\" : df_quarterly.shape[1] - 1, #removing the target column\n \"monthly\" : df_monthly.shape[1]//3,\n \"daily\" : 0\n }\n\n return X_train.values, X_test.values, y_train.values, y_test.values, number_variables","def Data_formatting(dz, y, timestep, TZ):\r\n \r\n #reindex data by datetime\r\n dz.index = pd.to_datetime(dz['DATE'])\r\n \r\n #Isolate temperature data\r\n dz = dz[['TMP']]\r\n \r\n #Delete data mistake\r\n dz = dz[dz['TMP'] != \"+9999,9\"]\r\n \r\n #Format data\r\n dz['TMP'] = dz['TMP'].str.replace(',', '.')\r\n dz['TMP'] = pd.to_numeric(dz['TMP'], errors='coerce')\r\n \r\n #Delete NaN data\r\n dz = dz.dropna()\r\n \r\n #Convert temperature\r\n dz['TMP'] = dz['TMP'] / 10\r\n dz['TMP'] = dz['TMP'] * (9/5) + 32\r\n \r\n #Convert datetime index utc to specified timezone\r\n dz.index = dz.index.tz_localize(pytz.utc).tz_convert(pytz.timezone(str(TZ))).strftime(\"%Y-%m-%d %H:%M:%S\")\r\n dz.index = pd.to_datetime(dz.index)\r\n \r\n #Resample data by average on timestep\r\n dz = dz.resample(rule = str(timestep)).mean()\r\n \r\n #Define the first date of the instance year\r\n fdy = dt.datetime.strptime(\"01/01/\"+str(y)+\" 00:00\", '%m/%d/%Y %H:%M')\r\n #Convert first date of the year to timezone\r\n fdy = Date_calibration(fdy, 0, TZ)\r\n \r\n #If we collect the date from the current year we limit the collect from 2days before now\r\n \r\n #Define the datetime 2 days before now\r\n dbeyest = dt.datetime.now(tz=pytz.timezone(str(TZ))) - dt.timedelta(days=2)\r\n \r\n #If the instance year is the current year\r\n if(y == dbeyest.year):\r\n #We limit the collect 2 days before now\r\n ldy = dt.datetime.strptime(str(dbeyest.month)+\"/\"+str(dbeyest.day)+\"/\"+str(y)+\" 23:59\", '%m/%d/%Y %H:%M')\r\n else:\r\n #Else, we collect the full year\r\n ldy = dt.datetime.strptime(\"12/31/\"+str(y)+\" 23:59\", '%m/%d/%Y %H:%M')\r\n \r\n #Convert the last date of the year to specified timezone\r\n ldy = Date_calibration(ldy, 0, TZ)\r\n \r\n #Set up dataframe for the specified datetime index and timestep\r\n ph = pd.DataFrame(index=pd.DatetimeIndex(start=fdy, end=ldy, freq=str(timestep)))\r\n \r\n #Past original data temperature in the time fitted dataframe (with the datetimeindex position)\r\n ph['TMP'] = dz['TMP']\r\n \r\n #Calculate the quality of the instance data\r\n nb_nan = ph['TMP'].isnull().sum()\r\n qual = (1 - (nb_nan) / len(ph)) * 100\r\n \r\n return dz, qual","def forecast(\n input_values: np.ndarray, future_dates: List, model: Sequential, scaler\n) -> pd.DataFrame:\n if scaler:\n future_values = scaler.inverse_transform(\n model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\n )\n else:\n future_values = model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\n\n df_future = pd.DataFrame(\n future_values, index=future_dates, columns=[\"Predicted Price\"]\n )\n return df_future","def _transform_training_set(self, gdax_rates):\n rates = [gdax_rate.closing_price for gdax_rate in gdax_rates]\n timestamps = [gdax_rate.end_time.timestamp() for gdax_rate in gdax_rates]\n\n x_train = np.reshape(timestamps, (len(timestamps), 1))\n y_train = rates\n\n return x_train, y_train","def read_dataset():\n\n\tonehotencoder = OneHotEncoder(categories=\"auto\", sparse=False)\n\tscaler = StandardScaler(with_mean=False)\n\tcwd = os.getcwd()\n\tfilename = cwd + '/default of credit card clients.xls'\n\tnanDict = {}\n\t#read file and create the dataframe\n\tdf = pd.read_excel(filename, header=1, skiprows=0,\n\t\t\t\t\t\tindex_col=0, na_values=nanDict)\n\n\tdf.rename(index=str,\n\t\t\t\tcolumns={\"default payment next month\": \"defaultPaymentNextMonth\"},\n\t\t\t\tinplace=True)\n\n\tdf = df.drop(df[(df.BILL_AMT1 == 0)&\n\t\t\t\t\t\t(df.BILL_AMT2 == 0)&\n\t\t\t\t\t\t(df.BILL_AMT3 == 0)&\n\t\t\t\t\t\t(df.BILL_AMT4 == 0)&\n\t\t\t\t\t\t(df.BILL_AMT5 == 0)&\n\t\t\t\t\t\t(df.BILL_AMT6 == 0)].index)\n\tdf = df.drop(df[(df.PAY_AMT1 == 0)&\n\t\t\t\t\t\t(df.PAY_AMT2 == 0)&\n\t\t\t\t\t\t(df.PAY_AMT3 == 0)&\n\t\t\t\t\t\t(df.PAY_AMT4 == 0)&\n\t\t\t\t\t\t(df.PAY_AMT5 == 0)&\n\t\t\t\t\t\t(df.PAY_AMT6 == 0)].index)\n\n\t# Creating matrix X and target variable y\n\tX = df.loc[:, df.columns != 'defaultPaymentNextMonth'].values\n\ty = df.loc[:, df.columns == 'defaultPaymentNextMonth'].values\n\n\tonehotencoder = OneHotEncoder(categories ='auto', sparse =False)\n\tscalar = StandardScaler(with_mean = False)\n\n\t#make one hots\n\tX = ColumnTransformer(\n\t\t\t\t[(\"\", onehotencoder, [1,2,3,5,6,7,8,9]),],\n\t\t\t\tremainder=\"passthrough\"\n\t\t\t).fit_transform(X)\n\n\tX = scalar.fit_transform(X)\n\n\treturn X, y","def load_data():\n df = pd.read_csv(\"../../Data/breast_cancer_data/data.csv\")\n\n cols = df.columns\n X = df[cols[2:-1]].to_numpy()\n y = df[cols[1]].to_numpy()\n y = (y=='M').astype(np.int) * 2 - 1\n\n train_X = X[:-150]\n train_y = y[:-150]\n\n test_X = X[-150:]\n test_y = y[-150:]\n\n return train_X, train_y, test_X, test_y","def predictions_0(data):\n predictions=[]\n for _, passenger in data.iterrows():\n #Predict the survival of 'passenger'\n predictions.append(0)\n #Return our predictions\n return pd.Series(predictions)\n #make the predictions\n predictions=predictions_0(data)\n \n '''\n Question 1\n Using the RMS Titanic data,how accurate would a prediction be that none of the passengers survived?\n Hint:Run the code cell below to see the accuracy of this prediction.\n '''\n print accuracy_score(outcomes,predictions)","def test_02_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\n self.assertEqual(metric,logged_metric)","def get_trend_pred(united_samples, look_back):\n\n features = united_samples[:, :1].astype(str)\n labels = united_samples[:, -1:]\n\n # move all dates a day behind\n delta = -1\n generator = (change_date(date[0], delta_days=delta) for date in features)\n new_dates = np.fromiter(generator, features.dtype)\n\n # selecting samples after April 2020 when the COVID-19 became global\n BOOL_COND_ARRAY = [(int(date[0:4]) >= 2020 and int(date[5:7]) >= 4)\n for date in new_dates]\n new_dates = new_dates[BOOL_COND_ARRAY]\n new_dates = new_dates.reshape(-1, 1)\n\n labels = labels[BOOL_COND_ARRAY]\n\n # converting date to numerical source:\n generator = (dt.toordinal(dt.strptime(date[0], DATE_FORMAT)) for date in\n new_dates)\n numerical_dates = np.fromiter(generator, features.dtype)\n numerical_dates = numerical_dates.reshape(-1, 1).astype(float)\n\n # change degree of polynomial features\n poly_features = PolynomialFeatures(degree=4)\n features_transformed = poly_features.fit_transform(numerical_dates)\n\n # model\n linreg_model = LinearRegression()\n linreg_model.fit(features_transformed, labels)\n\n # trend\n trend_labels = linreg_model.predict(\n poly_features.fit_transform(numerical_dates))\n\n ahead = trend_labels[-1:, 0]\n behind = trend_labels[-look_back-1: -look_back, 0]\n\n if ahead > behind:\n return 'upward'\n elif ahead < behind:\n return 'downward'\n else:\n return 'not_changed'","def __init__(self, ts_df, time_format=\"%Y-%m-%d %H:%M:%S\", freq='D',\n fill_method='ffill',\n n_test=0, n_val=0,\n hyper_params=None,\n test='adf',\n trend=None,\n seasonal=False,\n seasonal_periods=1,\n **kwds):\n self._ts_df_cols = ['ds', 'y']\n\n self.ts_df = ts_df\n self.time_format = time_format\n self.freq = freq\n self.fill_method = fill_method.lower()\n self.n_test = int(n_test)\n self.n_val = int(n_val)\n self.transform = None\n self._boxcox_lmbda = None\n\n self._mode = ''\n\n self._train_dt = None\n self._test_dt = None\n self._val_dt = None\n\n self.model_fit = None\n self.fittedvalues = None\n self.residuals = None\n self.rmse = 0\n self._gs = tsa.GridSearchClass()\n self.hyper_params = hyper_params\n self.best_model = dict()\n\n \"\"\"\n self.rmse_test = 0\n self.rmse_val = 0\n \"\"\"\n\n self.upper_whisker_res = None\n self.lower_conf_int = None\n self.upper_conf_int = None\n\n self.forecast = None\n self.residuals_forecast = None\n\n self._res_decomp = None\n self._arr_seasonal = None\n self._arr_trend = None\n self._arr_baseline = None\n\n self._test = test\n self._trend = trend\n if self._trend is not None:\n self._trend = self._trend.lower()\n self._seasonal = seasonal\n if isinstance(self._seasonal, str):\n self._seasonal = self._seasonal.lower()\n self._seasonal_periods = seasonal_periods\n\n self._uvts_cls_logger = Logger('uvts_cls')\n\n UVariateTimeSeriesClass.assertions(self)\n # work with ts_df\n self.ts_df = self.ts_df.reset_index()\n self.ts_df.columns = self._ts_df_cols\n self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')\n self.ts_df.set_index('ds', inplace=True)\n self._uvts_cls_logger.info(\n \"Received time series data of range: \" + str(min(self.ts_df.index)) + ' - ' + str(\n max(self.ts_df.index)) + \" and shape: \" + str(self.ts_df.shape))\n\n if not isinstance(self.ts_df.index, pd.DatetimeIndex):\n self._uvts_cls_logger.warning(\"Time conversion required...\")\n self.ts_df = self.ts_df.reset_index()\n try:\n self.ts_df['ds'] = self.ts_df['ds'].apply(\n lambda x: datetime.datetime.strptime(\n str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],\n self.time_format))\n except ValueError as e:\n self._uvts_cls_logger.warning(\"Zulu time conversion not successful: {}\".format(e))\n self._uvts_cls_logger.warning(\"Will try without assuming zulu time...\")\n try:\n self.ts_df['ds'] = self.ts_df['ds'].apply(\n lambda x: datetime.datetime.strptime(str(x), self.time_format))\n except ValueError as e:\n self._uvts_cls_logger.info(\"Time conversion not successful. Check your time_format: {}\".format(e))\n sys.exit(\"STOP\")\n else:\n self._uvts_cls_logger.info(\"Time conversion successful!\")\n else:\n self._uvts_cls_logger.info(\"Time conversion successful!\")\n # set index\n self.ts_df.set_index('ds', inplace=True)\n #\n self.ts_df.index = pd.to_datetime(self.ts_df.index)\n self.ts_df.sort_index(inplace=True)\n # resample\n self.ts_resample()\n UVariateTimeSeriesClass.assertions(self, post=True)\n #\n if self.n_val > len(self.ts_df) - self.n_test:\n self.n_val = len(self.ts_df) - self.n_test\n\n if self.n_test == 0 and self.n_val == 0:\n self._mode = 'forecast'\n elif self.n_test > 0:\n self._mode = 'test'\n elif self.n_test == 0 and self.n_val > 0:\n self._mode = 'validate'\n \n # delegate just for good programming style here\n super(UVariateTimeSeriesClass, self).__init__(**kwds)","def testprepare_dataset(self, stock):\n dataset = pd.DataFrame(yf.download(stock))\n dataset['Difference'] = dataset['Close'].diff() # Finds the difference in price each day\n dataset = dataset[1:] # Drops the first null value\n dataset_train = dataset[0:len(dataset)-30] # Training dataset will be the whole dataset minus the test dataset\n training_set = dataset_train.iloc[:, 6:7].values # Use the open values only\n\n # Feature Scaling\n sc = MinMaxScaler(feature_range=(0, 1)) # Scales all values so that they are in the range (0, 1)\n training_set_scaled = sc.fit_transform(training_set)\n\n # Create an input data structure with a timestep\n x_train = []\n y_train = []\n for i in range(self.timestep, len(dataset)-30):\n x_train.append(training_set_scaled[i - self.timestep:i, 0])\n y_train.append(training_set_scaled[i, 0])\n x_train, y_train = np.array(x_train), np.array(y_train)\n\n # Reshaping\n x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n\n return x_train, y_train","def merge_data():\n\n\tconfig = Config()\n\tfilename_train, filename_test = \"../data/train.csv\", \"../data/test.csv\" \n\n # create datasets\n\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\n\n # 1. datetime features\n\t# diff between weekday and day?\n\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\n\t#day - Between 1 and the number of days in the given month of the given year.\n\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\n\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\n\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\n\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\n\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\n\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\n\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\n\n\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\n\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\n\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\n\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\n\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\n\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\n\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\n\n\t# 2. Location features\n\tdef haversine(lon1, lat1, lon2, lat2):\n\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\t dlon = lon2 - lon1\n\t dlat = lat2 - lat1\n\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n\t c = 2 * np.arcsin(np.sqrt(a))\n\t km = 6367 * c # AVG_EARTH_RADIUS=6367\n\t miles = km * 0.621371\n\t return miles\n\n\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\n\t# a = haversine_array(lat1, lng1, lat1, lng2)\n\t# b = haversine_array(lat1, lng1, lat2, lng1)\n\t# return a + b\n\n\t# def bearing_array(lat1, lng1, lat2, lng2):\n\t# AVG_EARTH_RADIUS = 6371 # in km\n\t# lng_delta_rad = np.radians(lng2 - lng1)\n\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\n\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n\t# return np.degrees(np.arctan2(y, x))\n\n\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\n\t train.dropoff_longitude, train.dropoff_latitude)\n\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\n\t test.dropoff_longitude, test.dropoff_latitude)\n\n\n\t# 3. Use outsource data\n\tweatherdata_filename = \"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\"\n\tfastestroute_data_train = \"../data/outsource_data/fastest_train.csv\"\n\tfastestroute_data_test = \"../data/outsource_data/fastest_routes_test.csv\"\n\n\n\twd = pd.read_csv(weatherdata_filename, header=0)\n\twd['date'] = pd.to_datetime(wd.date, format=\"%d-%m-%Y\")\n\twd['pickup_day'] = wd['date'].dt.day\n\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \n\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \n\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \n\n\t# Merge training data with weather data on pickup_day\n\tprint(\"Merging training data with weather data ....\")\n\twd_train = pd.merge(train, wd, on='pickup_day')\n\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\n\tgc.collect()\n\n\t# Merge wd_train with fastestroute_data\n\tfastest = pd.read_csv(fastestroute_data_train, header=0)\n\tprint(\"Merging Location data with weather and training data ....\")\n\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\n\n\tgc.collect()\n\n\n\tprint(\"===================== CHECK TRAINING DATA =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Semi-final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\t# Use the same outsource data with test set\n\t# merge outsource data with test data as well\n\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\t'total_travel_time',\n\t\t\t\t\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\n\t 'step_maneuvers','step_direction',\t'step_location_list']\n\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\n\n\tprint(\"Merging test data with Location data ....\")\n\ttest = pd.merge(test, fastest_test, on='id', how='outer')\n\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\n\t 'end_street'], axis=1)\n\tprint(\"Merging test data with weather data ....\")\n\ttest = pd.merge(test, wd, on='pickup_day')\n\n\tprint(\"===================== CHECK TEST DATA =====================\")\n\n\tprint(test.head(2))\n\tprint(\"Semi-final test data shape is: {}\".format(test.shape))\n\tprint(\"Test data columns: {}\".format(test.columns))\n\n\n\n\t# 4. Do more data munging\n\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = (wd_train_fastest.trip_duration < 60) \n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.trip_duration > 79200\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\n\twd_train_fastest = wd_train_fastest[~mask]\n\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\n\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\n\n\t# 5. Do some data maskig based on location to create jfk and lgo features\n\tjfk_lon = -73.778889\n\tjfk_lat = 40.639722\n\tlga_lon = -73.872611\n\tlga_lat = 40.77725\n\n\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\n\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\n\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\n\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\n\n\n\tprint(\"===================== CHECK TRAINING DATA AGAIN =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\treturn wd_train_fastest, test","def training_data(loan_data):\n return loan_data[loan_data.status != \"LIVE\"]","def load_cup_data(train=True):\n type = \"TR\" if train else \"TS\"\n csv_file = path_data / Path(f\"ML_CUP/ML-CUP20-{type}.csv\")\n return pd.read_csv(csv_file, skiprows=7, header=None, index_col=0)","def read300yrf(period):\n directory300 = '/seley/ypeings/simu/PAMIP-1.6-QBO-300yr/monthly/'\n file300 = 'U10_1700-2000.nc'\n filename = directory300 + file300\n \n data = Dataset(filename)\n lat = data.variables['latitude'][:]\n lon = data.variables['longitude'][:]\n u10q = data.variables['U10'][:]\n data.close()\n \n ### Reshape in year/month\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\n \n ### Calculate over particular months\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\n \n ### Slice U10 at 65N\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\n lat = lat[latq].squeeze()\n u10 = u10[:,latq,:].squeeze()\n \n ### Take zonal mean \n u10z = np.nanmean(u10,axis=1)\n \n ### Remove missing data\n mask = np.where(u10z > -1e5)[0]\n \n ### Detrend\n u10zdt = sss.detrend(u10z[mask],type='linear')\n \n return lat,lon,u10zdt","def clean_train_test(train):\n\n train[\"Month\"] = train.Date.apply(lambda x: x.month)\n train[\"Year\"] = train.Date.apply(lambda x: x.year)\n train[\"Day\"] = train.Date.apply(lambda x: x.day)\n\n # Doesn't actually seem to help\n #train[\"Latitude_int\"] = train.Latitude.apply(int)\n #train[\"Longitude_int\"] = train.Longitude.apply(int)\n\n c2d = [\"Id\", \"Address\", \"AddressNumberAndStreet\", \"WnvPresent\",\n \"NumMosquitos\"]\n\n for column in c2d:\n if column in train.columns:\n train.drop(column, axis=1, inplace=True)\n\n return train","def train_test_data_df(train_data_file, test_data_file):\n dtype_dict = {\n \"age\": np.int32,\n \"education-num\": np.int32,\n \"capital-gain\": np.int32,\n \"capital-loss\": np.int32,\n \"hours-per-week\": np.int32\n }\n cols = [i for i in range(15) if i != 2]\n train_data = pd.read_csv(train_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n train_data = train_data.dropna(axis=0, how=\"any\")\n test_data = pd.read_csv(test_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n test_data = test_data.dropna(axis=0, how=\"any\")\n return train_data, test_data","def trainData(self, X, y, NeuralNet, epochs):","def data():\n\n run_type = 'standardised'\n sr = 48000\n train_perc = 0.9\n\n if sr == 48000:\n time_dimension = 282\n if sr == 44100:\n time_dimension = 259\n\n x_train, y_train, x_test, y_test = essential.compile_dataset(run_type, sr)\n\n # reshape for CNN input\n x_train = np.array([x.reshape((128, time_dimension, 1)) for x in x_train])\n x_test = np.array([x.reshape((128, time_dimension, 1)) for x in x_test])\n\n # encoded \n encoder = LabelEncoder()\n encoder.fit(y_train)\n encoder.fit(y_test)\n y_train = encoder.transform(y_train)\n y_test = encoder.transform(y_test)\n\n return x_train, y_train, x_test, y_test","def load_data(train_file, test_file):\n\n data = np.asarray(pd.read_csv(train_file, header=0))\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\n\n x_tra = data[:, :-1]\n y_tra = data[:, -1]\n\n return x_tra, y_tra, data_ts","def cafe(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'cafe.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/Stat2Data/CAFE.csv'\n maybe_download_and_extract(path, url,\n save_file_name='cafe.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","def read_full_predictions(self, model_name):\r\n dates = []\r\n true_0mo = []\r\n true_6mo = []\r\n pred_6mo = []\r\n true_12mo = []\r\n pred_12mo = []\r\n true_24mo = []\r\n pred_24mo = []\r\n for test in self.full_predictions:\r\n test_data = self.full_predictions[test]\r\n dates.extend(test_data[self.output_names[0]][model_name]['Dates'])\r\n true_0mo.extend(test_data[self.output_names[0]][model_name]['True'])\r\n true_6mo.extend(test_data[self.output_names[1]][model_name]['True'])\r\n pred_6mo.extend(test_data[self.output_names[1]][model_name]['Predicted'])\r\n true_12mo.extend(test_data[self.output_names[2]][model_name]['True'])\r\n pred_12mo.extend(test_data[self.output_names[2]][model_name]['Predicted'])\r\n true_24mo.extend(test_data[self.output_names[3]][model_name]['True'])\r\n pred_24mo.extend(test_data[self.output_names[3]][model_name]['Predicted'])\r\n \r\n results = pd.DataFrame() \r\n results['Dates'] = dates\r\n results['True_{}'.format(self.output_names[0])] = true_0mo\r\n results['True_{}'.format(self.output_names[1])] = true_6mo\r\n results['Pred_{}'.format(self.output_names[1])] = pred_6mo\r\n results['True_{}'.format(self.output_names[2])] = true_12mo\r\n results['Pred_{}'.format(self.output_names[2])] = pred_12mo\r\n results['True_{}'.format(self.output_names[3])] = true_24mo\r\n results['Pred_{}'.format(self.output_names[3])] = pred_24mo\r\n \r\n return(results)","def train(self):\n df = self.df\n self.scaler = MinMaxScaler()\n self.scaler.fit(df)\n df[df.columns] = self.scaler.transform(df)\n\n\n X_train, y_train = get_X_y(df, self.n_days, self.length , self.style)\n X_train = np.array(X_train)\n X_train.shape = (X_train.shape[0], X_train.shape[2])\n\n self.clf = LogisticRegression().fit(X_train, y_train)\n\n #es = EarlyStopping(monitor = 'accuracy',mode = 'min' , verbose = 1, patience = 100, restore_best_weights = True)","def test_adf(self):\n\n dftest = adfuller(self.ts_df['y'], autolag='AIC')\n dfoutput = pd.Series(dftest[0:4],\n index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\n for key, value in dftest[4].items():\n dfoutput['Critical Value (%s)' % key] = value\n print(dfoutput)\n if dftest[0] > dftest[4]['5%']:\n print(\n \"Test statistic greater than critical value at 5% --> series seems to be not stationary. \"\n \"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic.\")\n else:\n print(\n \"Test statistic less than critical value at 5% --> series seems to be stationary. \"\n \"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic.\")","def get_predictions(year, month):\n \n start_date = str(year)+\"-\"+str(month)+\"-01\"\n end_date = str(year)+\"-\"+str(month)+\"-\"+str(monthrange(year, month)[1])\n\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\"%Y-%m-%d\").tolist()\n\n # predictfunction \n # do predictions\n pred_arr = []\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\n \n try:\n predictions = load_predictions(file_name)\n predictions = predictions.round()\n except:\n print(\"An exception occurred\")\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\n \n \n for index,row in predictions.iterrows():\n \n pred_mail = 0\n pred_counter = 0\n pred_tel = 0\n \n # check predictions dataframe for 'Datum'\n if 'Datum' in predictions.columns:\n date = row['Datum']\n else:\n break;\n\n # check predictions dataframe for 'Mail'\n if 'Mail' in predictions.columns:\n pred_mail = row['Mail']\n\n # check predictions dataframe for 'Schalter'\n if 'Schalter' in predictions.columns:\n pred_counter = row['Schalter']\n\n # check predictions dataframe for 'Tel'\n if 'Tel' in predictions.columns:\n pred_tel = row['Tel']\n \n \n pred_dict = {'date': date, \n 'predictions':{'mail' : pred_mail, \n 'tel' : pred_tel, \n 'counter' : pred_counter\n }\n }\n\n pred_arr.append(pred_dict)\n\n print(pred_arr) \n \n return pred_arr","def data_preprocessing(dataset):\r\n df = pd.read_csv(dataset)\r\n df.head()\r\n df.describe()\r\n df.isnull().sum()\r\n df= df.drop(['instant'], axis=1)\r\n df['dteday'] = pd.to_datetime(df['dteday'].apply(str) + ' ' + df['hr'].apply(str) + ':00:00')\r\n return df","def test_01_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\n\n self.assertTrue(os.path.exists(log_file))","def test_jan1():\n utcnow = utc(2020, 1, 1, 0, 30)\n prod = parser(get_test_file(\"NESDIS/SCPCR1_jan1.txt\"), utcnow=utcnow)\n assert prod.data[0].valid == utc(2019, 12, 31, 23, 57)","def preprocess_data(data_path, last_day=None, schools_unis='two_separate', drop_features=None, min_confirmed=100,\n min_deaths=10, smoothing=1, mask_zero_deaths=False, mask_zero_cases=False):\n\n # load data from our csv\n df = pd.read_csv(data_path, parse_dates=[\"Date\"], infer_datetime_format=True).set_index(\n [\"Country Code\", \"Date\"])\n\n # handle custom last day of analysis\n if last_day is None:\n Ds = list(df.index.levels[1])\n else:\n Ds = list(df.index.levels[1])\n last_ts = pd.to_datetime(last_day, utc=True)\n Ds = Ds[:(1 + Ds.index(last_ts))]\n\n nDs = len(Ds)\n\n all_rs = list([r for r, _ in df.index])\n regions = list(df.index.levels[0])\n locations = [all_rs.index(r) for r in regions]\n sorted_regions = [r for l, r in sorted(zip(locations, regions))]\n nRs = len(sorted_regions)\n region_names = copy.deepcopy(sorted_regions)\n region_full_names = df.loc[region_names][\"Region Name\"]\n\n if drop_features is None:\n # note: these features are taken from the OxCGRT Dataset other than the mask wearing option\n drop_features = ['Mask Wearing', 'Travel Screen/Quarantine', 'Travel Bans', 'Public Transport Limited',\n 'Internal Movement Limited', 'Public Information Campaigns', 'Symptomatic Testing']\n\n for f in drop_features:\n print(f'Dropping NPI {f}')\n df = df.drop(f, axis=1)\n\n # pull data\n CMs = list(df.columns[4:])\n nCMs = len(CMs)\n\n ActiveCMs = np.zeros((nRs, nCMs, nDs))\n Confirmed = np.zeros((nRs, nDs))\n Deaths = np.zeros((nRs, nDs))\n Active = np.zeros((nRs, nDs))\n NewDeaths = np.zeros((nRs, nDs))\n NewCases = np.zeros((nRs, nDs))\n\n for r_i, r in enumerate(sorted_regions):\n region_names[r_i] = df.loc[(r, Ds[0])]['Region Name']\n for d_i, d in enumerate(Ds):\n Confirmed[r_i, d_i] = df.loc[(r, d)]['Confirmed']\n Deaths[r_i, d_i] = df.loc[(r, d)]['Deaths']\n Active[r_i, d_i] = df.loc[(r, d)]['Active']\n\n ActiveCMs[r_i, :, :] = df.loc[r].loc[Ds][CMs].values.T\n\n # compute new (daily) cases, after using thresholds\n Confirmed[Confirmed < min_confirmed] = np.nan\n Deaths[Deaths < min_deaths] = np.nan\n NewCases[:, 1:] = (Confirmed[:, 1:] - Confirmed[:, :-1])\n NewDeaths[:, 1:] = (Deaths[:, 1:] - Deaths[:, :-1])\n NewDeaths[NewDeaths < 0] = 0\n NewCases[NewCases < 0] = 0\n\n NewCases[np.isnan(NewCases)] = 0\n NewDeaths[np.isnan(NewDeaths)] = 0\n\n if smoothing != 1:\n print('Smoothing')\n\n # bulk smooth\n SmoothedNewCases = np.around(\n ss.convolve2d(NewCases, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary='symm',\n mode='same'))\n SmoothedNewDeaths = np.around(\n ss.convolve2d(NewDeaths, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary=\"symm\",\n mode='same'))\n\n # correct for specific regions\n for r in range(nRs):\n # if the country has too few deaths, ignore\n if Deaths[r, -1] < 50:\n print(f'Note: did not smooth deaths in {region_names[r]}')\n SmoothedNewDeaths[r, :] = NewDeaths[r, :]\n\n NewCases = SmoothedNewCases\n NewDeaths = SmoothedNewDeaths\n\n print('Masking invalid values')\n if mask_zero_deaths:\n NewDeaths[NewDeaths < 1] = np.nan\n else:\n NewDeaths[NewDeaths < 0] = np.nan\n\n if mask_zero_cases:\n NewCases[NewCases < 1] = np.nan\n else:\n NewCases[NewCases < 0] = np.nan\n\n Confirmed = np.ma.masked_invalid(Confirmed.astype(theano.config.floatX))\n Active = np.ma.masked_invalid(Active.astype(theano.config.floatX))\n Deaths = np.ma.masked_invalid(Deaths.astype(theano.config.floatX))\n NewDeaths = np.ma.masked_invalid(NewDeaths.astype(theano.config.floatX))\n NewCases = np.ma.masked_invalid(NewCases.astype(theano.config.floatX))\n\n # handle schools and universities\n if schools_unis == 'two_xor':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs_final[:, university_index, :] = np.logical_xor(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs = ActiveCMs_final\n CMs[school_index] = 'School and University Closure'\n CMs[university_index] = 'Schools xor University Closure'\n elif schools_unis == 'one_tiered':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :]) + 0.5 * np.logical_xor(\n ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\n CMs.remove('University Closure')\n elif schools_unis == 'two_separate':\n # don't need to do anything for this!\n pass\n elif schools_unis == 'one_and':\n school_index = CMs.index('School Closure')\n university_index = CMs.index('University Closure')\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\n ActiveCMs[:, school_index, :])\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\n CMs[school_index] = 'School and University Closure'\n CMs.remove('University Closure')\n\n return PreprocessedData(Active,\n Confirmed,\n ActiveCMs,\n CMs,\n sorted_regions,\n Ds,\n Deaths,\n NewDeaths,\n NewCases,\n region_full_names)","def get_data(temp_estimate_source='historic'):\n\n print 'get data from files'\n load = process_load_data(datafoldername+loadfilename_train)\n load_test = process_load_data(datafoldername+loadfilename_test)\n temp = process_temp_data(datafoldername+tempfilename_train)\n holidays = process_holiday_data(datafoldername+holidayfilename)\n\n print 'merge load with temp data'\n X_train_df = load.merge(temp, on='datetime', how='left')\n X_test_df = load_test.merge(temp, on='datetime', how='left')\n\n print 'estimate missing temps'\n # find rows with missing temperatures\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\n\n # source estimates for missing periods\n if temp_estimate_source == 'arima':\n # use preprocessed arima estimates\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\n elif temp_estimate_source == 'actuals':\n # use actual temperatures - as provided after conclusion of kaggle competition\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\n else:\n # use means of historical temps at same day/time.\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\n\n # merge estimates against missing rows, and use to update original dataframe in place\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\n replace_unknown_temps(X_test_df, replacementtemps)\n\n print 'merge in holiday dates'\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\n X_train_df['holiday'].fillna(0, inplace=True)\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\n X_test_df['holiday'].fillna(0, inplace=True)\n\n print 'add datetime categorical variables'\n X_train_df = add_datetime_categories(X_train_df)\n X_test_df = add_datetime_categories(X_test_df)\n\n return X_train_df, X_test_df","def main(input, output):\n # turn csv to dataframe\n df = pd.read_csv(f\"./data/{input}\", index_col=0)\n\n # split training and test\n X = df.drop(columns=['DEFAULT_NEXT_MONTH'])\n y = df['DEFAULT_NEXT_MONTH']\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.25, random_state=122)\n\n # We use robust scalar as most of our data is not normally distributed and we have a high amount of outliers.\n columns = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1', 'PAY_2',\n 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n scaler = preprocessing.RobustScaler()\n X_train = pd.DataFrame(scaler.fit_transform(X_train), columns=columns)\n X_test = pd.DataFrame(scaler.transform(X_test), columns=columns)\n\n # Use RFE to identify the most identify the most useful predictors.\n # Then we will drop those columns that are deemed as less useful.\n logreg = LogisticRegression(solver=\"lbfgs\")\n\n get_report(X_train, y_train, X_test, y_test, logreg, 'results_baseline')\n rfe = RFE(logreg, 7)\n rfe = rfe.fit(X_train, y_train.values.ravel())\n columns_to_drop = list()\n for i in range(len(rfe.support_)):\n include = rfe.support_[i]\n if include == False:\n columns_to_drop.append(columns[i])\n X_train = X_train.drop(columns_to_drop, axis=1)\n X_test = X_test.drop(columns_to_drop, axis=1)\n get_report(X_train, y_train, X_test, y_test, logreg, output)\n\n # write compounded dataframe with results of two get_report calls\n accuracies_output_df.to_csv(f'./{output}/accuracies.csv', index=False)","def simple_time_series(full_df, test_period, display_graphs=True):\n df = full_df.copy()\n df = df.filter([\"Canteen\"])\n\n train = df.iloc[:-test_period]\n test = df.iloc[-test_period:]\n\n resulting_prediction, predictions = prediction(train, test)\n\n if display_graphs is True:\n plt.figure(figsize=(14, 7))\n plt.plot(train)\n plt.plot(resulting_prediction)\n plt.legend([\"Real values\", \"Prediction\"], loc=\"best\")\n plt.xlabel(\"Date\")\n plt.ylabel(\"Number of people\")\n\n print(\n \"The mean absolute error (MAE) for the Simple Time Series model is {0:.0f} people\".format(\n find_MAE(test, predictions)\n )\n )","def walkforward_validation(data, test_start_date, test_end_date=None, step_size=15, testsize=15, model='SARIMA'):\n test_start_date = pd.to_datetime(test_start_date)\n current_max_date = test_start_date\n\n modelling_results = pd.DataFrame(columns=['series_name', 'model_type', 'test_start', 'test_end', 'MAE', 'MAPE', 'RMSE'])\n\n if test_end_date is None:\n test_end_date = data.index.max()\n test_end_date = pd.to_datetime(test_end_date)\n else:\n test_end_date = pd.to_datetime(test_end_date)\n\n while current_max_date < test_end_date:\n data.index = pd.to_datetime(data.index)\n iter_data = data[data.index <= current_max_date + timedelta(days=testsize)]\n test, train = test_train_spl(iter_data, testsize=testsize)\n\n if (model.upper() == 'SARIMA') | (model.upper() == 'SARIMAX'):\n print('USING SARIMA MODEL')\n mae, rmse, mape, name, preds, conf_intervals = mod_sarima(train=train, test=test, **arima_model_params)\n elif model.upper() == 'PROPHET':\n print('USING PROPHET MODEL')\n mae, rmse, mape, name, preds, conf_intervals = mod_prophet(train=train, test=test, **prophet_model_params)\n else:\n print('model name not known')\n iter_results = pd.DataFrame({'series_name': name, 'model_type': model, 'test_start': [current_max_date],\n 'test_end': [current_max_date + timedelta(testsize)], 'MAE': [mae], 'MAPE': [mape], 'RMSE': [rmse]})\n modelling_results = modelling_results.append(iter_results, ignore_index=True)\n\n # this line is just for validation of the effect of regressors in the forecast\n preds.to_csv(mod_report_path + arima_model_params['name'] + 'forecast_' + str(current_max_date).replace(':', '')+ '.csv')\n\n current_max_date = current_max_date + timedelta(days=step_size)\n\n return modelling_results","def __init__(self, calendar_data_path_str, sell_prices_data_path_str, sales_train_validation_data_path_str, train_test_date_split, dept_id = None):\n\n self.sales_train_validation_df = pd.read_csv(sales_train_validation_data_path_str)\n self.sales_train_validation_df.sort_values(\"id\", inplace = True)\n self.calendar_df = pd.read_csv(calendar_data_path_str)\n self.sell_prices_df = pd.read_csv(sell_prices_data_path_str)\n\n # Filter by dept_id if needed\n if dept_id is not None:\n self.sales_train_validation_df = self.sales_train_validation_df.loc[self.sales_train_validation_df[\"cat_id\"] == dept_id]\n\n # Generate a validation set if enable_validation is True\n train_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\"d_\")] + self.calendar_df[\"d\"].loc[self.calendar_df[\"date\"] <= \"2016-03-27\"].tolist()\n test_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\"d_\")] + self.calendar_df[\"d\"].loc[(self.calendar_df[\"date\"] > \"2016-03-27\") & (self.calendar_df[\"date\"] <= \"2016-04-24\")].tolist()\n self.train_df = self.sales_train_validation_df[train_cols_lst].reset_index(drop = True)\n self.valid_df = self.sales_train_validation_df[test_cols_lst].reset_index(drop = True)\n \n self.train_target_columns = [i for i in self.train_df.columns if i.startswith(\"d_\")]\n self.weight_columns = self.train_df.iloc[:, -28:].columns.tolist()\n self.train_df[\"all_id\"] = \"all\"\n self.valid_df[\"all_id\"] = \"all\"\n \n self.id_columns = [i for i in self.train_df.columns if not i.startswith(\"d_\")]\n self.valid_target_columns = [i for i in self.valid_df.columns if i.startswith(\"d_\")]\n\n self.group_ids = (\"all_id\", \"state_id\", \"store_id\", \"cat_id\", \"dept_id\", \"item_id\", \n [\"state_id\", \"cat_id\"], [\"state_id\", \"dept_id\"], [\"store_id\", \"cat_id\"], \n [\"store_id\", \"dept_id\"], [\"item_id\", \"state_id\"], [\"item_id\", \"store_id\"])\n\n self.train_series = self.trans_30490_to_42840(self.train_df, self.train_target_columns)\n self.valid_series = self.trans_30490_to_42840(self.valid_df, self.valid_target_columns)\n self.roll_mat_csr, self.roll_index = self.create_rollup_index()\n self.group_ids_items = self.generate_group_ids_items()\n\n self.S = self.get_s()\n self.W = self.get_w()\n self.SW = self.W / np.sqrt(self.S)\n\n self.weights = self.get_weight_df() # Equivalent to get_w()\n self.scale = self.get_scale() # Equivalent to get_s()\n\n #self.train_series = None\n #self.train_df = None\n gc.collect()","def mark_hours(train_file, test_file):\n train = pd.read_csv(train_file, header=0)\n test = pd.read_csv(test_file, header=0)\n \n \n \n train_hours = train.loc[:, ['duration_label', 'hours']].copy(deep=True).dropna()\n test_hours = test.loc[:, ['hours']].copy(deep=True).dropna()\n \n train_hours['hd'] = train_hours['hours'].apply(lambda x: x**2)\n test_hours['hd'] = test_hours['hours'].apply(lambda x: x**2)\n \n # change label to binary\n binary = {1: 0, 2: 0, 3: 1}\n train_hours['duration_label'] = train_hours['duration_label'].apply(lambda x: binary[x])\n \n # prepare data\n X_train = train_hours.loc[:, ['hours', 'hd']]\n y_train = train_hours['duration_label']\n X_test = test_hours.loc[:, ['hours', 'hd']]\n \n # train & predict\n clf = svm.SVC(kernel='rbf', gamma='auto', C=1, probability=True)\n clf.fit(X_train, y_train)\n predict_train = clf.predict(X_train)\n predict_test = clf.predict(X_test)\n \n train_hours['hour_label'] = predict_train\n test_hours['hour_label'] = predict_test\n \n # merge\n train = pd.merge(train, train_hours['hour_label'], how='left', right_on = 'hour_label', left_index=True, right_index=True).fillna(int(0)).drop('hours', axis=1)\n test = pd.merge(test, test_hours['hour_label'], how='left', right_on = 'hour_label', left_index=True, right_index=True).fillna(int(0)).drop('hours', axis=1)\n \n train.to_csv(train_file, header=True, index=False)\n test.to_csv(test_file, header=True, index=False)","def get_srs_features(df):\n\n #test result classified by labels\n li = df.TestResultsCode.tolist()\n labels = [ item.split('_') for item in li ]\n for item in labels:\n if len(item)==4:\n add = item[0]+item[1]\n item = item.insert( 0, add )\n for item in labels:\n if 'not' in item:\n item.remove('not')\n if 'detected' in item:\n item.remove('detected')\n\n\n #one-hot encode the test results\n disease = [ la[0] for la in labels ]\n spread = [ la[1] for la in labels ]\n risk = [ la[2] for la in labels ]\n\n disease_encode = pd.Series( disease ).str.get_dummies()\n spread_encode = pd.Series( spread ).str.get_dummies()\n risk_encode = pd.Series( risk ).str.get_dummies()\n\n disease_encode = pd.DataFrame( disease_encode )\n spread_encode = pd.DataFrame( spread_encode )\n risk_encode = pd.DataFrame( risk_encode)\n\n #interate one hot encoding of test results back to df\n df=df.drop(['PatientID', 'Address', 'CurrentLocation'],axis=1)\n df2 = df\n df2 = df2.drop(columns = 'TestResultsCode')\n\n results = pd.concat( [risk_encode, spread_encode, disease_encode], axis=1 )\n results = results.drop(['NotSpreader', 'NotatRisk'], axis=1)\n\n from sklearn.model_selection import train_test_split\n\n X_train, X_val, y_train, y_val = train_test_split( df2, results, test_size=0.33, random_state=33 ) #tr is test results numerically coded\n X_val, X_test, y_val, y_test = train_test_split( X_val, y_val , test_size=0.4, random_state=33)\n\n #REMOVED LOCATION FROM FEATURES\n\n # choosing from those features\n cols =['AgeGroup','AvgHouseholdExpenseOnPresents','AvgHouseholdExpenseOnSocialGames',\n 'AvgHouseholdExpenseParkingTicketsPerYear','AvgMinSportsPerDay','AvgTimeOnSocialMedia','AvgTimeOnStuding','BMI',\n 'DisciplineScore','HappinessScore','Job','NrCousins','StepsPerYear','SyndromeClass','TimeOnSocialActivities']\n\n X_train_sfs = X_train[cols]\n X_train_sfs = X_train_sfs.fillna(X_train_sfs.mean())\n\n from sklearn.neighbors import KNeighborsClassifier\n from mlxtend.feature_selection import SequentialFeatureSelector as SFS #import from mlxtend library\n knn = KNeighborsClassifier(n_neighbors=2) # ml_algo used = knn\n sfs = SFS(knn,\n k_features=10,\n forward=True, # if forward = True then SFS otherwise SBS\n floating=False,\n verbose=2,\n scoring='accuracy'\n )\n\n\n #after applying sfs fit the data:\n sfs.fit(X_train_sfs, y_train)\n\n return sfs.k_feature_names_","def __init__(\n self, data_set=\"fake_data\", max_year=(VALIDATION_YEAR_RANGE[0] - 1), **kwargs\n ):\n data_kwargs = {\n **{\n \"data_set\": data_set,\n \"train_year_range\": (max_year,),\n \"test_year_range\": (max_year, max_year + 1),\n },\n **kwargs,\n }\n super().__init__(\n **data_kwargs,\n )\n\n self.max_year = max_year","def test_sales_forecasting_one_ipu(self):\n with TemporaryDirectory() as temp_dir:\n self.run_command(\n (\n \"python3 main.py --use-synthetic-data --epochs 1\"\n f\" --mov-mean-window 0 --log-dir {temp_dir}\"\n ),\n working_path,\n [\n \"Begin training loop\", \"Training:\", r\"epoch:\\s+1\",\n \"Validation:\", \"Best RMSPE|no valid RMSPE results\"\n ]\n )","def test_03_predict(self):\n country = 'all'\n year = 2018\n month = 2\n n_next = 20\n query_data = {'country': 'all',\n 'year': 2018,\n 'month': 2,\n 'n_next': 20\n }\n\n request_json = {'query':query_data,'mode':'test'}\n\n r = requests.post('http://localhost:{}/predict'.format(port),json=request_json)\n response = literal_eval(r.text)\n\n self.assertTrue(len(response['y_pred'])==n_next)\n self.assertTrue(len(response['y_lower'])==n_next)\n self.assertTrue(len(response['y_upper'])==n_next)\n\n for p in response['y_pred']:\n self.assertTrue(isinstance(p,numbers.Number))\n\n for p in response['y_lower']:\n self.assertTrue(isinstance(p,numbers.Number))\n\n for p in response['y_upper']:\n self.assertTrue(isinstance(p,numbers.Number))","def __init__(self, pandas_dataframe, dates_column, target_column, regressors=None, train_test_split=0.66, seed=7,\n look_back=1, look_forward=1, interval=0):\n data = pd.DataFrame(index=pandas_dataframe[dates_column].values, data=pandas_dataframe[target_column].values)\n # Calculate the training set size\n train_size = int(len(data)*train_test_split)\n # Scale the data pre-train/test split\n scaler = MinMaxScaler(feature_range=(0, 1))\n self.scaler = scaler\n data = scaler.fit_transform(data)\n # Get the time series as stationary (for the given interval, if 0 don't make it a series of 0)\n if interval > 0:\n data = difference(data, interval)\n # Map the series to a supervised problem (values for days 1-n with regressors for these days to predict days\n # n + 1 ... n + k\n x, y = timeseries_to_supervised(data, look_back=look_back, look_forward=look_forward)\n # Split train and test\n self.x_train, self.y_train = x[:train_size], y[:train_size]\n self.x_test, self.y_test = x[train_size:], y[train_size:]\n # Use regressors if required\n if regressors is not None:\n self.x_train, self.x_test = add_regressors(self.x_train, self.x_test, regressors, pandas_dataframe,\n dates_column, look_forward, look_back)\n # Set last attributes\n self.seed = seed\n self.look_back = look_back\n self.look_forward = look_forward\n self.regressors = regressors","def fetch_bank_df(preprocess=False):\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n \"bank-marketing\", \"classification\", astype=\"pandas\", preprocess=preprocess\n )\n orig_X = pd.concat([train_X, test_X]).sort_index()\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\n column_map = {\n \"v1\": \"age\",\n \"v2\": \"job\",\n \"v3\": \"marital\",\n \"v4\": \"education\",\n \"v5\": \"default\",\n \"v6\": \"balance\",\n \"v7\": \"housing\",\n \"v8\": \"loan\",\n \"v9\": \"contact\",\n \"v10\": \"day\",\n \"v11\": \"month\",\n \"v12\": \"duration\",\n \"v13\": \"campaign\",\n \"v14\": \"pdays\",\n \"v15\": \"previous\",\n \"v16\": \"poutcome\",\n }\n if preprocess:\n\n def map_col(col):\n if col.find(\"_\") == -1:\n return column_map[col]\n prefix, suffix = col.split(\"_\")\n return column_map[prefix] + \"_\" + suffix\n\n orig_X.columns = [map_col(col) for col in orig_X.columns]\n age = pd.Series(orig_X[\"age\"] >= 25, dtype=np.float64)\n encoded_X = orig_X.assign(age=age)\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [1]},\n ],\n }\n return encoded_X, encoded_y, fairness_info\n else:\n orig_X.columns = [column_map[col] for col in orig_X.columns]\n fairness_info = {\n \"favorable_labels\": [1],\n \"protected_attributes\": [\n {\"feature\": \"age\", \"reference_group\": [[25, 1000]]},\n ],\n }\n return orig_X, orig_y, fairness_info","def create_forecast_dataset(self):\n pass","def baseline(x_data, y_data, stra = \"uniform\"):\r\n x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2)\r\n dummy = DummyClassifier(strategy= stra)\r\n dummy.fit(x_train, y_train)\r\n y_pred = dummy.predict(x_test)\r\n accu = accuracy_score(y_test, y_pred)\r\n return accu","def big9salary(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'big9salary.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/wooldridge/big9salary.csv'\n maybe_download_and_extract(path, url,\n save_file_name='big9salary.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","def fitting_lr_and_rf(file, test_yr, fit_lr, fit_rf):\n df = pd.read_csv(file)\n\n mask_test = (df.year == test_yr)\n mask_train = (df.year >= test_yr-6) & (df.year <= test_yr-1)\n\n target = 'wkts'\n\n features_full = ['year1_mtchs_pld', 'year2_mtchs_pld', 'year3_mtchs_pld', 'year4_mtchs_pld', 'year5_mtchs_pld',\n 'year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm','year4_wkts_pm', 'year5_wkts_pm',\n 'bowler_agnst_oppo', 'oppo_agnst_bowl_typ', 'bowl_home_adv', 'ground_bowl_typ']\n features_small = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm',\n 'bowler_agnst_oppo', 'oppo_agnst_bowl_typ', 'bowl_home_adv', 'ground_bowl_typ']\n features_smaller = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm',\n 'bowl_home_adv', 'ground_bowl_typ']\n features_smallest = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm']\n\n print(\"*********************************************\")\n print(\"**** RUNNING MODELS FOR FULL FEATURE SET ****\")\n print(\"*********************************************\")\n\n features = features_full.copy()\n\n X_train = df[mask_train][features]\n y_train = df[mask_train][target]\n X_test = df[mask_test][features]\n y_test = df[mask_test][target]\n\n if fit_lr:\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n if fit_rf:\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n\n print(\"**********************************************\")\n print(\"**** RUNNING MODELS FOR SMALL FEATURE SET ****\")\n print(\"**********************************************\")\n\n features = features_small.copy()\n\n X_train = df[mask_train][features]\n y_train = df[mask_train][target]\n X_test = df[mask_test][features]\n y_test = df[mask_test][target]\n\n if fit_lr:\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n if fit_rf:\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n\n print(\"************************************************\")\n print(\"**** RUNNING MODELS FOR SMALLER FEATURE SET ****\")\n print(\"************************************************\")\n\n features = features_smaller.copy()\n\n X_train = df[mask_train][features]\n y_train = df[mask_train][target]\n X_test = df[mask_test][features]\n y_test = df[mask_test][target]\n\n if fit_lr:\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n if fit_rf:\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n\n print(\"*************************************************\")\n print(\"**** RUNNING MODELS FOR SMALLEST FEATURE SET ****\")\n print(\"*************************************************\")\n\n features = features_smallest.copy()\n\n X_train = df[mask_train][features]\n y_train = df[mask_train][target]\n X_test = df[mask_test][features]\n y_test = df[mask_test][target]\n\n if fit_lr:\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\n\n if fit_rf:\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)","def test_ann(trainx, trainy, testx, testy):\n\tinput_size = len(trainx.iloc[0])\n\toutput_size = len(np.unique(trainy))\n\ttrain = ClassificationDataSet(input_size, 1, nb_classes=output_size)\n\tfor i in range(len(trainx.index)):\n\t\ttrain.addSample(trainx.iloc[i].values, trainy.iloc[i])\n\n\ttest = ClassificationDataSet(input_size, 1, nb_classes=output_size)\n\tfor i in range(len(testx.index)):\n\t\ttest.addSample(testx.iloc[i].values, testy.iloc[i])\n\ttrain._convertToOneOfMany()\n\ttest._convertToOneOfMany()\n\n\tprint(\"Number of training patterns: \", len(train))\n\tprint(\"Input and output dimensions: \", train.indim, train.outdim)\n\tprint(\"First sample (input, target):\")\n\tprint(train[\"input\"][0], train[\"target\"][0])\n\n\tn_hidden = 3\n\tfnn = buildNetwork(train.indim, n_hidden, train.outdim)\n\ttrainer = BackpropTrainer(\n\t fnn, dataset=train, momentum=0.1, verbose=True, weightdecay=0.01)\n\n\tprint(\"# hidden nodes: {}\".format(n_hidden))\n\tfor i in range(25):\n\t trainer.trainEpochs(1)\n\t trnresult = percentError(trainer.testOnClassData(), train[\"target\"])\n\t tstresult = percentError(\n\t trainer.testOnClassData(dataset=test), test[\"target\"])\n\t print(\"epoch: %4d\" % trainer.totalepochs, \" train error: %5.2f%%\" % trnresult, \" test error: %5.2f%%\" % tstresult)\n\tpred = fnn.activateOnDataset(test)\n\tpreds = [y.argmax() for y in pred]\n\tprint(accuracy_score(preds, testy, normalize=True))","def cps78_85(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'cps78_85.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/wooldridge/cps78_85.csv'\n maybe_download_and_extract(path, url,\n save_file_name='cps78_85.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","def test_train_bad():\n\tdf = pd.read_csv(\"test/sample_features.csv\")\n\tdf['test'] = 'test'\n\n\ty_train = df['price']\n\tX_train = df.loc[:, df.columns != 'price']\n\n\tparams = {'n_estimators': 5, 'random_state': 2}\n\n\twith pytest.raises(ValueError) as e:\n\t\trf_test = train(X_train, y_train, params)\n\n\tassert str(e.value) == 'Wrong data type of inputs for training a random forest regression model!'","def forecast(self, stock):\n # Load the trained model\n model_handler = ModelHandler()\n model = model_handler.load_json_model(stock)\n\n # Importing the training set\n dataset = pd.read_csv(stock.csv_name)\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\n\n # Create the test dataset\n dataset_test = dataset[len(dataset) - 30:]\n real_stock_price = dataset_test.iloc[:, 1:2].values\n dataset = dataset['Open']\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\n inputs = inputs.reshape(-1, 1)\n\n # Feature Scaling\n sc = MinMaxScaler(feature_range=(0, 1))\n inputs = sc.fit_transform(inputs)\n\n x_test = []\n x_test.append(inputs[0:60, 0])\n predicted_values = []\n for i in range(1, 31):\n x_test_np = np.array(x_test)\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\n new_data = model.predict(x_test_np)\n predicted_values.append(new_data[0])\n x_test[0] = np.delete(x_test[0], 0)\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\n\n predicted_values = sc.inverse_transform(predicted_values)\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\n plt.gcf().autofmt_xdate()\n plt.title(f'{stock.ticker} Stock Price Prediction')\n plt.xlabel('Time')\n plt.ylabel(f'{stock.ticker} Stock Price')\n plt.legend()\n plt.show()","def _assess(self):\n # get eruptions\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\n # check if data file exists\n self.exists = os.path.isfile(self.file)\n if not self.exists:\n t0 = datetime(2011,1,1)\n t1 = datetime(2011,1,2)\n self.update(t0,t1)\n # check date of latest data in file\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def train_on_raw_data(data_alice, data_bob):\n raw_test_data = pd.concat([data_alice[1], data_bob[1]])\n x = raw_test_data.drop('Class', axis=1)\n y = raw_test_data['Class']\n\n return fit_rf_classifier(x, y)","def predict(self, next_days):\n last_date = self.series.index[-1]\n for time in range(next_days):\n row = {}\n for c in self.countries:\n history = self.series[c]\n series = history.astype(float)\n model = ARIMA(series, order=(2,1,0))\n model_fit = model.fit()\n output = model_fit.forecast()\n row[c] = output[0][0]\n last_date = last_date + pd.DateOffset(1)\n self.series.loc[last_date] = row","def parse_data():\n n_train, n_test = 15000, 4996\n n_features = 1355191\n\n print('- parsing train data')\n X_train = sp.lil_matrix((n_train, n_features))\n y_train = np.zeros(n_train)\n with open('/Users/kitazawa/data/news20.train') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_train[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_train[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\n\n print('- parsing test data')\n X_test = sp.lil_matrix((n_test, n_features))\n y_test = np.zeros(n_test)\n with open('/Users/kitazawa/data/news20.test') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_test[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_test[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\n\n return X_train, y_train, X_test, y_test","def test_load_time_series (self):\n self.dm._load_time_series()\n df = self.dm.df_final\n # Define expected results.\n dt_1 = pd.to_datetime('3/1/2018')\n dt_2 = pd.to_datetime('2/10/2018')\n\n expected = [('SP500', dt_1, -0.013324),\n ('btc', dt_1, 0.053193),\n ('btc_volume', dt_1, 0.05040065),\n ('eth', dt_2, -0.026542),\n ('eth_volume', dt_2, -0.209714)]\n for (col, idx, value) in expected:\n msg = '{0} value not what expected on {1}'.format(\n col, cryp.fmt_date(idx))\n actual_value = df.loc[idx, col]\n self.assertAlmostEqual(value, actual_value, DEC_ACCY, msg)\n\n ##############################\n # Test non-crypto-asset (SPX) to ensure returns properly rolled forward.\n ##############################\n # Return on 1/13/2018 (Saturday), 1/14 (Sunday), and 1/15 (MLK day)\n # should all be equal to the return on 1/12 (Friday).\n # Next \"new\" return should be on 1/16 (price change from 1/12 to 1/16).\n expected_return_1 = 0.006750 # index change from 1/11 -> 1/12.\n expected_return_2 = -0.003524 # index change from 1/12 -> 1/16.\n expected_spx = [(pd.to_datetime('1/12/2018'), expected_return_1),\n (pd.to_datetime('1/13/2018'), expected_return_1),\n (pd.to_datetime('1/14/2018'), expected_return_1),\n (pd.to_datetime('1/15/2018'), expected_return_1),\n (pd.to_datetime('1/16/2018'), expected_return_2)]\n\n for (idx, value) in expected_spx:\n msg = 'SP500 return on {} not what expected.'.format(\n cryp.fmt_date(idx))\n actual_value = df.loc[idx, 'SP500']\n self.assertAlmostEqual(value, actual_value, DEC_ACCY, msg)","def test_04_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n\n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\n self.assertEqual(y_pred,logged_y_pred)","def fit(self, train):\n model = ARIMA(train, order=(self.p, self.d, self.q))\n model_fit = model.fit(disp=0)\n output = model_fit.forecast()\n nextDaysPred = output[0][0]\n return nextDaysPred","def growing_testset(train_filename, test_filename, clf, clf_name=None):\n train_features, train_groundtruths = read_file(train_filename)\n test_features, test_groundtruths = read_file(test_filename)\n \n if clf_name is not None and \"RANSAC\" in clf_name:\n train_groundtruths = [True if i ==\"s\" else False for i in train_groundtruths]\n test_groundtruths = [True if i ==\"s\" else False for i in test_groundtruths]\n\n clf.fit(train_features, train_groundtruths)\n\n if clf_name is not None and \"RANSAC\" in clf_name:\n preds_float = clf.predict(test_features)\n predictions = [True if i > 0.5 else False for i in preds_float]\n else:\n predictions = clf.predict(test_features)\n \n test_acc = []\n # test_acc.append(accuracy_score(test_groundtruths, predictions))\n test_acc.append(precision_score(test_groundtruths, predictions, average=None)[0])\n print(\"Accuracy : \" + str(test_acc))\n print(\"Precision: \" + str(precision_score(test_groundtruths, predictions, average=None)))\n print(\"Recall : \" + str(recall_score(test_groundtruths, predictions, average=None)))\n print(\"F-score : \" + str(f1_score(test_groundtruths, predictions, average=None)))\n\n n_splits = 10\n for n_split in range(2, n_splits+1):\n print(n_split)\n feats_array, gts_array = split(test_features, test_groundtruths, n_split)\n tmp_acc = []\n for feats, gts in zip(feats_array, gts_array):\n if clf_name is not None and \"RANSAC\" in clf_name:\n preds_float = clf.predict(feats)\n predictions = [True if i > 0.5 else False for i in preds_float]\n else:\n predictions = clf.predict(feats)\n # cur_acc = accuracy_score(gts, predictions)\n cur_acc = precision_score(gts, predictions, average=None)[0]\n tmp_acc.append(cur_acc)\n print(\"\\t\" + str(cur_acc))\n accuracy = sum(tmp_acc) / float(len(tmp_acc))\n test_acc.append(accuracy)\n return test_acc","def test_data_preprocessing(raw_data): \r\n\r\n # get data output\r\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\r\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\r\n with open('cache/extended_columns.pkl', 'rb') as f:\r\n extended_columns = pickle.load(f)\r\n with open('cache/num_date_columns.pkl', 'rb') as f:\r\n max_date_columns = pickle.load(f)\r\n \r\n data_output = class_binaryzation_for_test(data_output, extended_columns)\r\n try:\r\n data_output = date_separation1(data_output, max_num_columns=NUM_DATE_COLUMNS)\r\n except:\r\n data_output = date_separation2(data_output)\r\n data_output = money_digitalization(data_output)\r\n data_output = get_date_in_days(data_output)\r\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\r\n - data_output['Posting Date Date Days from 2016'] \r\n\r\n return data_output","def ANdatefixer(years):\n\n\n\t# ========== create the new dates ==========\n\t# year = ds.Year\n\n\t# +++++ set up the list of dates +++++\n\tdates = OrderedDict()\n\ttm = [dt.datetime(int(year) , 6, 30) for year in years]\n\tdates[\"time\"] = pd.to_datetime(tm)\n\n\tdates[\"calendar\"] = 'standard'\n\tdates[\"units\"] = 'days since 1900-01-01 00:00'\n\t\n\tdates[\"CFTime\"] = date2num(\n\t\ttm, calendar=dates[\"calendar\"], units=dates[\"units\"])\n\n\treturn dates","def test(self):\n y_list = []\n y_hat_list = []\n for ex_dict in ut.TEST_LIST:\n y_list.append(ex_dict[1])\n y_hat_list.append(self.predict(ex_dict[0]))\n acc = ut.compute_accuracy(y_hat_list, y_list)\n return y_hat_list, acc","def train_predict(train_file_arg):\n\n # Load command line arguments\n train_file = train_file_arg\n parameter_file = \"prophet/lstm/trainingConfig.json\"\n\n # Load training parameters\n params = json.loads(open(parameter_file).read())\n\n # Load time series dataset, and split it into train and test\n x_train, y_train, x_test, _, x_test_raw, _,\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\n\n # Build RNN (LSTM) model\n lstm_layer = [1, params[\"window_size\"], params[\"hidden_unit\"], 1]\n model = buildModel.rnn_lstm(lstm_layer, params)\n\n # Train RNN (LSTM) model with train set\n model.fit(\n x_train,\n y_train,\n batch_size=params[\"batch_size\"],\n epochs=params[\"epochs\"],\n validation_split=params[\"validation_split\"])\n\n # Check the model against test set\n predicted = buildModel.predict_next_timestamp(model, x_test)\n predicted_raw = []\n for i in range(len(x_test_raw)):\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\n\n # Predict next time stamp\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\n print(\"The next time stamp forecasting is: {}\".format(next_timestamp_raw))\n\n # Add 5 minutes for a new timestamp of predictions\n last_datetime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(last_datetime_epoch))\n last_datetime = datetime.strptime(last_datetime, \"%Y-%m-%d %H:%M:%S\")\n new_datetime = last_datetime + timedelta(seconds=300)\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\n new_datetime = new_datetime.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Concatenate datetime and price forecast\n new_entry = \"coin_name,\" + str(new_datetime_epoch) + \",\" \\\n + str(next_timestamp_raw) + \",coin_supply\" + \",coin_mc\" + \"\\n\"\n\n # Write to CSV file of new prediction\n fd = open(train_file, \"a\")\n fd.write(new_entry)\n fd.close()\n\n # Return new prediction\n return [new_datetime, str(next_timestamp_raw)]","def read300yr(period):\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\n file300 = 'U10_1700-2000.nc'\n filename = directory300 + file300\n \n data = Dataset(filename)\n lat = data.variables['latitude'][:]\n lon = data.variables['longitude'][:]\n u10q = data.variables['U10'][:]\n data.close()\n \n ### Reshape in year/month\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\n \n ### Calculate over particular months\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\n \n ### Slice U10 at 65N\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\n lat = lat[latq].squeeze()\n u10 = u10[:,latq,:].squeeze()\n \n ### Take zonal mean \n u10z = np.nanmean(u10,axis=1)\n \n ### Remove missing data\n mask = np.where(u10z > -1e5)[0]\n \n ### Detrend\n u10zdt = sss.detrend(u10z[mask],type='linear')\n \n return lat,lon,u10zdt","def parseAndPopulateTrainRunningDates(jsonData, trainNumber):\n if not jsonData:\n logger.error(\"TrainNumber[%s] doesn't run on any day.\", trainNumber)\n return[]\n\n returneddata = json.loads(jsonData)\n jsonLogger.info(\"Days Data[%s] for TrainNumber[%s] *** \",returneddata,trainNumber)\n days = {}\n\n if returneddata[\"response_code\"]==200:\n trainData = returneddata[\"train\"]\n daysData = trainData[\"days\"]\n if daysData:\n for day in trainData[\"days\"]:\n days[day[\"day-code\"]]=day[\"runs\"]\n\n if len(days) == 0:\n logger.error(\"TrainNumber[%s] doesn't run on any day.\", trainNumber)\n return []\n\n models.addRunningDaysToTrain(days, trainNumber)","def buildAndTrain(trainingData):\n\tname = trainingData.drop(['count', 'casual', 'registered'], axis=1).columns\n\ttarget = trainingData['count'].values\n\tfeature = trainingData.drop(['count', 'casual', 'registered'], axis=1).values\n\t# feature scaling\n\tfeature_scaled = preprocessing.scale(feature)\n\t# 0.5 cross validate\n\tcv = cross_validation.ShuffleSplit(len(feature_scaled), n_iter=5, test_size=0.2, random_state=0)\n\t# build model, then training and get accuracy of it\n\tprint('\\n---------岭回归结果--------\\n')\n\tfor train, test in cv:\n\t\tregLR = linear_model.Ridge().fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregLR.score(feature_scaled[train], target[train]),\n\t\t regLR.score(feature_scaled[test], target[test])))\n\tprint('\\n---------svm结果--------\\n')\n\tfor train, test in cv:\n\t\tregSvm = svm.SVR().fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregSvm.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregSvm.score(feature_scaled[test], target[test])))\n\tprint('\\n---------随机森林结果--------\\n')\n\tfor train, test in cv:\n\t\tregRF = RandomForestRegressor(n_estimators=100).fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRF.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRF.score(feature_scaled[test], target[test])))\n\t# reduce some low correction feature\n\tfeatureReduced = trainingData.drop(['count', 'casual', 'registered', 'holiday', 'workingday', 'day'], axis=1).values\n\tfeatureReduced_scaled = preprocessing.scale(featureReduced)\n\tprint('\\n---------减少特征维度以避免过拟合后的随机森林结果--------\\n')\n\tfor train, test in cv:\n\t\tregRFImpr = RandomForestRegressor(n_estimators=100).fit(featureReduced_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFImpr.score(featureReduced_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFImpr.score(featureReduced_scaled[test], target[test])))\n\t# use grid search algorithm to improve random forest regression\n\tX_train, X_test, y_train, y_test = cross_validation.train_test_split(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeature_scaled, target, test_size=0.2, random_state=0)\n\ttuned_parameters = [{'n_estimators': [10,100,500], 'max_depth': [2,3,4,5,6,7,8,9,10]}]\n\tscores = ['r2']\n\n\tfor score in scores:\n\t\tprint(score)\n\t\tclf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)\n\t\tclf.fit(X_train, y_train)\n\t\tprint(clf.best_estimator_)\n\t\tprint('each parameter combination is ')\n\t\tfor params, mean_score, scores in clf.grid_scores_:\n\t\t\tprint('{0:.3f} (+/-{1:.03f}) for {2}'.format(mean_score, scores.std()/2, params))\n\n\tprint('--------最优参数下的随机森林结果--------')\n\tfor train, test in cv:\n\t\tregRFBest = RandomForestRegressor(n_estimators=100, max_depth=10).fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFBest.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFBest.score(feature_scaled[test], target[test])))\n\treturn regRFBest, feature_scaled, target","def prepare_training_data(consumption_series, lag):\r\n # scale training data\r\n scaler = MinMaxScaler(feature_range=(-1, 1))\r\n consumption_vals = scaler.fit_transform(consumption_series.values.reshape(-1, 1))\r\n \r\n # convert consumption series to lagged features\r\n consumption_lagged = create_lagged_features(consumption_vals, lag=lag)\r\n\r\n # X, y format taking the first column (original time series) to be the y\r\n X = consumption_lagged.drop('consumption', axis=1).values\r\n y = consumption_lagged.consumption.values\r\n \r\n # keras expects 3 dimensional X\r\n X = X.reshape(X.shape[0], 1, X.shape[1])\r\n \r\n return X, y, scaler","def clean_features(test_data, train_data, undef):\n \n test_set = test_data\n train_set = train_data\n \n # Preprocessing of the four jet datasets\n for jet in range(4):\n \n # Remove columns full of undefined values (-999.0 in the datase)\n train_set[jet][train_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\n train_set[jet] = train_set[jet][:, ~np.all(np.isnan(train_set[jet]), axis=0)]\n test_set[jet][test_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\n test_set[jet] = test_set[jet][:, ~np.all(np.isnan(test_set[jet]), axis=0)]\n\n # Remove columns without standard deviation to remove column full of the same value\n train_id_pred = train_set[jet][:,0:2]\n train_features = train_set[jet][:,2:]\n train_features = train_features[:, np.nanstd(train_features, axis=0) != 0]\n train_set[jet] = np.concatenate((train_id_pred, train_features), axis=1)\n \n # Extract prediction column of test before because it has std=0, and re-insert it after at the begining of the table\n test_id_pred = test_set[jet][:,0:2]\n test_features = test_set[jet][:,2:]\n test_features = test_features[:, np.nanstd(test_features, axis=0) != 0]\n test_set[jet] = np.concatenate((test_id_pred, test_features), axis=1)\n \n return test_set, train_set","def data_read(variable):\t\t\r\n\tdef day2datetime(scenario,days):\r\n\t\t\"\"\"\r\n\t\t# convert days from a reference into int datetime \r\n\t\t# do not take leap years into account\r\n\t\t\"\"\"\r\n\t\tdate_int = np.empty((len(days)));date_int[:]=np.nan\r\n\t\tif scenario =='T1970C': start_year =1970\r\n\t\telse: start_year =2010\r\n\t\tstart =(start_year*365)\r\n\t\tith=0\t\r\n\t\tfor iday in days:\r\n\t\t\tmonth_days =np.array([31,28,31,30,31,30,31,31,30,31,30,31])\r\n\t\t\tcalendar_days = np.array([0,31,59,90,120,151,181,212,243,273,304,334,365])\r\n\t\t\ttotal_days = int(iday) + start; \r\n\t\t\tyear = total_days//365; \r\n\t\t\tremainder = total_days%365\r\n\t\t\tif remainder ==0: year=year-1;month=12;day=31\r\n\t\t\telse: \r\n\t\t\t\tmonth = 1+[layer for layer in range(len(calendar_days)) if calendar_days[layer]< remainder and calendar_days[layer+1]>=remainder][0]\r\n\t\t\t\tday = int(remainder - calendar_days[month-1])\r\n\t\t\t\tif day == 0: day = month_days[month-1]\r\n\t\t\tdate_int[ith] = year*10000+month*100+day\r\n\t\t\tith=ith+1\r\n\t\treturn date_int.astype(int)\r\n\t\t\r\n\tdef mon_mean2annual_mean(scenario,time,data):\r\n\t\tannual_mean=np.empty((30,192,288));annual_mean[:]=np.nan\r\n\t\tcalendar_day = np.array([31,28,31,30,31,30,31,31,30,31,30,31])\r\n\t\tif scenario=='T1970RCP':\r\n\t\t\tyear_series = range(2020,2050)\r\n\t\telif scenario=='EdgEne':\r\n\t\t\tyear_series = range(2200,2230)\r\n\t\telif scenario=='Edg70GO':\r\n\t\t\tyear_series = range(2070,2100)\r\n\t\telse:\r\n\t\t\tyear_series = range(2130,2160)\r\n\t\tfor iyear in year_series:\r\n\t\t\t\r\n\t\t\tif (iyear == year_series[0] and time[0]//100 >= year_series[0] *100+1):\r\n\t\t\t\tlayer_b=0\r\n\t\t\telse:\r\n\t\t\t\tlayer_b = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+1][0] #June01\r\n\t\t\tif (iyear == year_series[-1] and time[-1]//100 <= year_series[-1] *100+12):\r\n\t\t\t\tlayer_e=-2\r\n\t\t\telse:\r\n\t\t\t\tlayer_e = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+12][0] #August 31\r\n\t\t\tdata_cache = data[layer_b:layer_e+1,:,:]\r\n\t\t\tannual_mean[iyear-year_series[0],:,:] = stats.nanmean(data_cache,axis=0)\r\n\t\treturn annual_mean\r\n\r\n\tdef data_netcdf(scenario,variable):\r\n\t\tinput_path ='/exports/csce/datastore/geos/users/s1667168/CESM_EDGAR/ModelOutput/FullCp/'\r\n\t\tvar_path = input_path+scenario+'/mon/atm/'+scenario+'.atm.mon.'+variable+'.nc'\r\n\t\t# print var_path\r\n\t\tnc_fid = nc4.Dataset(var_path,mode='r')\r\n\t\tlat = nc_fid.variables['lat'][:]\r\n\t\tlon = nc_fid.variables['lon'][:]\r\n\t\tdays = nc_fid.variables['time'][:]; time = day2datetime(scenario,days);#print time\r\n\t\tif variable ==\"VQ\" or variable == \"VT\":\r\n\t\t\tdata = np.nanmean(nc_fid.variables[variable][:,23:30,:,:],axis=1) # 850hpa\r\n\t\telse:\r\n\t\t\tdata = nc_fid.variables[variable][:]#-273.15\r\n\t\tnc_fid.close()\r\n\t\tvar40map = mon_mean2annual_mean(scenario,time,data)\r\n\t\treturn lon,lat,var40map\r\n\t\r\n\tlon,lat,Edg70GO = data_netcdf('Edg70GO',variable)\r\n\t_,_,T1970 = data_netcdf('T1970RCP',variable)\r\n\t_,_,EdgRef = data_netcdf('EdgRef',variable)\r\n\t_,_,Edg70Oz = data_netcdf('Edg70Oz',variable)\r\n\t_,_,EdgEne = data_netcdf('EdgEne',variable)\r\n\t_,_,EdgTech = data_netcdf('EdgTech',variable)\r\n\treturn lon,lat,T1970,Edg70GO,Edg70Oz,EdgRef,EdgEne,EdgTech","def test(self):\n with torch.no_grad():\n self.model.eval()\n p10_forecast, p10_forecast, p90_forecast, target = None, None, None, None\n\n t = time()\n for step, sample in enumerate(self.test_loader):\n\n # Hide future predictions from input vector, set to 0 (or 1) values where timestep > encoder_steps\n steps = self.cnf.all_params['num_encoder_steps']\n pred_len = sample['outputs'].shape[1]\n x = sample['inputs'].float().to(self.cnf.device)\n x[:, steps:, 0] = 1\n\n # Feed input to the model\n if self.cnf.all_params[\"model\"] == \"transformer\" or self.cnf.all_params[\"model\"] == \"grn_transformer\":\n\n # Auto-regressive prediction\n for i in range(pred_len):\n output = self.model.forward(x)\n x[:, steps + i, 0] = output[:, i, 1]\n output = self.model.forward(x)\n\n elif self.cnf.all_params[\"model\"] == \"tf_transformer\":\n output, _, _ = self.model.forward(x)\n else:\n raise NameError\n\n output = output.squeeze()\n y, y_pred = sample['outputs'].squeeze().float().to(self.cnf.device), output\n\n # Compute loss\n loss, _ = self.loss(y_pred, y)\n smape = symmetric_mean_absolute_percentage_error(output[:, :, 1].detach().cpu().numpy(),\n sample['outputs'][:, :, 0].detach().cpu().numpy())\n\n # De-Normalize to compute metrics\n target = unnormalize_tensor(self.data_formatter, y, sample['identifier'][0][0])\n p10_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 0], sample['identifier'][0][0])\n p50_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 1], sample['identifier'][0][0])\n p90_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 2], sample['identifier'][0][0])\n\n # Compute metrics\n self.test_losses['p10'].append(self.loss.numpy_normalised_quantile_loss(p10_forecast, target, 0.1))\n self.test_losses['p50'].append(self.loss.numpy_normalised_quantile_loss(p50_forecast, target, 0.5))\n self.test_losses['p90'].append(self.loss.numpy_normalised_quantile_loss(p90_forecast, target, 0.9))\n\n self.test_loss.append(loss.item())\n self.test_smape.append(smape)\n\n # Plot serie prediction\n p1, p2, p3, target = np.expand_dims(p10_forecast, axis=-1), np.expand_dims(p50_forecast, axis=-1), \\\n np.expand_dims(p90_forecast, axis=-1), np.expand_dims(target, axis=-1)\n p = np.concatenate((p1, p2, p3), axis=-1)\n plot_temporal_serie(p, target)\n\n # Log stuff\n for k in self.test_losses.keys():\n mean_test_loss = np.mean(self.test_losses[k])\n print(f'\\t● AVG {k} Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\n\n # log log log\n mean_test_loss = np.mean(self.test_loss)\n mean_smape = np.mean(self.test_smape)\n print(f'\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\n print(f'\\t● AVG SMAPE on TEST-set: {mean_smape:.6f} │ T: {time() - t:.2f} s')","def load_data():\r\n from sklearn.feature_extraction.text import CountVectorizer\r\n\r\n # Load the data\r\n\r\n with open(\"clean_real.txt\", 'r') as RealNews:\r\n RealStrAr = RealNews.read().split('\\n')\r\n\r\n with open(\"clean_fake.txt\", 'r') as FakeNews:\r\n FakeStrAr = FakeNews.read().split('\\n')\r\n\r\n # Preprocess it using a vectorizer\r\n\r\n MyCoolVectorizer = CountVectorizer()\r\n X = MyCoolVectorizer.fit_transform(RealStrAr + FakeStrAr)\r\n\r\n RealLabels = np.ones((len(RealStrAr), 1)) # means real\r\n FakeLabels = np.zeros((len(FakeStrAr), 1)) # means fake\r\n AllLabels = np.append(RealLabels, FakeLabels, axis=0)\r\n\r\n FinalTensor = np.append(X.toarray(), AllLabels, axis=1)\r\n\r\n # Randomize it and split it\r\n\r\n np.random.shuffle(FinalTensor)\r\n\r\n # divide and multiply by 2 just to make sure it's even\r\n ROUGHLY70 = 2 * ((FinalTensor.shape[0] * 70 / 100) / 2)\r\n ROUGHLY15 = (FinalTensor.shape[0] - ROUGHLY70) / 2\r\n\r\n # TEST SET VALIDATION SET TRAINING SET DICTIONARY\r\n return (FinalTensor[:ROUGHLY15], FinalTensor[ROUGHLY15 : 2 * ROUGHLY15], FinalTensor[-ROUGHLY70:], MyCoolVectorizer.get_feature_names())","def fit_timeseries(xdates, ydata):\n\n pass","def test_ARIMA_pred(self, whp_pandas):\n test_class = Slug_Forecasting(whp_pandas.copy())\n test_class.stationarity_check()\n test_class.split_data()\n test_class.ARIMA_model(1, 0, 1, show=False)\n test_class.ARIMA_pred(pred_time=60)\n assert hasattr(test_class, \"forecast\"), \"Forecast attribute must have been created\"","def test():\n temp_data = fetch_temp_data(\n (\"https://opendata-download-metobs.smhi.se/api/version/\" +\n \"latest/parameter/1/station/52350/period/latest-day/data.json\"))\n data = temp_series(temp_data)\n print(data)","def test_train_test_split_uni_exo(load_uni_exo_data_target):\n data, target = load_uni_exo_data_target\n\n ####################################\n #### Continuous fh without Gaps ####\n ####################################\n\n #### Integer fh ----\n exp = TSForecastingExperiment()\n fh = 12\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test.index == data.iloc[-fh:].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(1, 10) # 9 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [1, 2, 3, 4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\n\n #################################\n #### Continuous fh with Gaps ####\n #################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.arange(7, 13) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n\n ####################################\n #### Discontinuous fh with Gaps ####\n ####################################\n\n #### Numpy fh ----\n exp = TSForecastingExperiment()\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)\n\n #### List fh ----\n exp = TSForecastingExperiment()\n fh = [4, 5, 6, 10, 11, 12]\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\n assert np.all(exp.dataset.index == data.index)\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\n # `test`` call still refers to y_test indices and not X_test indices\n assert len(exp.test) == len(fh)\n assert np.all(exp.X.index == data.index)\n assert np.all(exp.y.index == data.index)\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\n assert len(exp.y_test) == len(fh)\n assert np.all(exp.dataset_transformed.index == data.index)\n assert np.all(\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.test_transformed) == len(fh)\n assert np.all(exp.X_transformed.index == data.index)\n assert np.all(exp.y_transformed.index == data.index)\n assert np.all(\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n # Exogenous variables will not have any gaps (only target has gaps)\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\n assert np.all(\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\n )\n assert len(exp.y_test_transformed) == len(fh)","def create_train_set(addition_to_filename = ''):\n df = f.create_df()\n print('original df size is ', df.shape)\n print('original df columns ', df.columns)\n\n df = f.calculate_missing_prices_for_train_set(df)\n print('df size after averaging price ', df.shape)\n df = f.downcast_dtypes(df)\n df = f.add_lag(all_data_df = df, df_to_add_lag= df, number_of_months=3)\n df = f.add_days_stat(df)\n\n print(df.columns)\n\n # df = h.add_holidays(df)\n # print('df size with holidays ', df.shape)\n\n timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n pickle.dump(df, open(f\"{timestr}_{addition_to_filename}_train.pickle.dat\", \"wb\"))\n\n # save feature names for further use\n features_list = create_feature_names_list(df)\n pickle.dump(features_list, open(f\"{timestr}_{addition_to_filename}_features.pickle.dat\", \"wb\"))","def adf_test(series,title=''):\n print(f'Augmented Dickey-Fuller Test: {title}')\n result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n \n labels = ['ADF test statistic','p-value','# lags used','# observations']\n out = pd.Series(result[0:4],index=labels)\n\n for key,val in result[4].items():\n out[f'critical value ({key})']=val\n \n print(out.to_string()) # .to_string() removes the line \"dtype: float64\"\n \n if result[1] <= 0.05:\n print(\"Strong evidence against the null hypothesis\")\n print(\"Reject the null hypothesis\")\n print(\"Data has no unit root and is stationary\")\n else:\n print(\"Weak evidence against the null hypothesis\")\n print(\"Fail to reject the null hypothesis\")\n print(\"Data has a unit root and is non-stationary\")","def test_change_of_year(self):\n\n input_ = [\n self.indicator_record(date=datetime.date(2006, 11, 1), value=0.31),\n self.indicator_record(date=datetime.date(2006, 12, 1), value=0.48),\n ]\n output = self.expander._ipca_from_15_expander(input_)\n expected = self.indicator_record(date=datetime.date(2007, 1, 1), value=0.35)\n actual = output[-1]\n\n self.assertEqual(expected, actual)","def transform_data(data_orig):\n\tif isinstance(data_orig, str):\n\t\tdata_orig = pd.read_csv(data_orig)\n\n\tdata = data_orig\n\n\tnum_rows,num_variables = data.shape\n\tall_columns = data.columns.tolist()\n\tclean_data(data,all_columns,ignore_na=False,fill_mode=\"prob\")\n\texpand_features(data)\n\tvariables = ['Pclass','Sex',\"Fare\",\"Age\",\"SibSp\",\"Parch\",\"Embarked\",\"Fam_size\",\\\n\t\t\t\t \"cabin_no\",\"ticket_no\",\"friend\",\"Fare_person\",\"Child\"]\n\tX = pd.get_dummies(data[variables])\n\n\t## normalise features to zero man and unit variance\n\tscaler = preprocessing.StandardScaler().fit(X)\n\tX_scaled = scaler.transform(X)\n\tX = pd.DataFrame(X_scaled, columns=X.columns)\n\n\tif \"Survived\" in data.columns:\n\t\ty = data['Survived']\n\telse:\n\t\ty = None\n\n\treturn X, y","def prepare_data(train, test):\n # change the name of the target column\n train.rename(columns={\"revenue\": \"target\"}, inplace=True)\n # map bool values to yes and no\n train[\"Weekend\"] = train[\"Weekend\"].map({True: \"Yes\", False: \"No\"})\n test[\"Weekend\"] = test[\"Weekend\"].map({True: \"Yes\", False: \"No\"})\n # set the id col as index\n train.set_index(\"id\", inplace=True)\n test.set_index(\"id\", inplace=True)\n\n # seperate the fetures and the target\n X_train = train.drop(\"target\", axis=1).copy()\n y_train = train[\"target\"].copy()\n X_test = test.copy()\n\n # select numerical and categorical columns\n num_cols = X_train.select_dtypes(exclude=\"object\").columns.tolist()\n cat_cols = X_train.select_dtypes(include=\"object\").columns.tolist()\n\n # numerical pipeline\n num_pipe = make_pipeline(SimpleImputer(strategy=\"mean\"))\n\n # categorical pipeline\n cat_pipe = make_pipeline(\n SimpleImputer(strategy=\"constant\", fill_value=\"NA\"),\n OneHotEncoder(handle_unknown=\"ignore\", sparse=False),\n )\n\n # full pipeline for data preprocessing\n full_pipe = ColumnTransformer(\n [(\"num\", num_pipe, num_cols), (\"cat\", cat_pipe, cat_cols)]\n )\n return X_train, y_train, X_test, full_pipe","def run_forecast(data_start_date='1980-01-01', forecast_end_date='2015-12-01',\n test_date_start='2010-01-01', test_model=True):\n \n # 1. Prepare dataframe.\n df = read_and_prepare_dataframe(start_date=data_start_date)\n \n # 2. Split dataframe into train and test.\n y, y_train, y_test = time_series_train_test_split_y(df=df, test_date_start=test_date_start)\n \n # 3. Optimize parameters.\n opt_order, opt_seasonal_order = optimize_sarima_parameters(y,\n trend='c', start_p=0, start_q=0, test='adf', \n max_order=6, stepwise=True, trace=False)\n \n # 4. Train SARIMAX model.\n results = train_sarima_model(y_train=y_train, order=opt_order, seasonal_order=opt_seasonal_order, \n plot_diagnostics=False,\n trend='c', enforce_stationarity=False, enforce_invertibility=False)\n \n # 5. Test the model (optional).\n if test_model:\n test_sarima_model(y=y, y_test=y_test, results=results, dynamic=True)\n \n # 6. Make the forecast with the model and return the table containing the results.\n table = forecast_temperature(y=y, results=results, end_date=forecast_end_date)\n return table","def time_series_train_test_split_y(df, test_date_start):\n test_date_cutoff = datetime.strptime(test_date_start, '%Y-%m-%d')\n train_date_cutoff = test_date_cutoff - timedelta(1)\n \n y = df['AverageTemperature']\n y_train = y.loc[:train_date_cutoff]\n y_test = y.loc[test_date_cutoff:]\n \n return y, y_train, y_test","def random_forest_test_Data(strat_test_set):\n logging.info(\"Random forest.....\")\n X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n y_test = strat_test_set[\"median_house_value\"].copy()\n X_test_num = X_test.drop(\"ocean_proximity\", axis=1)\n imputer = SimpleImputer(strategy=\"median\")\n imputer.fit(X_test_num)\n X_test_prepared = imputer.transform(X_test_num)\n X_test_prepared = pd.DataFrame(\n X_test_prepared, columns=X_test_num.columns, index=X_test.index\n )\n X_test_prepared = feature_eng2(X_test_prepared, X_test)\n return X_test_prepared, y_test"],"string":"[\n \"def arimax_by_month_train_test(model_data,future_data,months_ahead):\\n transformed_df = arima_by_zip_data_transform(model_data)\\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\\n\\n y_train = transformed_df\\n\\n\\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]\\n\\n now = datetime.datetime.now()\\n this_month = pd.to_datetime(str(now.year)+ '-'+ str(now.month) + '-'+str(1))\\n y_test=y_test[(y_test.Month_Year>this_month)&(y_test.Month_Year<(this_month+pd.offsets.MonthBegin(months_ahead+1)))].sort_values('Month_Year')\\n months_list = np.asarray(y_test.Month_Year)\\n\\n y_test = y_test.set_index(['Month_Year'],inplace=False)\\n\\n\\n for i,row in y_train.Eviction_Notice.iteritems():\\n if row>(y_train.Eviction_Notice.mean()+ y_train.Eviction_Notice.std()*3):\\n y_train.Eviction_Notice.iloc[i]=y_train.Eviction_Notice.mean()\\n\\n y_train = y_train[['Month_Year','Eviction_Notice','CASANF0URN']].set_index(['Month_Year'],inplace=False)\\n\\n return y_train, y_test,months_list\",\n \"def __init__(self):\\r\\n \\r\\n #self.max_stocks = 100\\r\\n self.max_stocks = 200\\r\\n \\\"\\\"\\\" cv_factor determines what portion of stocks to put in cross validation set and what portion\\r\\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\\r\\n set. cv_factor = 3 means every third stock goes into cross validation set \\\"\\\"\\\"\\r\\n self.cv_factor = 2 \\r\\n \\\"\\\"\\\" future_day is how many training days in the future we train for. Setting future_day = 25\\r\\n means we are measuring how the stock does 25 days out \\\"\\\"\\\"\\r\\n self.future_day = 25\\r\\n \\\"\\\"\\\" The train_dates are the dates for training and cross validation\\\"\\\"\\\"\\r\\n self.train_dates = []\\r\\n first_train_date = dateutl.days_since_1900('2001-01-01')\\r\\n num_train_dates = 10\\r\\n train_date_increment = 60\\r\\n self.train_dates.append(first_train_date)\\r\\n for iday in range(1,num_train_dates):\\r\\n last_train_date = self.train_dates[iday-1]\\r\\n self.train_dates.append(last_train_date + train_date_increment)\\r\\n \\\"\\\"\\\"self.train_dates[1] -= 1 \\\"\\\"\\\"\\r\\n \\r\\n \\\"\\\"\\\" test_dates are the dates we are using for testing \\\"\\\"\\\"\\r\\n self.test_dates = []\\r\\n first_test_date = dateutl.days_since_1900('2010-01-01')\\r\\n num_test_dates = 10\\r\\n test_date_increment = 60\\r\\n self.test_dates.append(first_test_date) \\r\\n for iday in range(1,num_test_dates):\\r\\n last_test_date = self.test_dates[iday-1]\\r\\n self.test_dates.append(last_test_date + test_date_increment)\\r\\n \\\"\\\"\\\"self.test_dates[1] -= 1\\r\\n self.test_dates[3] += 1\\r\\n self.test_dates[4] += 3\\r\\n self.test_dates[5] += 4\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"train_history_days and train_increment set how many historical days we use to\\r\\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\\r\\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\\r\\n as input features \\\"\\\"\\\"\\r\\n self.train_days = 21\\r\\n self.train_increment = 5\\r\\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\\\\r\\n 'cmo','mfi','natr','roc','stoch','uo']\\r\\n \\\"\\\"\\\" output is just a boolean about calling the output function to write out \\r\\n appropriate X and y matricies. The default is False meaning do not write out\\r\\n matricies \\\"\\\"\\\"\\r\\n self.output = False\",\n \"def get_pred_score(file_name, kpi, start_train, end_train, periods=365):\\n # set names for columns in order\\n names = ['date', 'handle_time', 'handle_time_forecast',\\n 'volume', 'volume_forecast']\\n df = pd.read_csv('./data/'+file_name, names=names, header=1, index_col=0)\\n\\n df['date'] = pd.to_datetime(df['date'])\\n df.index = df['date']\\n\\n # create column for aht\\n df['aht'] = df['handle_time']/df['volume']\\n df['aht_forecast'] = df['handle_time_forecast']/df['volume_forecast']\\n\\n # df = df[(df['aht'] > 200) & (df['aht'] < 1500)]\\n\\n df1 = df.copy()\\n\\n # create business unit name\\n bu = file_name[:6]\\n\\n # # remove outliers if it helps the model\\n # df = remove_outliers(df, bu)\\n\\n # remove holidays\\n df = df[~df.index.isin(h.iloc[:,0].tolist())]\\n\\n # remove weekends\\n df = df[~df.index.weekday.isin([5,6])]\\n\\n # # plot data in a notebook\\n # plot data\\n # plot_time_vol(df)\\n\\n # create training\\n df = create_training_data(df, kpi, start_train, end_train)\\n\\n \\n # display(df.head())\\n m,future = create_forecast(df, periods=int(periods))\\n forecast = m.predict(future)\\n\\n # # plot forecast in a notebook\\n # plot_forecast(forecast)\\n\\n # set forecast beginning and end date\\n f = forecast.copy()\\n end_test = df1.index.max()\\n f = f[(forecast['ds'] > end_train) & (f['ds'] <= end_test)]\\n\\n # create validation dataset\\n df2 = df1.copy()\\n df2['ds'] = pd.to_datetime(df2.index.date) \\n\\n # set start date of validation data equal to June 1st, 2020 or any other date\\n df2 = df2[df2['ds'] > end_train]\\n\\n # remove weekends and holidays from data\\n df2 = df2[~df2.index.isin(h.iloc[:,0].tolist())]\\n df2 = df2[~df2.index.weekday.isin([5,6])]\\n os.makedirs('./preds/'+kpi, exist_ok=True)\\n\\n future_forecast=forecast[['ds', 'yhat_lower', 'yhat', 'yhat_upper']][forecast['ds']>datetime.datetime.now()]\\n\\n f[['ds', 'yhat_lower', 'yhat', 'yhat_upper']].to_csv('./preds/'+kpi+'/'+bu+'.csv')\\n forecast.to_csv('./preds/analysis/'+kpi+'_'+bu+'.csv')\\n\\n # Validate test data, it must match for scoring\\n mae = evaluate_model(f,df2, kpi, metric='mae')\\n\\n mae.update({'kpi': kpi, 'start_train':start_train, 'end_train': end_train, 'end_test': end_test}) \\n\\n curr = pd.read_csv('scores/'+kpi+'_score.csv',index_col=0)\\n new = pd.DataFrame(mae, index=[bu])\\n\\n new.to_csv('./scores/current/'+bu+'_'+kpi+'.csv')\\n \\n if bu not in curr.index:\\n curr = pd.concat([curr, new], 0)\\n # create log everytime a score is superceded\\n elif new.loc[bu,'prophet'] < curr.loc[bu,'prophet']:\\n data = pd.concat([curr.loc[[bu],:],new.loc[[bu],:]],0)\\n data.to_csv('./scores/logs/'+bu+'_'+kpi+'_'+datetime.datetime.now()\\n .strftime(\\\"%b %d %Y %H:%M:%S\\\").replace(' ', '_')+'.csv')\\n curr.update(new)\\n curr.to_csv('./scores/'+kpi+'_score.csv')\\n\\n return 'error with metric'\\n print('\\\\ntraining',file_name, 'on', kpi, 'start train:',start_train, \\n 'end train:', end_train, 'end_test:', )\",\n \"def dataDescription(train):\\n print(train.describe())\\n # No NaNs\\n print(train.head())\\n # 5 columns: store number, department number, date, weekly sales, isHoliday\\n\\n # Lets view the weekly sales, grouped by date\\n fig, ax = plt.subplots()\\n salesByDate = train.groupby('Date').mean().Weekly_Sales\\n salesByDate.plot()\\n plt.show()\\n fig, ax = plt.subplots()\\n autocorrelation_plot(salesByDate)\\n plt.show()\\n # Lets plot all store's sales by date on the same graph\\n salesByStore = train.groupby(by=['Store', 'Date']).mean().Weekly_Sales\\n salesByStore.unstack(level=0).plot()\\n # Lets plot all department's sales by date on the same graph\\n salesByStore = train.groupby(by=['Dept', 'Date']).mean().Weekly_Sales\\n salesByStore.unstack(level=0).plot()\\n # It looks like all stores have peaks at the same times, however just a\\n # single department. Could this be the holiday department?\\n fig, ax = plt.subplots()\\n holidayDates = train.groupby('Date').mean().IsHoliday\\n holidayDates.plot()\\n plt.show()\\n # Very few holiday days...\\n # What are the average sales on holidays vs not holidays\\n holidaySales = train[train['IsHoliday'] == True].Weekly_Sales.mean()\\n nonHolidaySales = train[train['IsHoliday'] == False].Weekly_Sales.mean()\\n print(\\\"The mean weekly sales on holidays is {:.2f} \\\".format(holidaySales) +\\n \\\"and then {:.2f} for non-holidays.\\\".format(nonHolidaySales))\\n\\n # Lets looks at how complete the data is. Create a series with all possible\\n # dates in. Compare with each store and department.\\n trainDates = pd.to_datetime(train.Date)\\n trainDates = pd.DatetimeIndex(trainDates.unique())\\n# First confirm there are no missing dates in the whole range.\\n# trainDatesTest = pd.date_range(trainDates.min(),\\n# trainDates.max(), freq='7D')\\n# (trainDates == trainDatesTest).all()\\n stores = np.unique(train['Store'])\\n depts = np.unique(train['Dept'])\\n missingDates = defaultdict(int)\\n for store in stores:\\n for dept in depts:\\n trainThis = train[train['Store'] == store]\\n trainThis = trainThis[trainThis['Dept'] == dept]\\n missing = len(trainDates) - len(pd.DatetimeIndex(trainThis.Date))\\n missingDates[missing] += 1\\n\\n # The majority miss nothing. 314 stores don't have certain departments\\n # - as can be expected!\\n fig, ax = plt.subplots()\\n ax.bar(list(missingDates.keys()), missingDates.values())\",\n \"def form_data(stocks, init_param):\\r\\n \\r\\n rs = stocks[1].rsi\\r\\n ts = stocks[1].tsi\\r\\n a = 1\\r\\n \\r\\n \\r\\n for date in init_param.train_dates:\\r\\n try:\\r\\n training_data\\r\\n except NameError:\\r\\n training_data = LearningData()\\r\\n training_data.construct(stocks, date, init_param.future_day, init_param.features)\\r\\n else:\\r\\n training_data.append(stocks, date, init_param.future_day, init_param.features)\\r\\n \\r\\n for date in init_param.test_dates:\\r\\n try:\\r\\n test_data\\r\\n except NameError:\\r\\n test_data = LearningData()\\r\\n test_data.construct(stocks, date, init_param.future_day, init_param.features)\\r\\n else:\\r\\n test_data.append(stocks, date, init_param.future_day, init_param.features)\\r\\n \\r\\n #reference_date = dateutl.days_since_1900('1991-01-01')\\r\\n #test_data.construct(stocks,[reference_date, day_history, init_param.future_day])\\r\\n \\r\\n return training_data, test_data\",\n \"def sts_predict_canteen_values(full_df, prediction_df, future=True):\\n\\n df = full_df.copy()\\n df = df.filter([\\\"Canteen\\\"])\\n test_period = prediction_df.shape[0]\\n\\n # The days between last row in dataset and today needs to be calculated\\n if future is True:\\n # Finding days between end date of dataset and today\\n date_today = datetime.now()\\n end_date = df.index[-1]\\n future_test_period = test_period + (date_today - end_date).days - 1\\n\\n # The whole dataframe is now the train set\\n train = df\\n # Creating a new dataframe containing all dates between end date and today + future days from prediction_df\\n date_df = pd.DataFrame()\\n date_df[\\\"date\\\"] = pd.date_range(\\n (end_date + timedelta(1)).date(),\\n periods=future_test_period,\\n freq=\\\"D\\\",\\n )\\n date_df.index = pd.to_datetime(date_df.pop(\\\"date\\\"))\\n # Setting the new df as test set\\n test = date_df\\n else:\\n # If future = False, the test period is the last part of the full_df, train is everything up until this point\\n train = df.iloc[:-test_period]\\n test = df.iloc[-test_period:]\\n\\n # Make prediction\\n _, predictions = prediction(train, test)\\n return predictions.iloc[-test_period:]\",\n \"def run_trainig_and_testing(company = \\\"FB\\\", training_window = 60,\\n trainig_period_y=8, testing_period_y=1,\\n prediction_day=dt.datetime.now() ):\\n # Load data for model\\n training_data, testing_data = fetch_data(company, trainig_period_y, testing_period_y, prediction_day)\\n # prepare data\\n # scaler is object which will scale whole series values to defined feature_range interval\\n scaler = MinMaxScaler(feature_range=(0,1))\\n # we are insterested only in the closing values\\n # optionaly we can use AdjClose -> adjusted for splits\\n scaled_data = scaler.fit_transform(training_data['Close'].values.reshape(-1,1))\\n\\n # x is our learning data\\n x_train, y_train = create_training_tensors(training_window, scaled_data)\\n\\n # build the model\\n model = create_model((x_train.shape[1], 1))\\n model.fit(x_train, y_train, epochs=5, batch_size=32)\\n\\n # for future we can save the model here and load for future\\n\\n \\\"\\\"\\\"\\n TESTING MODEL ACCURACY\\n \\\"\\\"\\\"\\n\\n # load test data\\n\\n tst_start_dt = dt.datetime(2020,1,1)\\n tst_end_dt = dt.datetime(2021,1,1)\\n\\n\\n actual_prices = testing_data['Close'].values\\n total_dataset = pd.concat((training_data['Close'], testing_data['Close']), axis=0)\\n\\n model_inputs = total_dataset[len(total_dataset) - len(testing_data) - training_window:].values\\n model_inputs = model_inputs.reshape(-1,1)\\n model_inputs = scaler.fit_transform(model_inputs)\\n\\n # make prediction on test data\\n\\n x_test, y_test = create_training_tensors(training_window, model_inputs)\\n\\n predicted_prices = model.predict(x_test)\\n # predicted prices are scaled to (0-1) so we need to rescale them to actual prices\\n predicted_prices = scaler.inverse_transform(predicted_prices)\\n\\n plot_actual_vs_predicted(predicted_prices,actual_prices, company,\\n x_labels=[date.strftime(\\\"%d-%m-%y\\\") for date in testing_data.index] )\\n\\n # predict next day\\n real_data = [model_inputs[len(model_inputs) + 1 - training_window: len(model_inputs)+1, 0]]\\n real_data = np.array(real_data)\\n real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))\\n print(scaler.inverse_transform(real_data[-1]))\\n\\n prediction = model.predict(real_data)\\n prediction = scaler.inverse_transform(prediction)\\n print(f\\\"Next day value is: {prediction}\\\")\\n\\n print(\\\"===================================\\\")\\n print(model.summary())\\n print(\\\"===================================\\\")\\n print(\\\"Evaluating model\\\")\\n result = model.evaluate(x_test, y_test)\\n print(dict(zip(model.metrics_names, result)))\\n\\n\\n # TODO\\n # experiment with next days predictions\",\n \"def _forecast_train(stan_fit, p):\\n \\n train_n = stan_fit.data['N']\\n t = np.arange(train_n) / train_n\\n train_changepoints = stan_fit.data['s']\\n \\n A = (t[:, None] > train_changepoints) * 1. # t x s\\n \\n X = fourier_series(t, p=p, fourier_order=stan_fit.data['n_fourier']) \\n \\n samples = stan_fit.extract(permuted=True)\\n \\n samples_n = len(samples['lp__'])\\n \\n y_hat = np.zeros(shape=(samples_n, len(t)))\\n mu_trend = np.zeros(shape=(samples_n, len(t)))\\n mu_s = np.zeros(shape=(samples_n, len(t)))\\n \\n for iteration in range(samples_n):\\n \\n k = samples['k'][iteration]\\n m = samples['m'][iteration]\\n deltas = samples['delta'][iteration]\\n beta = samples['beta'][iteration]\\n sigma = samples['sigma'][iteration]\\n \\n gamma = -train_changepoints * deltas;\\n mu_trend[iteration,:] = k + np.matmul(A, deltas) * t + (m + np.matmul(A, gamma))\\n mu_s[iteration,:] = np.matmul(X, beta)\\n \\n mu = mu_trend[iteration] + mu_s[iteration]\\n \\n y_hat[iteration,:] = np.random.normal(mu, sigma)\\n \\n return y_hat, mu_trend, mu_s, samples, samples_n, train_n, train_changepoints\",\n \"def predic_subscription():\\r\\n \\r\\n df_test=pd.read_csv(request.files.get(\\\"file\\\"),sep=';')\\r\\n def data_cleaning(bank_loan):\\r\\n categorical_value=[feature for feature in bank_loan if bank_loan[feature].dtype =='O']\\r\\n for feature in categorical_value:\\r\\n temp=bank_loan[feature].unique()\\r\\n temp_df={i:j for j,i in enumerate(temp,0)}\\r\\n bank_loan[feature]=bank_loan[feature].map(temp_df)\\r\\n return bank_loan\\r\\n df_test=data_cleaning(df_test)\\r\\n \\r\\n scale=StandardScaler()\\r\\n scaled_data=scale.fit_transform(df_test)\\r\\n prediction=subscribe_pre.predict(scaled_data)\\r\\n return str(list(prediction))\",\n \"def prophet_train(data): \\n \\n model = Prophet(interval_width=.95, changepoint_prior_scale=6, yearly_seasonality=True, \\n seasonality_prior_scale=1, weekly_seasonality=False, daily_seasonality=False)\\n model.add_seasonality(name='monthly', period=120, fourier_order=4)\\n \\n model.fit(data)\\n \\n future_dates = model.make_future_dataframe(periods=12, freq='MS')\\n forecast = model.predict(future_dates)\\n forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\\n results = forecast[forecast['ds']>='2018-01-01']\\n return(results)\",\n \"def train(self, training_data):\\n # load and preprocess\\n super(Forecast, self).train(training_data)\\n # remove NaNs\\n self.historical_data = self.historical_data.loc[~self.historical_data.isnull().any(axis=1)]\\n # project timestamps into vector space\\n if self.timestamp_column in self.historical_data.columns:\\n self.use_timestamp = True\\n ts = self.historical_data.set_index(self.timestamp_column).index\\n self.epoch = min(ts)\\n self.epoch_span = float((max(ts) - self.epoch).total_seconds())\\n time_features = make_time_features(ts,\\n index=self.historical_data.index,\\n epoch=self.epoch,\\n epoch_span=self.epoch_span)\\n self.historical_data = pd.concat([self.historical_data, time_features], axis=1)\\n self.historical_data.drop(self.timestamp_column, axis=1, inplace=True)\\n # leave all other variables independent\\n self.independent_variables = [name for name in self.historical_data.columns\\n if name not in self.dependent_variables]\\n\\n self.model.fit(self.historical_data[self.independent_variables],\\n self.historical_data[self.dependent_variables])\\n\\n # release historical data to save on memory\\n # note that python garbage collection is not instantaneous\\n LOG.warn(\\\"Releasing building load forecast training data. The agent will not be able to retrain on this data\\\")\\n self.historical_data = None\",\n \"def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\\n df = self.get_prep_data()\\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\\n print(f'----train----')\\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----validation----')\\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----test----')\\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n self.set_train(train)\\n self.set_validation(val)\\n self.set_test(test)\\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n train_y = train['target']\\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n val_y = val['target']\\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n test_y = test['target']\\n self.set_train_X(train_X)\\n self.set_train_y(train_y)\\n self.set_val_X(val_X)\\n self.set_val_y(val_y)\\n self.set_test_X(test_X)\\n self.set_test_y(test_y)\",\n \"def _stratify_training_data(\\n self,\\n train_ds: xr.Dataset,\\n target_variable: str,\\n pred_months: int,\\n expected_length: Optional[int],\\n ) -> None:\\n\\n min_date = self._get_datetime(train_ds.time.values.min())\\n max_date = self._get_datetime(train_ds.time.values.max())\\n\\n cur_pred_year, cur_pred_month = max_date.year, max_date.month\\n\\n # for every month-year create & save the x, y datasets for training\\n cur_min_date = max_date\\n while cur_min_date >= min_date:\\n # each iteration count down one month (02 -> 01 -> 12 ...)\\n arrays, cur_min_date = self._stratify_xy(\\n ds=train_ds,\\n year=cur_pred_year,\\n target_variable=target_variable,\\n target_month=cur_pred_month,\\n pred_months=pred_months,\\n expected_length=expected_length,\\n )\\n if arrays is not None:\\n self._save(\\n arrays,\\n year=cur_pred_year,\\n month=cur_pred_month,\\n dataset_type=\\\"train\\\",\\n )\\n cur_pred_year, cur_pred_month = cur_min_date.year, cur_min_date.month\",\n \"def train_test_dataset(self, train_rate=0.8):\\n point_date = int(len(self.y) * train_rate)\\n y_to_train = self.y[:point_date]\\n y_to_val = self.y[point_date:]\\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\\n date_val = self.y.index[point_date]\\n return y_to_train, y_to_val, predict_date, date_val\",\n \"def set_training_data(self):\\n # Optional training data period\\n # TODO: add training data period feature to training data query\\n if not self.training_period == None:\\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\\\"%Y-%m-%d\\\")\\n print(f\\\"Training data start date: {training_period_date}\\\")\\n # Extract queried data from Athena\\n #athena = athena_connect.Athena()\\n #features_df = athena.pandas_read_athena(self.training_data_sql)\\n with open('feature_sql.txt', 'w') as f:\\n print(self.training_data_sql, file=f) \\n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\\n features_df.fillna(0, inplace=True)\\n print(features_df.shape)\\n features_df = features_df[max(self.feature_minutes_list):]\\n print(features_df.shape)\\n # Remove infinity string\\n features_df.replace({'Infinity': 0}, inplace=True)\\n # Convert all object fields to numeric except date fields\\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\\n self.training_df = features_df\",\n \"def application_train_test(num_rows=None, nan_as_category=False):\\n df = pd.read_csv('application_train.csv', nrows=num_rows)\\n test_df = pd.read_csv('application_test.csv', nrows=num_rows)\\n print(\\\"Train samples: {}, test samples: {}\\\".format(len(df), len(test_df)))\\n print(\\\"Train df shape: {}, test df shape: {}\\\".format(df.shape,\\n test_df.shape))\\n df = df.append(test_df).reset_index()\\n print(\\\"df shape: {} - After reset index\\\".format(df.shape))\\n # Optional: Remove 4 applications with XNA CODE_GENDER (train set)\\n df = df[df['CODE_GENDER'] != 'XNA']\\n\\n # Categorical features with Binary encode (0 or 1; two categories)\\n for bin_feature in ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']:\\n df[bin_feature], uniques = pd.factorize(df[bin_feature])\\n # print(uniques)\\n # Categorical features with One-Hot encode\\n print(\\\"df shape: {} - After binary encode 3 columns\\\".format(df.shape))\\n\\n df, cat_cols = one_hot_encoder(df, nan_as_category)\\n # print(cat_cols)\\n print(\\\"df shape: {} - After one-hot encoding other columns\\\".format(df.shape))\\n\\n # NaN values for DAYS_EMPLOYED: 365.243 -> nan\\n df['DAYS_EMPLOYED'].replace(365243, np.nan, inplace=True)\\n # 5 new features (percentages)\\n df['DAYS_EMPLOYED_PERC'] = df['DAYS_EMPLOYED'] / df['DAYS_BIRTH']\\n df['INCOME_CREDIT_PERC'] = df['AMT_INCOME_TOTAL'] / df['AMT_CREDIT']\\n df['INCOME_PER_PERSON'] = df['AMT_INCOME_TOTAL'] / df['CNT_FAM_MEMBERS']\\n df['ANNUITY_INCOME_PERC'] = df['AMT_ANNUITY'] / df['AMT_INCOME_TOTAL']\\n df['PAYMENT_RATE'] = df['AMT_ANNUITY'] / df['AMT_CREDIT']\\n print(\\\"df shape: {} - After adding 5 manual features\\\".format(df.shape))\\n del test_df\\n gc.collect()\\n return df\",\n \"def foo(username):\\n year = 2018\\n cal = Calendar(-1)\\n training_list = []\\n path = users_folder_file_path + username\\n\\n weekdays = ['Sunday', 'Monday', 'Tuesday',\\n 'Wednesday', 'Thursday', 'Friday', 'Saturday']\\n mydate = date.today()\\n list_month = []\\n for i in range(12):\\n list_month.append(mydate.strftime(\\\"%B\\\"))\\n mydate = mydate + relativedelta(months=+1)\\n\\n logged_training = []\\n\\n with open(path + '/logged_training.csv') as csvfile:\\n readCSV = csv.reader(csvfile, delimiter=',')\\n next(readCSV, None)\\n year_t = 0\\n month_t = 0\\n day_t = 0\\n count = 0\\n for row in readCSV:\\n if row[0] != '' and row[1] != '':\\n if year_t == int(row[0].split('-')[0]) and \\\\\\n month_t == int(row[0].split('-')[1]) and \\\\\\n day_t == int(row[0].split('-')[2]):\\n\\n logged_training[count-1][0] = \\\\\\n logged_training[count-1][0] + float(row[1])\\n\\n else:\\n logged_training.append(\\n [float(row[1]), int(row[0].split('-')[0]),\\n int(row[0].split('-')[1]), int(row[0].split('-')[2])])\\n\\n year_t = int(row[0].split('-')[0])\\n month_t = int(row[0].split('-')[1])\\n day_t = int(row[0].split('-')[2])\\n count += 1\\n\\n if len(logged_training) > 0:\\n dates = [training[1:] for training in logged_training]\\n dates = [datetime.strptime('-'.join(str(x) for x in date1),\\n '%Y-%m-%d') for date1 in dates]\\n last_day = max(dates)\\n else:\\n last_day = datetime.now().replace(hour=0,\\n minute=0,\\n second=0,\\n microsecond=0)\\n\\n with open(path + '/planned_training.csv') as csvfile:\\n readCSV = csv.reader(csvfile, delimiter=',')\\n next(readCSV, None)\\n for row in readCSV:\\n if datetime.strptime(row[2], '%Y-%m-%d') > last_day:\\n training_list.append([row[0],\\n int(row[2].split('-')[0]),\\n int(row[2].split('-')[1]),\\n int(row[2].split('-')[2])])\\n try:\\n if not year:\\n year = date.today().year\\n cal_list = [\\n cal.monthdatescalendar(year, i+1)\\n for i in range(12)\\n if i >= date.today().month - 1]\\n cal_list2 = [\\n cal.monthdatescalendar(year, i + 1)\\n for i in range(12)\\n if i < date.today().month - 1]\\n\\n cal_list3 = cal_list + cal_list2\\n\\n except IOError:\\n abort(404)\\n else:\\n race_miles = training_list[-1][0]\\n if username == 'alex' or username == 'Diane':\\n generate_mileage_line(username)\\n\\n return render_template('new_alex.html',\\n weekdays=weekdays,\\n list_month=list_month,\\n username=username,\\n year=year,\\n cal=cal_list3,\\n training_list=training_list[:-1],\\n logged_training=logged_training,\\n race_date=training_list[-1],\\n race_miles=race_miles)\\n else:\\n generate_mileage_line(username)\\n return render_template('new_.html',\\n weekdays=weekdays,\\n list_month=list_month,\\n username=username,\\n year=year,\\n cal=cal_list3,\\n training_list=training_list[:-1],\\n logged_training=logged_training,\\n race_date=training_list[-1],\\n race_miles=race_miles)\\n abort(404)\",\n \"def naive_forecasting(x_test, y_test):\\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred\",\n \"def forecast(dataloader):\\n model = NBeats.load_from_checkpoint(os.path.join(DIR, 'model/nbeats.ckpt'))\\n raw_predictions = model.predict(dataloader, mode=\\\"raw\\\", return_x=False)\\n y = np.array(raw_predictions['prediction'].reshape(-1, 1))\\n df = pd.DataFrame(data=y, columns=['prediction'])\\n df.to_csv(os.path.join(DIR, 'data/nbeats_forecast.csv'), index=False)\\n return y\",\n \"def load_data(\\n ticker:str,T_test:int,file_name_monthly:str,\\n file_name_quarterly:str,y_col='Lucro/Prejuízo do Período'\\n )-> Tuple[np.array,np.array,np.array,np.array]:\\n\\n df_quarterly = read_ticker(ticker)\\n\\n if file_name_monthly is None and file_name_quarterly is None:\\n # if no monthly data, set df_monthly as empty df\\n df_monthly = pd.DataFrame() \\n df_all = df_quarterly\\n elif file_name_quarterly is None:\\n df_monthly = read_ipea(file_name_monthly,time_freq='monthly')\\n # TODO remove this\\n df_monthly = df_monthly.iloc[:,:120]\\n df_all = pd.concat([df_monthly,df_quarterly],join='inner',axis=1)\\n else:\\n df_monthly = read_ipea(file_name_monthly,time_freq='monthly')\\n # TODO remove this\\n df_monthly = df_monthly.iloc[:,:120]\\n if file_name_quarterly != None:\\n ipea_quarterly = read_ipea(file_name_quarterly,time_freq='quarterly')\\n # TODO remove this\\n ipea_quarterly = ipea_quarterly.iloc[:,:10]\\n df_quarterly = pd.concat([ipea_quarterly,df_quarterly],join='inner',axis=1)\\n df_all = pd.concat([df_monthly,df_quarterly],join='inner',axis=1)\\n \\n\\n y_all = df_all.loc[:,y_col].copy()\\n X_all = df_all.drop(columns=y_col)\\n\\n X_train = X_all.iloc[:-T_test,:]\\n X_test = X_all.iloc[-T_test:,:]\\n\\n y_train = y_all[:-T_test]\\n y_test = y_all[-T_test:]\\n\\n number_variables = {\\n \\\"quarterly\\\" : df_quarterly.shape[1] - 1, #removing the target column\\n \\\"monthly\\\" : df_monthly.shape[1]//3,\\n \\\"daily\\\" : 0\\n }\\n\\n return X_train.values, X_test.values, y_train.values, y_test.values, number_variables\",\n \"def Data_formatting(dz, y, timestep, TZ):\\r\\n \\r\\n #reindex data by datetime\\r\\n dz.index = pd.to_datetime(dz['DATE'])\\r\\n \\r\\n #Isolate temperature data\\r\\n dz = dz[['TMP']]\\r\\n \\r\\n #Delete data mistake\\r\\n dz = dz[dz['TMP'] != \\\"+9999,9\\\"]\\r\\n \\r\\n #Format data\\r\\n dz['TMP'] = dz['TMP'].str.replace(',', '.')\\r\\n dz['TMP'] = pd.to_numeric(dz['TMP'], errors='coerce')\\r\\n \\r\\n #Delete NaN data\\r\\n dz = dz.dropna()\\r\\n \\r\\n #Convert temperature\\r\\n dz['TMP'] = dz['TMP'] / 10\\r\\n dz['TMP'] = dz['TMP'] * (9/5) + 32\\r\\n \\r\\n #Convert datetime index utc to specified timezone\\r\\n dz.index = dz.index.tz_localize(pytz.utc).tz_convert(pytz.timezone(str(TZ))).strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n dz.index = pd.to_datetime(dz.index)\\r\\n \\r\\n #Resample data by average on timestep\\r\\n dz = dz.resample(rule = str(timestep)).mean()\\r\\n \\r\\n #Define the first date of the instance year\\r\\n fdy = dt.datetime.strptime(\\\"01/01/\\\"+str(y)+\\\" 00:00\\\", '%m/%d/%Y %H:%M')\\r\\n #Convert first date of the year to timezone\\r\\n fdy = Date_calibration(fdy, 0, TZ)\\r\\n \\r\\n #If we collect the date from the current year we limit the collect from 2days before now\\r\\n \\r\\n #Define the datetime 2 days before now\\r\\n dbeyest = dt.datetime.now(tz=pytz.timezone(str(TZ))) - dt.timedelta(days=2)\\r\\n \\r\\n #If the instance year is the current year\\r\\n if(y == dbeyest.year):\\r\\n #We limit the collect 2 days before now\\r\\n ldy = dt.datetime.strptime(str(dbeyest.month)+\\\"/\\\"+str(dbeyest.day)+\\\"/\\\"+str(y)+\\\" 23:59\\\", '%m/%d/%Y %H:%M')\\r\\n else:\\r\\n #Else, we collect the full year\\r\\n ldy = dt.datetime.strptime(\\\"12/31/\\\"+str(y)+\\\" 23:59\\\", '%m/%d/%Y %H:%M')\\r\\n \\r\\n #Convert the last date of the year to specified timezone\\r\\n ldy = Date_calibration(ldy, 0, TZ)\\r\\n \\r\\n #Set up dataframe for the specified datetime index and timestep\\r\\n ph = pd.DataFrame(index=pd.DatetimeIndex(start=fdy, end=ldy, freq=str(timestep)))\\r\\n \\r\\n #Past original data temperature in the time fitted dataframe (with the datetimeindex position)\\r\\n ph['TMP'] = dz['TMP']\\r\\n \\r\\n #Calculate the quality of the instance data\\r\\n nb_nan = ph['TMP'].isnull().sum()\\r\\n qual = (1 - (nb_nan) / len(ph)) * 100\\r\\n \\r\\n return dz, qual\",\n \"def forecast(\\n input_values: np.ndarray, future_dates: List, model: Sequential, scaler\\n) -> pd.DataFrame:\\n if scaler:\\n future_values = scaler.inverse_transform(\\n model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\\n )\\n else:\\n future_values = model.predict(input_values.reshape(1, -1, 1)).reshape(-1, 1)\\n\\n df_future = pd.DataFrame(\\n future_values, index=future_dates, columns=[\\\"Predicted Price\\\"]\\n )\\n return df_future\",\n \"def _transform_training_set(self, gdax_rates):\\n rates = [gdax_rate.closing_price for gdax_rate in gdax_rates]\\n timestamps = [gdax_rate.end_time.timestamp() for gdax_rate in gdax_rates]\\n\\n x_train = np.reshape(timestamps, (len(timestamps), 1))\\n y_train = rates\\n\\n return x_train, y_train\",\n \"def read_dataset():\\n\\n\\tonehotencoder = OneHotEncoder(categories=\\\"auto\\\", sparse=False)\\n\\tscaler = StandardScaler(with_mean=False)\\n\\tcwd = os.getcwd()\\n\\tfilename = cwd + '/default of credit card clients.xls'\\n\\tnanDict = {}\\n\\t#read file and create the dataframe\\n\\tdf = pd.read_excel(filename, header=1, skiprows=0,\\n\\t\\t\\t\\t\\t\\tindex_col=0, na_values=nanDict)\\n\\n\\tdf.rename(index=str,\\n\\t\\t\\t\\tcolumns={\\\"default payment next month\\\": \\\"defaultPaymentNextMonth\\\"},\\n\\t\\t\\t\\tinplace=True)\\n\\n\\tdf = df.drop(df[(df.BILL_AMT1 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.BILL_AMT2 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.BILL_AMT3 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.BILL_AMT4 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.BILL_AMT5 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.BILL_AMT6 == 0)].index)\\n\\tdf = df.drop(df[(df.PAY_AMT1 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.PAY_AMT2 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.PAY_AMT3 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.PAY_AMT4 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.PAY_AMT5 == 0)&\\n\\t\\t\\t\\t\\t\\t(df.PAY_AMT6 == 0)].index)\\n\\n\\t# Creating matrix X and target variable y\\n\\tX = df.loc[:, df.columns != 'defaultPaymentNextMonth'].values\\n\\ty = df.loc[:, df.columns == 'defaultPaymentNextMonth'].values\\n\\n\\tonehotencoder = OneHotEncoder(categories ='auto', sparse =False)\\n\\tscalar = StandardScaler(with_mean = False)\\n\\n\\t#make one hots\\n\\tX = ColumnTransformer(\\n\\t\\t\\t\\t[(\\\"\\\", onehotencoder, [1,2,3,5,6,7,8,9]),],\\n\\t\\t\\t\\tremainder=\\\"passthrough\\\"\\n\\t\\t\\t).fit_transform(X)\\n\\n\\tX = scalar.fit_transform(X)\\n\\n\\treturn X, y\",\n \"def load_data():\\n df = pd.read_csv(\\\"../../Data/breast_cancer_data/data.csv\\\")\\n\\n cols = df.columns\\n X = df[cols[2:-1]].to_numpy()\\n y = df[cols[1]].to_numpy()\\n y = (y=='M').astype(np.int) * 2 - 1\\n\\n train_X = X[:-150]\\n train_y = y[:-150]\\n\\n test_X = X[-150:]\\n test_y = y[-150:]\\n\\n return train_X, train_y, test_X, test_y\",\n \"def predictions_0(data):\\n predictions=[]\\n for _, passenger in data.iterrows():\\n #Predict the survival of 'passenger'\\n predictions.append(0)\\n #Return our predictions\\n return pd.Series(predictions)\\n #make the predictions\\n predictions=predictions_0(data)\\n \\n '''\\n Question 1\\n Using the RMS Titanic data,how accurate would a prediction be that none of the passengers survived?\\n Hint:Run the code cell below to see the accuracy of this prediction.\\n '''\\n print accuracy_score(outcomes,predictions)\",\n \"def test_02_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\\n self.assertEqual(metric,logged_metric)\",\n \"def get_trend_pred(united_samples, look_back):\\n\\n features = united_samples[:, :1].astype(str)\\n labels = united_samples[:, -1:]\\n\\n # move all dates a day behind\\n delta = -1\\n generator = (change_date(date[0], delta_days=delta) for date in features)\\n new_dates = np.fromiter(generator, features.dtype)\\n\\n # selecting samples after April 2020 when the COVID-19 became global\\n BOOL_COND_ARRAY = [(int(date[0:4]) >= 2020 and int(date[5:7]) >= 4)\\n for date in new_dates]\\n new_dates = new_dates[BOOL_COND_ARRAY]\\n new_dates = new_dates.reshape(-1, 1)\\n\\n labels = labels[BOOL_COND_ARRAY]\\n\\n # converting date to numerical source:\\n generator = (dt.toordinal(dt.strptime(date[0], DATE_FORMAT)) for date in\\n new_dates)\\n numerical_dates = np.fromiter(generator, features.dtype)\\n numerical_dates = numerical_dates.reshape(-1, 1).astype(float)\\n\\n # change degree of polynomial features\\n poly_features = PolynomialFeatures(degree=4)\\n features_transformed = poly_features.fit_transform(numerical_dates)\\n\\n # model\\n linreg_model = LinearRegression()\\n linreg_model.fit(features_transformed, labels)\\n\\n # trend\\n trend_labels = linreg_model.predict(\\n poly_features.fit_transform(numerical_dates))\\n\\n ahead = trend_labels[-1:, 0]\\n behind = trend_labels[-look_back-1: -look_back, 0]\\n\\n if ahead > behind:\\n return 'upward'\\n elif ahead < behind:\\n return 'downward'\\n else:\\n return 'not_changed'\",\n \"def __init__(self, ts_df, time_format=\\\"%Y-%m-%d %H:%M:%S\\\", freq='D',\\n fill_method='ffill',\\n n_test=0, n_val=0,\\n hyper_params=None,\\n test='adf',\\n trend=None,\\n seasonal=False,\\n seasonal_periods=1,\\n **kwds):\\n self._ts_df_cols = ['ds', 'y']\\n\\n self.ts_df = ts_df\\n self.time_format = time_format\\n self.freq = freq\\n self.fill_method = fill_method.lower()\\n self.n_test = int(n_test)\\n self.n_val = int(n_val)\\n self.transform = None\\n self._boxcox_lmbda = None\\n\\n self._mode = ''\\n\\n self._train_dt = None\\n self._test_dt = None\\n self._val_dt = None\\n\\n self.model_fit = None\\n self.fittedvalues = None\\n self.residuals = None\\n self.rmse = 0\\n self._gs = tsa.GridSearchClass()\\n self.hyper_params = hyper_params\\n self.best_model = dict()\\n\\n \\\"\\\"\\\"\\n self.rmse_test = 0\\n self.rmse_val = 0\\n \\\"\\\"\\\"\\n\\n self.upper_whisker_res = None\\n self.lower_conf_int = None\\n self.upper_conf_int = None\\n\\n self.forecast = None\\n self.residuals_forecast = None\\n\\n self._res_decomp = None\\n self._arr_seasonal = None\\n self._arr_trend = None\\n self._arr_baseline = None\\n\\n self._test = test\\n self._trend = trend\\n if self._trend is not None:\\n self._trend = self._trend.lower()\\n self._seasonal = seasonal\\n if isinstance(self._seasonal, str):\\n self._seasonal = self._seasonal.lower()\\n self._seasonal_periods = seasonal_periods\\n\\n self._uvts_cls_logger = Logger('uvts_cls')\\n\\n UVariateTimeSeriesClass.assertions(self)\\n # work with ts_df\\n self.ts_df = self.ts_df.reset_index()\\n self.ts_df.columns = self._ts_df_cols\\n self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')\\n self.ts_df.set_index('ds', inplace=True)\\n self._uvts_cls_logger.info(\\n \\\"Received time series data of range: \\\" + str(min(self.ts_df.index)) + ' - ' + str(\\n max(self.ts_df.index)) + \\\" and shape: \\\" + str(self.ts_df.shape))\\n\\n if not isinstance(self.ts_df.index, pd.DatetimeIndex):\\n self._uvts_cls_logger.warning(\\\"Time conversion required...\\\")\\n self.ts_df = self.ts_df.reset_index()\\n try:\\n self.ts_df['ds'] = self.ts_df['ds'].apply(\\n lambda x: datetime.datetime.strptime(\\n str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],\\n self.time_format))\\n except ValueError as e:\\n self._uvts_cls_logger.warning(\\\"Zulu time conversion not successful: {}\\\".format(e))\\n self._uvts_cls_logger.warning(\\\"Will try without assuming zulu time...\\\")\\n try:\\n self.ts_df['ds'] = self.ts_df['ds'].apply(\\n lambda x: datetime.datetime.strptime(str(x), self.time_format))\\n except ValueError as e:\\n self._uvts_cls_logger.info(\\\"Time conversion not successful. Check your time_format: {}\\\".format(e))\\n sys.exit(\\\"STOP\\\")\\n else:\\n self._uvts_cls_logger.info(\\\"Time conversion successful!\\\")\\n else:\\n self._uvts_cls_logger.info(\\\"Time conversion successful!\\\")\\n # set index\\n self.ts_df.set_index('ds', inplace=True)\\n #\\n self.ts_df.index = pd.to_datetime(self.ts_df.index)\\n self.ts_df.sort_index(inplace=True)\\n # resample\\n self.ts_resample()\\n UVariateTimeSeriesClass.assertions(self, post=True)\\n #\\n if self.n_val > len(self.ts_df) - self.n_test:\\n self.n_val = len(self.ts_df) - self.n_test\\n\\n if self.n_test == 0 and self.n_val == 0:\\n self._mode = 'forecast'\\n elif self.n_test > 0:\\n self._mode = 'test'\\n elif self.n_test == 0 and self.n_val > 0:\\n self._mode = 'validate'\\n \\n # delegate just for good programming style here\\n super(UVariateTimeSeriesClass, self).__init__(**kwds)\",\n \"def testprepare_dataset(self, stock):\\n dataset = pd.DataFrame(yf.download(stock))\\n dataset['Difference'] = dataset['Close'].diff() # Finds the difference in price each day\\n dataset = dataset[1:] # Drops the first null value\\n dataset_train = dataset[0:len(dataset)-30] # Training dataset will be the whole dataset minus the test dataset\\n training_set = dataset_train.iloc[:, 6:7].values # Use the open values only\\n\\n # Feature Scaling\\n sc = MinMaxScaler(feature_range=(0, 1)) # Scales all values so that they are in the range (0, 1)\\n training_set_scaled = sc.fit_transform(training_set)\\n\\n # Create an input data structure with a timestep\\n x_train = []\\n y_train = []\\n for i in range(self.timestep, len(dataset)-30):\\n x_train.append(training_set_scaled[i - self.timestep:i, 0])\\n y_train.append(training_set_scaled[i, 0])\\n x_train, y_train = np.array(x_train), np.array(y_train)\\n\\n # Reshaping\\n x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\\n\\n return x_train, y_train\",\n \"def merge_data():\\n\\n\\tconfig = Config()\\n\\tfilename_train, filename_test = \\\"../data/train.csv\\\", \\\"../data/test.csv\\\" \\n\\n # create datasets\\n\\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\\n\\n # 1. datetime features\\n\\t# diff between weekday and day?\\n\\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\\n\\t#day - Between 1 and the number of days in the given month of the given year.\\n\\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\\n\\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\\n\\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\\n\\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\\n\\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\\n\\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\\n\\n\\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\\n\\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\\n\\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\\n\\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\\n\\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\\n\\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\\n\\n\\t# 2. Location features\\n\\tdef haversine(lon1, lat1, lon2, lat2):\\n\\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n\\t dlon = lon2 - lon1\\n\\t dlat = lat2 - lat1\\n\\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\\n\\t c = 2 * np.arcsin(np.sqrt(a))\\n\\t km = 6367 * c # AVG_EARTH_RADIUS=6367\\n\\t miles = km * 0.621371\\n\\t return miles\\n\\n\\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\\n\\t# a = haversine_array(lat1, lng1, lat1, lng2)\\n\\t# b = haversine_array(lat1, lng1, lat2, lng1)\\n\\t# return a + b\\n\\n\\t# def bearing_array(lat1, lng1, lat2, lng2):\\n\\t# AVG_EARTH_RADIUS = 6371 # in km\\n\\t# lng_delta_rad = np.radians(lng2 - lng1)\\n\\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\\n\\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\\n\\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\\n\\t# return np.degrees(np.arctan2(y, x))\\n\\n\\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\\n\\t train.dropoff_longitude, train.dropoff_latitude)\\n\\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\\n\\t test.dropoff_longitude, test.dropoff_latitude)\\n\\n\\n\\t# 3. Use outsource data\\n\\tweatherdata_filename = \\\"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\\\"\\n\\tfastestroute_data_train = \\\"../data/outsource_data/fastest_train.csv\\\"\\n\\tfastestroute_data_test = \\\"../data/outsource_data/fastest_routes_test.csv\\\"\\n\\n\\n\\twd = pd.read_csv(weatherdata_filename, header=0)\\n\\twd['date'] = pd.to_datetime(wd.date, format=\\\"%d-%m-%Y\\\")\\n\\twd['pickup_day'] = wd['date'].dt.day\\n\\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \\n\\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \\n\\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \\n\\n\\t# Merge training data with weather data on pickup_day\\n\\tprint(\\\"Merging training data with weather data ....\\\")\\n\\twd_train = pd.merge(train, wd, on='pickup_day')\\n\\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\\n\\tgc.collect()\\n\\n\\t# Merge wd_train with fastestroute_data\\n\\tfastest = pd.read_csv(fastestroute_data_train, header=0)\\n\\tprint(\\\"Merging Location data with weather and training data ....\\\")\\n\\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\\n\\n\\tgc.collect()\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Semi-final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\t# Use the same outsource data with test set\\n\\t# merge outsource data with test data as well\\n\\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\\t'total_travel_time',\\n\\t\\t\\t\\t\\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\\n\\t 'step_maneuvers','step_direction',\\t'step_location_list']\\n\\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\\n\\n\\tprint(\\\"Merging test data with Location data ....\\\")\\n\\ttest = pd.merge(test, fastest_test, on='id', how='outer')\\n\\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\\n\\t 'end_street'], axis=1)\\n\\tprint(\\\"Merging test data with weather data ....\\\")\\n\\ttest = pd.merge(test, wd, on='pickup_day')\\n\\n\\tprint(\\\"===================== CHECK TEST DATA =====================\\\")\\n\\n\\tprint(test.head(2))\\n\\tprint(\\\"Semi-final test data shape is: {}\\\".format(test.shape))\\n\\tprint(\\\"Test data columns: {}\\\".format(test.columns))\\n\\n\\n\\n\\t# 4. Do more data munging\\n\\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = (wd_train_fastest.trip_duration < 60) \\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.trip_duration > 79200\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\\n\\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\\n\\n\\t# 5. Do some data maskig based on location to create jfk and lgo features\\n\\tjfk_lon = -73.778889\\n\\tjfk_lat = 40.639722\\n\\tlga_lon = -73.872611\\n\\tlga_lat = 40.77725\\n\\n\\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\n\\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\\n\\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\\n\\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\\n\\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA AGAIN =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\treturn wd_train_fastest, test\",\n \"def training_data(loan_data):\\n return loan_data[loan_data.status != \\\"LIVE\\\"]\",\n \"def load_cup_data(train=True):\\n type = \\\"TR\\\" if train else \\\"TS\\\"\\n csv_file = path_data / Path(f\\\"ML_CUP/ML-CUP20-{type}.csv\\\")\\n return pd.read_csv(csv_file, skiprows=7, header=None, index_col=0)\",\n \"def read300yrf(period):\\n directory300 = '/seley/ypeings/simu/PAMIP-1.6-QBO-300yr/monthly/'\\n file300 = 'U10_1700-2000.nc'\\n filename = directory300 + file300\\n \\n data = Dataset(filename)\\n lat = data.variables['latitude'][:]\\n lon = data.variables['longitude'][:]\\n u10q = data.variables['U10'][:]\\n data.close()\\n \\n ### Reshape in year/month\\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\\n \\n ### Calculate over particular months\\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\\n \\n ### Slice U10 at 65N\\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\\n lat = lat[latq].squeeze()\\n u10 = u10[:,latq,:].squeeze()\\n \\n ### Take zonal mean \\n u10z = np.nanmean(u10,axis=1)\\n \\n ### Remove missing data\\n mask = np.where(u10z > -1e5)[0]\\n \\n ### Detrend\\n u10zdt = sss.detrend(u10z[mask],type='linear')\\n \\n return lat,lon,u10zdt\",\n \"def clean_train_test(train):\\n\\n train[\\\"Month\\\"] = train.Date.apply(lambda x: x.month)\\n train[\\\"Year\\\"] = train.Date.apply(lambda x: x.year)\\n train[\\\"Day\\\"] = train.Date.apply(lambda x: x.day)\\n\\n # Doesn't actually seem to help\\n #train[\\\"Latitude_int\\\"] = train.Latitude.apply(int)\\n #train[\\\"Longitude_int\\\"] = train.Longitude.apply(int)\\n\\n c2d = [\\\"Id\\\", \\\"Address\\\", \\\"AddressNumberAndStreet\\\", \\\"WnvPresent\\\",\\n \\\"NumMosquitos\\\"]\\n\\n for column in c2d:\\n if column in train.columns:\\n train.drop(column, axis=1, inplace=True)\\n\\n return train\",\n \"def train_test_data_df(train_data_file, test_data_file):\\n dtype_dict = {\\n \\\"age\\\": np.int32,\\n \\\"education-num\\\": np.int32,\\n \\\"capital-gain\\\": np.int32,\\n \\\"capital-loss\\\": np.int32,\\n \\\"hours-per-week\\\": np.int32\\n }\\n cols = [i for i in range(15) if i != 2]\\n train_data = pd.read_csv(train_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n train_data = train_data.dropna(axis=0, how=\\\"any\\\")\\n test_data = pd.read_csv(test_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n test_data = test_data.dropna(axis=0, how=\\\"any\\\")\\n return train_data, test_data\",\n \"def trainData(self, X, y, NeuralNet, epochs):\",\n \"def data():\\n\\n run_type = 'standardised'\\n sr = 48000\\n train_perc = 0.9\\n\\n if sr == 48000:\\n time_dimension = 282\\n if sr == 44100:\\n time_dimension = 259\\n\\n x_train, y_train, x_test, y_test = essential.compile_dataset(run_type, sr)\\n\\n # reshape for CNN input\\n x_train = np.array([x.reshape((128, time_dimension, 1)) for x in x_train])\\n x_test = np.array([x.reshape((128, time_dimension, 1)) for x in x_test])\\n\\n # encoded \\n encoder = LabelEncoder()\\n encoder.fit(y_train)\\n encoder.fit(y_test)\\n y_train = encoder.transform(y_train)\\n y_test = encoder.transform(y_test)\\n\\n return x_train, y_train, x_test, y_test\",\n \"def load_data(train_file, test_file):\\n\\n data = np.asarray(pd.read_csv(train_file, header=0))\\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\\n\\n x_tra = data[:, :-1]\\n y_tra = data[:, -1]\\n\\n return x_tra, y_tra, data_ts\",\n \"def cafe(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'cafe.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/Stat2Data/CAFE.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='cafe.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"def read_full_predictions(self, model_name):\\r\\n dates = []\\r\\n true_0mo = []\\r\\n true_6mo = []\\r\\n pred_6mo = []\\r\\n true_12mo = []\\r\\n pred_12mo = []\\r\\n true_24mo = []\\r\\n pred_24mo = []\\r\\n for test in self.full_predictions:\\r\\n test_data = self.full_predictions[test]\\r\\n dates.extend(test_data[self.output_names[0]][model_name]['Dates'])\\r\\n true_0mo.extend(test_data[self.output_names[0]][model_name]['True'])\\r\\n true_6mo.extend(test_data[self.output_names[1]][model_name]['True'])\\r\\n pred_6mo.extend(test_data[self.output_names[1]][model_name]['Predicted'])\\r\\n true_12mo.extend(test_data[self.output_names[2]][model_name]['True'])\\r\\n pred_12mo.extend(test_data[self.output_names[2]][model_name]['Predicted'])\\r\\n true_24mo.extend(test_data[self.output_names[3]][model_name]['True'])\\r\\n pred_24mo.extend(test_data[self.output_names[3]][model_name]['Predicted'])\\r\\n \\r\\n results = pd.DataFrame() \\r\\n results['Dates'] = dates\\r\\n results['True_{}'.format(self.output_names[0])] = true_0mo\\r\\n results['True_{}'.format(self.output_names[1])] = true_6mo\\r\\n results['Pred_{}'.format(self.output_names[1])] = pred_6mo\\r\\n results['True_{}'.format(self.output_names[2])] = true_12mo\\r\\n results['Pred_{}'.format(self.output_names[2])] = pred_12mo\\r\\n results['True_{}'.format(self.output_names[3])] = true_24mo\\r\\n results['Pred_{}'.format(self.output_names[3])] = pred_24mo\\r\\n \\r\\n return(results)\",\n \"def train(self):\\n df = self.df\\n self.scaler = MinMaxScaler()\\n self.scaler.fit(df)\\n df[df.columns] = self.scaler.transform(df)\\n\\n\\n X_train, y_train = get_X_y(df, self.n_days, self.length , self.style)\\n X_train = np.array(X_train)\\n X_train.shape = (X_train.shape[0], X_train.shape[2])\\n\\n self.clf = LogisticRegression().fit(X_train, y_train)\\n\\n #es = EarlyStopping(monitor = 'accuracy',mode = 'min' , verbose = 1, patience = 100, restore_best_weights = True)\",\n \"def test_adf(self):\\n\\n dftest = adfuller(self.ts_df['y'], autolag='AIC')\\n dfoutput = pd.Series(dftest[0:4],\\n index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\\n for key, value in dftest[4].items():\\n dfoutput['Critical Value (%s)' % key] = value\\n print(dfoutput)\\n if dftest[0] > dftest[4]['5%']:\\n print(\\n \\\"Test statistic greater than critical value at 5% --> series seems to be not stationary. \\\"\\n \\\"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic.\\\")\\n else:\\n print(\\n \\\"Test statistic less than critical value at 5% --> series seems to be stationary. \\\"\\n \\\"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic.\\\")\",\n \"def get_predictions(year, month):\\n \\n start_date = str(year)+\\\"-\\\"+str(month)+\\\"-01\\\"\\n end_date = str(year)+\\\"-\\\"+str(month)+\\\"-\\\"+str(monthrange(year, month)[1])\\n\\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\\\"%Y-%m-%d\\\").tolist()\\n\\n # predictfunction \\n # do predictions\\n pred_arr = []\\n file_name = '../predictions/model_'+str(year)+'_'+str(month)+'.csv'\\n \\n try:\\n predictions = load_predictions(file_name)\\n predictions = predictions.round()\\n except:\\n print(\\\"An exception occurred\\\")\\n predictions = pd.DataFrame(data = date_range,columns=['Datum'])\\n \\n \\n for index,row in predictions.iterrows():\\n \\n pred_mail = 0\\n pred_counter = 0\\n pred_tel = 0\\n \\n # check predictions dataframe for 'Datum'\\n if 'Datum' in predictions.columns:\\n date = row['Datum']\\n else:\\n break;\\n\\n # check predictions dataframe for 'Mail'\\n if 'Mail' in predictions.columns:\\n pred_mail = row['Mail']\\n\\n # check predictions dataframe for 'Schalter'\\n if 'Schalter' in predictions.columns:\\n pred_counter = row['Schalter']\\n\\n # check predictions dataframe for 'Tel'\\n if 'Tel' in predictions.columns:\\n pred_tel = row['Tel']\\n \\n \\n pred_dict = {'date': date, \\n 'predictions':{'mail' : pred_mail, \\n 'tel' : pred_tel, \\n 'counter' : pred_counter\\n }\\n }\\n\\n pred_arr.append(pred_dict)\\n\\n print(pred_arr) \\n \\n return pred_arr\",\n \"def data_preprocessing(dataset):\\r\\n df = pd.read_csv(dataset)\\r\\n df.head()\\r\\n df.describe()\\r\\n df.isnull().sum()\\r\\n df= df.drop(['instant'], axis=1)\\r\\n df['dteday'] = pd.to_datetime(df['dteday'].apply(str) + ' ' + df['hr'].apply(str) + ':00:00')\\r\\n return df\",\n \"def test_01_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\\n\\n self.assertTrue(os.path.exists(log_file))\",\n \"def test_jan1():\\n utcnow = utc(2020, 1, 1, 0, 30)\\n prod = parser(get_test_file(\\\"NESDIS/SCPCR1_jan1.txt\\\"), utcnow=utcnow)\\n assert prod.data[0].valid == utc(2019, 12, 31, 23, 57)\",\n \"def preprocess_data(data_path, last_day=None, schools_unis='two_separate', drop_features=None, min_confirmed=100,\\n min_deaths=10, smoothing=1, mask_zero_deaths=False, mask_zero_cases=False):\\n\\n # load data from our csv\\n df = pd.read_csv(data_path, parse_dates=[\\\"Date\\\"], infer_datetime_format=True).set_index(\\n [\\\"Country Code\\\", \\\"Date\\\"])\\n\\n # handle custom last day of analysis\\n if last_day is None:\\n Ds = list(df.index.levels[1])\\n else:\\n Ds = list(df.index.levels[1])\\n last_ts = pd.to_datetime(last_day, utc=True)\\n Ds = Ds[:(1 + Ds.index(last_ts))]\\n\\n nDs = len(Ds)\\n\\n all_rs = list([r for r, _ in df.index])\\n regions = list(df.index.levels[0])\\n locations = [all_rs.index(r) for r in regions]\\n sorted_regions = [r for l, r in sorted(zip(locations, regions))]\\n nRs = len(sorted_regions)\\n region_names = copy.deepcopy(sorted_regions)\\n region_full_names = df.loc[region_names][\\\"Region Name\\\"]\\n\\n if drop_features is None:\\n # note: these features are taken from the OxCGRT Dataset other than the mask wearing option\\n drop_features = ['Mask Wearing', 'Travel Screen/Quarantine', 'Travel Bans', 'Public Transport Limited',\\n 'Internal Movement Limited', 'Public Information Campaigns', 'Symptomatic Testing']\\n\\n for f in drop_features:\\n print(f'Dropping NPI {f}')\\n df = df.drop(f, axis=1)\\n\\n # pull data\\n CMs = list(df.columns[4:])\\n nCMs = len(CMs)\\n\\n ActiveCMs = np.zeros((nRs, nCMs, nDs))\\n Confirmed = np.zeros((nRs, nDs))\\n Deaths = np.zeros((nRs, nDs))\\n Active = np.zeros((nRs, nDs))\\n NewDeaths = np.zeros((nRs, nDs))\\n NewCases = np.zeros((nRs, nDs))\\n\\n for r_i, r in enumerate(sorted_regions):\\n region_names[r_i] = df.loc[(r, Ds[0])]['Region Name']\\n for d_i, d in enumerate(Ds):\\n Confirmed[r_i, d_i] = df.loc[(r, d)]['Confirmed']\\n Deaths[r_i, d_i] = df.loc[(r, d)]['Deaths']\\n Active[r_i, d_i] = df.loc[(r, d)]['Active']\\n\\n ActiveCMs[r_i, :, :] = df.loc[r].loc[Ds][CMs].values.T\\n\\n # compute new (daily) cases, after using thresholds\\n Confirmed[Confirmed < min_confirmed] = np.nan\\n Deaths[Deaths < min_deaths] = np.nan\\n NewCases[:, 1:] = (Confirmed[:, 1:] - Confirmed[:, :-1])\\n NewDeaths[:, 1:] = (Deaths[:, 1:] - Deaths[:, :-1])\\n NewDeaths[NewDeaths < 0] = 0\\n NewCases[NewCases < 0] = 0\\n\\n NewCases[np.isnan(NewCases)] = 0\\n NewDeaths[np.isnan(NewDeaths)] = 0\\n\\n if smoothing != 1:\\n print('Smoothing')\\n\\n # bulk smooth\\n SmoothedNewCases = np.around(\\n ss.convolve2d(NewCases, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary='symm',\\n mode='same'))\\n SmoothedNewDeaths = np.around(\\n ss.convolve2d(NewDeaths, 1 / smoothing * np.ones(shape=(1, smoothing)), boundary=\\\"symm\\\",\\n mode='same'))\\n\\n # correct for specific regions\\n for r in range(nRs):\\n # if the country has too few deaths, ignore\\n if Deaths[r, -1] < 50:\\n print(f'Note: did not smooth deaths in {region_names[r]}')\\n SmoothedNewDeaths[r, :] = NewDeaths[r, :]\\n\\n NewCases = SmoothedNewCases\\n NewDeaths = SmoothedNewDeaths\\n\\n print('Masking invalid values')\\n if mask_zero_deaths:\\n NewDeaths[NewDeaths < 1] = np.nan\\n else:\\n NewDeaths[NewDeaths < 0] = np.nan\\n\\n if mask_zero_cases:\\n NewCases[NewCases < 1] = np.nan\\n else:\\n NewCases[NewCases < 0] = np.nan\\n\\n Confirmed = np.ma.masked_invalid(Confirmed.astype(theano.config.floatX))\\n Active = np.ma.masked_invalid(Active.astype(theano.config.floatX))\\n Deaths = np.ma.masked_invalid(Deaths.astype(theano.config.floatX))\\n NewDeaths = np.ma.masked_invalid(NewDeaths.astype(theano.config.floatX))\\n NewCases = np.ma.masked_invalid(NewCases.astype(theano.config.floatX))\\n\\n # handle schools and universities\\n if schools_unis == 'two_xor':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs_final[:, university_index, :] = np.logical_xor(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs = ActiveCMs_final\\n CMs[school_index] = 'School and University Closure'\\n CMs[university_index] = 'Schools xor University Closure'\\n elif schools_unis == 'one_tiered':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :]) + 0.5 * np.logical_xor(\\n ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n\\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\\n CMs.remove('University Closure')\\n elif schools_unis == 'two_separate':\\n # don't need to do anything for this!\\n pass\\n elif schools_unis == 'one_and':\\n school_index = CMs.index('School Closure')\\n university_index = CMs.index('University Closure')\\n ActiveCMs_final = copy.deepcopy(ActiveCMs)\\n ActiveCMs_final[:, school_index, :] = np.logical_and(ActiveCMs[:, university_index, :],\\n ActiveCMs[:, school_index, :])\\n ActiveCMs = np.delete(ActiveCMs_final, university_index, axis=1)\\n CMs[school_index] = 'School and University Closure'\\n CMs.remove('University Closure')\\n\\n return PreprocessedData(Active,\\n Confirmed,\\n ActiveCMs,\\n CMs,\\n sorted_regions,\\n Ds,\\n Deaths,\\n NewDeaths,\\n NewCases,\\n region_full_names)\",\n \"def get_data(temp_estimate_source='historic'):\\n\\n print 'get data from files'\\n load = process_load_data(datafoldername+loadfilename_train)\\n load_test = process_load_data(datafoldername+loadfilename_test)\\n temp = process_temp_data(datafoldername+tempfilename_train)\\n holidays = process_holiday_data(datafoldername+holidayfilename)\\n\\n print 'merge load with temp data'\\n X_train_df = load.merge(temp, on='datetime', how='left')\\n X_test_df = load_test.merge(temp, on='datetime', how='left')\\n\\n print 'estimate missing temps'\\n # find rows with missing temperatures\\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\\n\\n # source estimates for missing periods\\n if temp_estimate_source == 'arima':\\n # use preprocessed arima estimates\\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\\n elif temp_estimate_source == 'actuals':\\n # use actual temperatures - as provided after conclusion of kaggle competition\\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\\n else:\\n # use means of historical temps at same day/time.\\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\\n\\n # merge estimates against missing rows, and use to update original dataframe in place\\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\\n replace_unknown_temps(X_test_df, replacementtemps)\\n\\n print 'merge in holiday dates'\\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\\n X_train_df['holiday'].fillna(0, inplace=True)\\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\\n X_test_df['holiday'].fillna(0, inplace=True)\\n\\n print 'add datetime categorical variables'\\n X_train_df = add_datetime_categories(X_train_df)\\n X_test_df = add_datetime_categories(X_test_df)\\n\\n return X_train_df, X_test_df\",\n \"def main(input, output):\\n # turn csv to dataframe\\n df = pd.read_csv(f\\\"./data/{input}\\\", index_col=0)\\n\\n # split training and test\\n X = df.drop(columns=['DEFAULT_NEXT_MONTH'])\\n y = df['DEFAULT_NEXT_MONTH']\\n X_train, X_test, y_train, y_test = train_test_split(\\n X, y, test_size=0.25, random_state=122)\\n\\n # We use robust scalar as most of our data is not normally distributed and we have a high amount of outliers.\\n columns = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1', 'PAY_2',\\n 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\\n 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\\n 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\\n scaler = preprocessing.RobustScaler()\\n X_train = pd.DataFrame(scaler.fit_transform(X_train), columns=columns)\\n X_test = pd.DataFrame(scaler.transform(X_test), columns=columns)\\n\\n # Use RFE to identify the most identify the most useful predictors.\\n # Then we will drop those columns that are deemed as less useful.\\n logreg = LogisticRegression(solver=\\\"lbfgs\\\")\\n\\n get_report(X_train, y_train, X_test, y_test, logreg, 'results_baseline')\\n rfe = RFE(logreg, 7)\\n rfe = rfe.fit(X_train, y_train.values.ravel())\\n columns_to_drop = list()\\n for i in range(len(rfe.support_)):\\n include = rfe.support_[i]\\n if include == False:\\n columns_to_drop.append(columns[i])\\n X_train = X_train.drop(columns_to_drop, axis=1)\\n X_test = X_test.drop(columns_to_drop, axis=1)\\n get_report(X_train, y_train, X_test, y_test, logreg, output)\\n\\n # write compounded dataframe with results of two get_report calls\\n accuracies_output_df.to_csv(f'./{output}/accuracies.csv', index=False)\",\n \"def simple_time_series(full_df, test_period, display_graphs=True):\\n df = full_df.copy()\\n df = df.filter([\\\"Canteen\\\"])\\n\\n train = df.iloc[:-test_period]\\n test = df.iloc[-test_period:]\\n\\n resulting_prediction, predictions = prediction(train, test)\\n\\n if display_graphs is True:\\n plt.figure(figsize=(14, 7))\\n plt.plot(train)\\n plt.plot(resulting_prediction)\\n plt.legend([\\\"Real values\\\", \\\"Prediction\\\"], loc=\\\"best\\\")\\n plt.xlabel(\\\"Date\\\")\\n plt.ylabel(\\\"Number of people\\\")\\n\\n print(\\n \\\"The mean absolute error (MAE) for the Simple Time Series model is {0:.0f} people\\\".format(\\n find_MAE(test, predictions)\\n )\\n )\",\n \"def walkforward_validation(data, test_start_date, test_end_date=None, step_size=15, testsize=15, model='SARIMA'):\\n test_start_date = pd.to_datetime(test_start_date)\\n current_max_date = test_start_date\\n\\n modelling_results = pd.DataFrame(columns=['series_name', 'model_type', 'test_start', 'test_end', 'MAE', 'MAPE', 'RMSE'])\\n\\n if test_end_date is None:\\n test_end_date = data.index.max()\\n test_end_date = pd.to_datetime(test_end_date)\\n else:\\n test_end_date = pd.to_datetime(test_end_date)\\n\\n while current_max_date < test_end_date:\\n data.index = pd.to_datetime(data.index)\\n iter_data = data[data.index <= current_max_date + timedelta(days=testsize)]\\n test, train = test_train_spl(iter_data, testsize=testsize)\\n\\n if (model.upper() == 'SARIMA') | (model.upper() == 'SARIMAX'):\\n print('USING SARIMA MODEL')\\n mae, rmse, mape, name, preds, conf_intervals = mod_sarima(train=train, test=test, **arima_model_params)\\n elif model.upper() == 'PROPHET':\\n print('USING PROPHET MODEL')\\n mae, rmse, mape, name, preds, conf_intervals = mod_prophet(train=train, test=test, **prophet_model_params)\\n else:\\n print('model name not known')\\n iter_results = pd.DataFrame({'series_name': name, 'model_type': model, 'test_start': [current_max_date],\\n 'test_end': [current_max_date + timedelta(testsize)], 'MAE': [mae], 'MAPE': [mape], 'RMSE': [rmse]})\\n modelling_results = modelling_results.append(iter_results, ignore_index=True)\\n\\n # this line is just for validation of the effect of regressors in the forecast\\n preds.to_csv(mod_report_path + arima_model_params['name'] + 'forecast_' + str(current_max_date).replace(':', '')+ '.csv')\\n\\n current_max_date = current_max_date + timedelta(days=step_size)\\n\\n return modelling_results\",\n \"def __init__(self, calendar_data_path_str, sell_prices_data_path_str, sales_train_validation_data_path_str, train_test_date_split, dept_id = None):\\n\\n self.sales_train_validation_df = pd.read_csv(sales_train_validation_data_path_str)\\n self.sales_train_validation_df.sort_values(\\\"id\\\", inplace = True)\\n self.calendar_df = pd.read_csv(calendar_data_path_str)\\n self.sell_prices_df = pd.read_csv(sell_prices_data_path_str)\\n\\n # Filter by dept_id if needed\\n if dept_id is not None:\\n self.sales_train_validation_df = self.sales_train_validation_df.loc[self.sales_train_validation_df[\\\"cat_id\\\"] == dept_id]\\n\\n # Generate a validation set if enable_validation is True\\n train_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\\\"d_\\\")] + self.calendar_df[\\\"d\\\"].loc[self.calendar_df[\\\"date\\\"] <= \\\"2016-03-27\\\"].tolist()\\n test_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\\\"d_\\\")] + self.calendar_df[\\\"d\\\"].loc[(self.calendar_df[\\\"date\\\"] > \\\"2016-03-27\\\") & (self.calendar_df[\\\"date\\\"] <= \\\"2016-04-24\\\")].tolist()\\n self.train_df = self.sales_train_validation_df[train_cols_lst].reset_index(drop = True)\\n self.valid_df = self.sales_train_validation_df[test_cols_lst].reset_index(drop = True)\\n \\n self.train_target_columns = [i for i in self.train_df.columns if i.startswith(\\\"d_\\\")]\\n self.weight_columns = self.train_df.iloc[:, -28:].columns.tolist()\\n self.train_df[\\\"all_id\\\"] = \\\"all\\\"\\n self.valid_df[\\\"all_id\\\"] = \\\"all\\\"\\n \\n self.id_columns = [i for i in self.train_df.columns if not i.startswith(\\\"d_\\\")]\\n self.valid_target_columns = [i for i in self.valid_df.columns if i.startswith(\\\"d_\\\")]\\n\\n self.group_ids = (\\\"all_id\\\", \\\"state_id\\\", \\\"store_id\\\", \\\"cat_id\\\", \\\"dept_id\\\", \\\"item_id\\\", \\n [\\\"state_id\\\", \\\"cat_id\\\"], [\\\"state_id\\\", \\\"dept_id\\\"], [\\\"store_id\\\", \\\"cat_id\\\"], \\n [\\\"store_id\\\", \\\"dept_id\\\"], [\\\"item_id\\\", \\\"state_id\\\"], [\\\"item_id\\\", \\\"store_id\\\"])\\n\\n self.train_series = self.trans_30490_to_42840(self.train_df, self.train_target_columns)\\n self.valid_series = self.trans_30490_to_42840(self.valid_df, self.valid_target_columns)\\n self.roll_mat_csr, self.roll_index = self.create_rollup_index()\\n self.group_ids_items = self.generate_group_ids_items()\\n\\n self.S = self.get_s()\\n self.W = self.get_w()\\n self.SW = self.W / np.sqrt(self.S)\\n\\n self.weights = self.get_weight_df() # Equivalent to get_w()\\n self.scale = self.get_scale() # Equivalent to get_s()\\n\\n #self.train_series = None\\n #self.train_df = None\\n gc.collect()\",\n \"def mark_hours(train_file, test_file):\\n train = pd.read_csv(train_file, header=0)\\n test = pd.read_csv(test_file, header=0)\\n \\n \\n \\n train_hours = train.loc[:, ['duration_label', 'hours']].copy(deep=True).dropna()\\n test_hours = test.loc[:, ['hours']].copy(deep=True).dropna()\\n \\n train_hours['hd'] = train_hours['hours'].apply(lambda x: x**2)\\n test_hours['hd'] = test_hours['hours'].apply(lambda x: x**2)\\n \\n # change label to binary\\n binary = {1: 0, 2: 0, 3: 1}\\n train_hours['duration_label'] = train_hours['duration_label'].apply(lambda x: binary[x])\\n \\n # prepare data\\n X_train = train_hours.loc[:, ['hours', 'hd']]\\n y_train = train_hours['duration_label']\\n X_test = test_hours.loc[:, ['hours', 'hd']]\\n \\n # train & predict\\n clf = svm.SVC(kernel='rbf', gamma='auto', C=1, probability=True)\\n clf.fit(X_train, y_train)\\n predict_train = clf.predict(X_train)\\n predict_test = clf.predict(X_test)\\n \\n train_hours['hour_label'] = predict_train\\n test_hours['hour_label'] = predict_test\\n \\n # merge\\n train = pd.merge(train, train_hours['hour_label'], how='left', right_on = 'hour_label', left_index=True, right_index=True).fillna(int(0)).drop('hours', axis=1)\\n test = pd.merge(test, test_hours['hour_label'], how='left', right_on = 'hour_label', left_index=True, right_index=True).fillna(int(0)).drop('hours', axis=1)\\n \\n train.to_csv(train_file, header=True, index=False)\\n test.to_csv(test_file, header=True, index=False)\",\n \"def get_srs_features(df):\\n\\n #test result classified by labels\\n li = df.TestResultsCode.tolist()\\n labels = [ item.split('_') for item in li ]\\n for item in labels:\\n if len(item)==4:\\n add = item[0]+item[1]\\n item = item.insert( 0, add )\\n for item in labels:\\n if 'not' in item:\\n item.remove('not')\\n if 'detected' in item:\\n item.remove('detected')\\n\\n\\n #one-hot encode the test results\\n disease = [ la[0] for la in labels ]\\n spread = [ la[1] for la in labels ]\\n risk = [ la[2] for la in labels ]\\n\\n disease_encode = pd.Series( disease ).str.get_dummies()\\n spread_encode = pd.Series( spread ).str.get_dummies()\\n risk_encode = pd.Series( risk ).str.get_dummies()\\n\\n disease_encode = pd.DataFrame( disease_encode )\\n spread_encode = pd.DataFrame( spread_encode )\\n risk_encode = pd.DataFrame( risk_encode)\\n\\n #interate one hot encoding of test results back to df\\n df=df.drop(['PatientID', 'Address', 'CurrentLocation'],axis=1)\\n df2 = df\\n df2 = df2.drop(columns = 'TestResultsCode')\\n\\n results = pd.concat( [risk_encode, spread_encode, disease_encode], axis=1 )\\n results = results.drop(['NotSpreader', 'NotatRisk'], axis=1)\\n\\n from sklearn.model_selection import train_test_split\\n\\n X_train, X_val, y_train, y_val = train_test_split( df2, results, test_size=0.33, random_state=33 ) #tr is test results numerically coded\\n X_val, X_test, y_val, y_test = train_test_split( X_val, y_val , test_size=0.4, random_state=33)\\n\\n #REMOVED LOCATION FROM FEATURES\\n\\n # choosing from those features\\n cols =['AgeGroup','AvgHouseholdExpenseOnPresents','AvgHouseholdExpenseOnSocialGames',\\n 'AvgHouseholdExpenseParkingTicketsPerYear','AvgMinSportsPerDay','AvgTimeOnSocialMedia','AvgTimeOnStuding','BMI',\\n 'DisciplineScore','HappinessScore','Job','NrCousins','StepsPerYear','SyndromeClass','TimeOnSocialActivities']\\n\\n X_train_sfs = X_train[cols]\\n X_train_sfs = X_train_sfs.fillna(X_train_sfs.mean())\\n\\n from sklearn.neighbors import KNeighborsClassifier\\n from mlxtend.feature_selection import SequentialFeatureSelector as SFS #import from mlxtend library\\n knn = KNeighborsClassifier(n_neighbors=2) # ml_algo used = knn\\n sfs = SFS(knn,\\n k_features=10,\\n forward=True, # if forward = True then SFS otherwise SBS\\n floating=False,\\n verbose=2,\\n scoring='accuracy'\\n )\\n\\n\\n #after applying sfs fit the data:\\n sfs.fit(X_train_sfs, y_train)\\n\\n return sfs.k_feature_names_\",\n \"def __init__(\\n self, data_set=\\\"fake_data\\\", max_year=(VALIDATION_YEAR_RANGE[0] - 1), **kwargs\\n ):\\n data_kwargs = {\\n **{\\n \\\"data_set\\\": data_set,\\n \\\"train_year_range\\\": (max_year,),\\n \\\"test_year_range\\\": (max_year, max_year + 1),\\n },\\n **kwargs,\\n }\\n super().__init__(\\n **data_kwargs,\\n )\\n\\n self.max_year = max_year\",\n \"def test_sales_forecasting_one_ipu(self):\\n with TemporaryDirectory() as temp_dir:\\n self.run_command(\\n (\\n \\\"python3 main.py --use-synthetic-data --epochs 1\\\"\\n f\\\" --mov-mean-window 0 --log-dir {temp_dir}\\\"\\n ),\\n working_path,\\n [\\n \\\"Begin training loop\\\", \\\"Training:\\\", r\\\"epoch:\\\\s+1\\\",\\n \\\"Validation:\\\", \\\"Best RMSPE|no valid RMSPE results\\\"\\n ]\\n )\",\n \"def test_03_predict(self):\\n country = 'all'\\n year = 2018\\n month = 2\\n n_next = 20\\n query_data = {'country': 'all',\\n 'year': 2018,\\n 'month': 2,\\n 'n_next': 20\\n }\\n\\n request_json = {'query':query_data,'mode':'test'}\\n\\n r = requests.post('http://localhost:{}/predict'.format(port),json=request_json)\\n response = literal_eval(r.text)\\n\\n self.assertTrue(len(response['y_pred'])==n_next)\\n self.assertTrue(len(response['y_lower'])==n_next)\\n self.assertTrue(len(response['y_upper'])==n_next)\\n\\n for p in response['y_pred']:\\n self.assertTrue(isinstance(p,numbers.Number))\\n\\n for p in response['y_lower']:\\n self.assertTrue(isinstance(p,numbers.Number))\\n\\n for p in response['y_upper']:\\n self.assertTrue(isinstance(p,numbers.Number))\",\n \"def __init__(self, pandas_dataframe, dates_column, target_column, regressors=None, train_test_split=0.66, seed=7,\\n look_back=1, look_forward=1, interval=0):\\n data = pd.DataFrame(index=pandas_dataframe[dates_column].values, data=pandas_dataframe[target_column].values)\\n # Calculate the training set size\\n train_size = int(len(data)*train_test_split)\\n # Scale the data pre-train/test split\\n scaler = MinMaxScaler(feature_range=(0, 1))\\n self.scaler = scaler\\n data = scaler.fit_transform(data)\\n # Get the time series as stationary (for the given interval, if 0 don't make it a series of 0)\\n if interval > 0:\\n data = difference(data, interval)\\n # Map the series to a supervised problem (values for days 1-n with regressors for these days to predict days\\n # n + 1 ... n + k\\n x, y = timeseries_to_supervised(data, look_back=look_back, look_forward=look_forward)\\n # Split train and test\\n self.x_train, self.y_train = x[:train_size], y[:train_size]\\n self.x_test, self.y_test = x[train_size:], y[train_size:]\\n # Use regressors if required\\n if regressors is not None:\\n self.x_train, self.x_test = add_regressors(self.x_train, self.x_test, regressors, pandas_dataframe,\\n dates_column, look_forward, look_back)\\n # Set last attributes\\n self.seed = seed\\n self.look_back = look_back\\n self.look_forward = look_forward\\n self.regressors = regressors\",\n \"def fetch_bank_df(preprocess=False):\\n (train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\\n \\\"bank-marketing\\\", \\\"classification\\\", astype=\\\"pandas\\\", preprocess=preprocess\\n )\\n orig_X = pd.concat([train_X, test_X]).sort_index()\\n orig_y = pd.concat([train_y, test_y]).sort_index().astype(np.float64)\\n column_map = {\\n \\\"v1\\\": \\\"age\\\",\\n \\\"v2\\\": \\\"job\\\",\\n \\\"v3\\\": \\\"marital\\\",\\n \\\"v4\\\": \\\"education\\\",\\n \\\"v5\\\": \\\"default\\\",\\n \\\"v6\\\": \\\"balance\\\",\\n \\\"v7\\\": \\\"housing\\\",\\n \\\"v8\\\": \\\"loan\\\",\\n \\\"v9\\\": \\\"contact\\\",\\n \\\"v10\\\": \\\"day\\\",\\n \\\"v11\\\": \\\"month\\\",\\n \\\"v12\\\": \\\"duration\\\",\\n \\\"v13\\\": \\\"campaign\\\",\\n \\\"v14\\\": \\\"pdays\\\",\\n \\\"v15\\\": \\\"previous\\\",\\n \\\"v16\\\": \\\"poutcome\\\",\\n }\\n if preprocess:\\n\\n def map_col(col):\\n if col.find(\\\"_\\\") == -1:\\n return column_map[col]\\n prefix, suffix = col.split(\\\"_\\\")\\n return column_map[prefix] + \\\"_\\\" + suffix\\n\\n orig_X.columns = [map_col(col) for col in orig_X.columns]\\n age = pd.Series(orig_X[\\\"age\\\"] >= 25, dtype=np.float64)\\n encoded_X = orig_X.assign(age=age)\\n encoded_y = pd.Series(orig_y == 0, dtype=np.float64, name=orig_y.name)\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [1]},\\n ],\\n }\\n return encoded_X, encoded_y, fairness_info\\n else:\\n orig_X.columns = [column_map[col] for col in orig_X.columns]\\n fairness_info = {\\n \\\"favorable_labels\\\": [1],\\n \\\"protected_attributes\\\": [\\n {\\\"feature\\\": \\\"age\\\", \\\"reference_group\\\": [[25, 1000]]},\\n ],\\n }\\n return orig_X, orig_y, fairness_info\",\n \"def create_forecast_dataset(self):\\n pass\",\n \"def baseline(x_data, y_data, stra = \\\"uniform\\\"):\\r\\n x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2)\\r\\n dummy = DummyClassifier(strategy= stra)\\r\\n dummy.fit(x_train, y_train)\\r\\n y_pred = dummy.predict(x_test)\\r\\n accu = accuracy_score(y_test, y_pred)\\r\\n return accu\",\n \"def big9salary(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'big9salary.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/wooldridge/big9salary.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='big9salary.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"def fitting_lr_and_rf(file, test_yr, fit_lr, fit_rf):\\n df = pd.read_csv(file)\\n\\n mask_test = (df.year == test_yr)\\n mask_train = (df.year >= test_yr-6) & (df.year <= test_yr-1)\\n\\n target = 'wkts'\\n\\n features_full = ['year1_mtchs_pld', 'year2_mtchs_pld', 'year3_mtchs_pld', 'year4_mtchs_pld', 'year5_mtchs_pld',\\n 'year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm','year4_wkts_pm', 'year5_wkts_pm',\\n 'bowler_agnst_oppo', 'oppo_agnst_bowl_typ', 'bowl_home_adv', 'ground_bowl_typ']\\n features_small = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm',\\n 'bowler_agnst_oppo', 'oppo_agnst_bowl_typ', 'bowl_home_adv', 'ground_bowl_typ']\\n features_smaller = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm',\\n 'bowl_home_adv', 'ground_bowl_typ']\\n features_smallest = ['year1_wkts_pm', 'year2_wkts_pm', 'year3_wkts_pm', 'year4_wkts_pm', 'year5_wkts_pm']\\n\\n print(\\\"*********************************************\\\")\\n print(\\\"**** RUNNING MODELS FOR FULL FEATURE SET ****\\\")\\n print(\\\"*********************************************\\\")\\n\\n features = features_full.copy()\\n\\n X_train = df[mask_train][features]\\n y_train = df[mask_train][target]\\n X_test = df[mask_test][features]\\n y_test = df[mask_test][target]\\n\\n if fit_lr:\\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n if fit_rf:\\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n\\n print(\\\"**********************************************\\\")\\n print(\\\"**** RUNNING MODELS FOR SMALL FEATURE SET ****\\\")\\n print(\\\"**********************************************\\\")\\n\\n features = features_small.copy()\\n\\n X_train = df[mask_train][features]\\n y_train = df[mask_train][target]\\n X_test = df[mask_test][features]\\n y_test = df[mask_test][target]\\n\\n if fit_lr:\\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n if fit_rf:\\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n\\n print(\\\"************************************************\\\")\\n print(\\\"**** RUNNING MODELS FOR SMALLER FEATURE SET ****\\\")\\n print(\\\"************************************************\\\")\\n\\n features = features_smaller.copy()\\n\\n X_train = df[mask_train][features]\\n y_train = df[mask_train][target]\\n X_test = df[mask_test][features]\\n y_test = df[mask_test][target]\\n\\n if fit_lr:\\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n if fit_rf:\\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n\\n print(\\\"*************************************************\\\")\\n print(\\\"**** RUNNING MODELS FOR SMALLEST FEATURE SET ****\\\")\\n print(\\\"*************************************************\\\")\\n\\n features = features_smallest.copy()\\n\\n X_train = df[mask_train][features]\\n y_train = df[mask_train][target]\\n X_test = df[mask_test][features]\\n y_test = df[mask_test][target]\\n\\n if fit_lr:\\n fit_lr_model(df, X_train, y_train, X_test, y_test, mask_test)\\n\\n if fit_rf:\\n fit_rf_model(df, X_train, y_train, X_test, y_test, mask_test)\",\n \"def test_ann(trainx, trainy, testx, testy):\\n\\tinput_size = len(trainx.iloc[0])\\n\\toutput_size = len(np.unique(trainy))\\n\\ttrain = ClassificationDataSet(input_size, 1, nb_classes=output_size)\\n\\tfor i in range(len(trainx.index)):\\n\\t\\ttrain.addSample(trainx.iloc[i].values, trainy.iloc[i])\\n\\n\\ttest = ClassificationDataSet(input_size, 1, nb_classes=output_size)\\n\\tfor i in range(len(testx.index)):\\n\\t\\ttest.addSample(testx.iloc[i].values, testy.iloc[i])\\n\\ttrain._convertToOneOfMany()\\n\\ttest._convertToOneOfMany()\\n\\n\\tprint(\\\"Number of training patterns: \\\", len(train))\\n\\tprint(\\\"Input and output dimensions: \\\", train.indim, train.outdim)\\n\\tprint(\\\"First sample (input, target):\\\")\\n\\tprint(train[\\\"input\\\"][0], train[\\\"target\\\"][0])\\n\\n\\tn_hidden = 3\\n\\tfnn = buildNetwork(train.indim, n_hidden, train.outdim)\\n\\ttrainer = BackpropTrainer(\\n\\t fnn, dataset=train, momentum=0.1, verbose=True, weightdecay=0.01)\\n\\n\\tprint(\\\"# hidden nodes: {}\\\".format(n_hidden))\\n\\tfor i in range(25):\\n\\t trainer.trainEpochs(1)\\n\\t trnresult = percentError(trainer.testOnClassData(), train[\\\"target\\\"])\\n\\t tstresult = percentError(\\n\\t trainer.testOnClassData(dataset=test), test[\\\"target\\\"])\\n\\t print(\\\"epoch: %4d\\\" % trainer.totalepochs, \\\" train error: %5.2f%%\\\" % trnresult, \\\" test error: %5.2f%%\\\" % tstresult)\\n\\tpred = fnn.activateOnDataset(test)\\n\\tpreds = [y.argmax() for y in pred]\\n\\tprint(accuracy_score(preds, testy, normalize=True))\",\n \"def cps78_85(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'cps78_85.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/wooldridge/cps78_85.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='cps78_85.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"def test_train_bad():\\n\\tdf = pd.read_csv(\\\"test/sample_features.csv\\\")\\n\\tdf['test'] = 'test'\\n\\n\\ty_train = df['price']\\n\\tX_train = df.loc[:, df.columns != 'price']\\n\\n\\tparams = {'n_estimators': 5, 'random_state': 2}\\n\\n\\twith pytest.raises(ValueError) as e:\\n\\t\\trf_test = train(X_train, y_train, params)\\n\\n\\tassert str(e.value) == 'Wrong data type of inputs for training a random forest regression model!'\",\n \"def forecast(self, stock):\\n # Load the trained model\\n model_handler = ModelHandler()\\n model = model_handler.load_json_model(stock)\\n\\n # Importing the training set\\n dataset = pd.read_csv(stock.csv_name)\\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\\n\\n # Create the test dataset\\n dataset_test = dataset[len(dataset) - 30:]\\n real_stock_price = dataset_test.iloc[:, 1:2].values\\n dataset = dataset['Open']\\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\\n inputs = inputs.reshape(-1, 1)\\n\\n # Feature Scaling\\n sc = MinMaxScaler(feature_range=(0, 1))\\n inputs = sc.fit_transform(inputs)\\n\\n x_test = []\\n x_test.append(inputs[0:60, 0])\\n predicted_values = []\\n for i in range(1, 31):\\n x_test_np = np.array(x_test)\\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\\n new_data = model.predict(x_test_np)\\n predicted_values.append(new_data[0])\\n x_test[0] = np.delete(x_test[0], 0)\\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\\n\\n predicted_values = sc.inverse_transform(predicted_values)\\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\\n plt.gcf().autofmt_xdate()\\n plt.title(f'{stock.ticker} Stock Price Prediction')\\n plt.xlabel('Time')\\n plt.ylabel(f'{stock.ticker} Stock Price')\\n plt.legend()\\n plt.show()\",\n \"def _assess(self):\\n # get eruptions\\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\\n # check if data file exists\\n self.exists = os.path.isfile(self.file)\\n if not self.exists:\\n t0 = datetime(2011,1,1)\\n t1 = datetime(2011,1,2)\\n self.update(t0,t1)\\n # check date of latest data in file\\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def train_on_raw_data(data_alice, data_bob):\\n raw_test_data = pd.concat([data_alice[1], data_bob[1]])\\n x = raw_test_data.drop('Class', axis=1)\\n y = raw_test_data['Class']\\n\\n return fit_rf_classifier(x, y)\",\n \"def predict(self, next_days):\\n last_date = self.series.index[-1]\\n for time in range(next_days):\\n row = {}\\n for c in self.countries:\\n history = self.series[c]\\n series = history.astype(float)\\n model = ARIMA(series, order=(2,1,0))\\n model_fit = model.fit()\\n output = model_fit.forecast()\\n row[c] = output[0][0]\\n last_date = last_date + pd.DateOffset(1)\\n self.series.loc[last_date] = row\",\n \"def parse_data():\\n n_train, n_test = 15000, 4996\\n n_features = 1355191\\n\\n print('- parsing train data')\\n X_train = sp.lil_matrix((n_train, n_features))\\n y_train = np.zeros(n_train)\\n with open('/Users/kitazawa/data/news20.train') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_train[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_train[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\\n\\n print('- parsing test data')\\n X_test = sp.lil_matrix((n_test, n_features))\\n y_test = np.zeros(n_test)\\n with open('/Users/kitazawa/data/news20.test') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_test[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_test[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\\n\\n return X_train, y_train, X_test, y_test\",\n \"def test_load_time_series (self):\\n self.dm._load_time_series()\\n df = self.dm.df_final\\n # Define expected results.\\n dt_1 = pd.to_datetime('3/1/2018')\\n dt_2 = pd.to_datetime('2/10/2018')\\n\\n expected = [('SP500', dt_1, -0.013324),\\n ('btc', dt_1, 0.053193),\\n ('btc_volume', dt_1, 0.05040065),\\n ('eth', dt_2, -0.026542),\\n ('eth_volume', dt_2, -0.209714)]\\n for (col, idx, value) in expected:\\n msg = '{0} value not what expected on {1}'.format(\\n col, cryp.fmt_date(idx))\\n actual_value = df.loc[idx, col]\\n self.assertAlmostEqual(value, actual_value, DEC_ACCY, msg)\\n\\n ##############################\\n # Test non-crypto-asset (SPX) to ensure returns properly rolled forward.\\n ##############################\\n # Return on 1/13/2018 (Saturday), 1/14 (Sunday), and 1/15 (MLK day)\\n # should all be equal to the return on 1/12 (Friday).\\n # Next \\\"new\\\" return should be on 1/16 (price change from 1/12 to 1/16).\\n expected_return_1 = 0.006750 # index change from 1/11 -> 1/12.\\n expected_return_2 = -0.003524 # index change from 1/12 -> 1/16.\\n expected_spx = [(pd.to_datetime('1/12/2018'), expected_return_1),\\n (pd.to_datetime('1/13/2018'), expected_return_1),\\n (pd.to_datetime('1/14/2018'), expected_return_1),\\n (pd.to_datetime('1/15/2018'), expected_return_1),\\n (pd.to_datetime('1/16/2018'), expected_return_2)]\\n\\n for (idx, value) in expected_spx:\\n msg = 'SP500 return on {} not what expected.'.format(\\n cryp.fmt_date(idx))\\n actual_value = df.loc[idx, 'SP500']\\n self.assertAlmostEqual(value, actual_value, DEC_ACCY, msg)\",\n \"def test_04_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n\\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\\n self.assertEqual(y_pred,logged_y_pred)\",\n \"def fit(self, train):\\n model = ARIMA(train, order=(self.p, self.d, self.q))\\n model_fit = model.fit(disp=0)\\n output = model_fit.forecast()\\n nextDaysPred = output[0][0]\\n return nextDaysPred\",\n \"def growing_testset(train_filename, test_filename, clf, clf_name=None):\\n train_features, train_groundtruths = read_file(train_filename)\\n test_features, test_groundtruths = read_file(test_filename)\\n \\n if clf_name is not None and \\\"RANSAC\\\" in clf_name:\\n train_groundtruths = [True if i ==\\\"s\\\" else False for i in train_groundtruths]\\n test_groundtruths = [True if i ==\\\"s\\\" else False for i in test_groundtruths]\\n\\n clf.fit(train_features, train_groundtruths)\\n\\n if clf_name is not None and \\\"RANSAC\\\" in clf_name:\\n preds_float = clf.predict(test_features)\\n predictions = [True if i > 0.5 else False for i in preds_float]\\n else:\\n predictions = clf.predict(test_features)\\n \\n test_acc = []\\n # test_acc.append(accuracy_score(test_groundtruths, predictions))\\n test_acc.append(precision_score(test_groundtruths, predictions, average=None)[0])\\n print(\\\"Accuracy : \\\" + str(test_acc))\\n print(\\\"Precision: \\\" + str(precision_score(test_groundtruths, predictions, average=None)))\\n print(\\\"Recall : \\\" + str(recall_score(test_groundtruths, predictions, average=None)))\\n print(\\\"F-score : \\\" + str(f1_score(test_groundtruths, predictions, average=None)))\\n\\n n_splits = 10\\n for n_split in range(2, n_splits+1):\\n print(n_split)\\n feats_array, gts_array = split(test_features, test_groundtruths, n_split)\\n tmp_acc = []\\n for feats, gts in zip(feats_array, gts_array):\\n if clf_name is not None and \\\"RANSAC\\\" in clf_name:\\n preds_float = clf.predict(feats)\\n predictions = [True if i > 0.5 else False for i in preds_float]\\n else:\\n predictions = clf.predict(feats)\\n # cur_acc = accuracy_score(gts, predictions)\\n cur_acc = precision_score(gts, predictions, average=None)[0]\\n tmp_acc.append(cur_acc)\\n print(\\\"\\\\t\\\" + str(cur_acc))\\n accuracy = sum(tmp_acc) / float(len(tmp_acc))\\n test_acc.append(accuracy)\\n return test_acc\",\n \"def test_data_preprocessing(raw_data): \\r\\n\\r\\n # get data output\\r\\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\\r\\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\\r\\n with open('cache/extended_columns.pkl', 'rb') as f:\\r\\n extended_columns = pickle.load(f)\\r\\n with open('cache/num_date_columns.pkl', 'rb') as f:\\r\\n max_date_columns = pickle.load(f)\\r\\n \\r\\n data_output = class_binaryzation_for_test(data_output, extended_columns)\\r\\n try:\\r\\n data_output = date_separation1(data_output, max_num_columns=NUM_DATE_COLUMNS)\\r\\n except:\\r\\n data_output = date_separation2(data_output)\\r\\n data_output = money_digitalization(data_output)\\r\\n data_output = get_date_in_days(data_output)\\r\\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\\\\r\\n - data_output['Posting Date Date Days from 2016'] \\r\\n\\r\\n return data_output\",\n \"def ANdatefixer(years):\\n\\n\\n\\t# ========== create the new dates ==========\\n\\t# year = ds.Year\\n\\n\\t# +++++ set up the list of dates +++++\\n\\tdates = OrderedDict()\\n\\ttm = [dt.datetime(int(year) , 6, 30) for year in years]\\n\\tdates[\\\"time\\\"] = pd.to_datetime(tm)\\n\\n\\tdates[\\\"calendar\\\"] = 'standard'\\n\\tdates[\\\"units\\\"] = 'days since 1900-01-01 00:00'\\n\\t\\n\\tdates[\\\"CFTime\\\"] = date2num(\\n\\t\\ttm, calendar=dates[\\\"calendar\\\"], units=dates[\\\"units\\\"])\\n\\n\\treturn dates\",\n \"def test(self):\\n y_list = []\\n y_hat_list = []\\n for ex_dict in ut.TEST_LIST:\\n y_list.append(ex_dict[1])\\n y_hat_list.append(self.predict(ex_dict[0]))\\n acc = ut.compute_accuracy(y_hat_list, y_list)\\n return y_hat_list, acc\",\n \"def train_predict(train_file_arg):\\n\\n # Load command line arguments\\n train_file = train_file_arg\\n parameter_file = \\\"prophet/lstm/trainingConfig.json\\\"\\n\\n # Load training parameters\\n params = json.loads(open(parameter_file).read())\\n\\n # Load time series dataset, and split it into train and test\\n x_train, y_train, x_test, _, x_test_raw, _,\\\\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\\n\\n # Build RNN (LSTM) model\\n lstm_layer = [1, params[\\\"window_size\\\"], params[\\\"hidden_unit\\\"], 1]\\n model = buildModel.rnn_lstm(lstm_layer, params)\\n\\n # Train RNN (LSTM) model with train set\\n model.fit(\\n x_train,\\n y_train,\\n batch_size=params[\\\"batch_size\\\"],\\n epochs=params[\\\"epochs\\\"],\\n validation_split=params[\\\"validation_split\\\"])\\n\\n # Check the model against test set\\n predicted = buildModel.predict_next_timestamp(model, x_test)\\n predicted_raw = []\\n for i in range(len(x_test_raw)):\\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\\n\\n # Predict next time stamp\\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\\n print(\\\"The next time stamp forecasting is: {}\\\".format(next_timestamp_raw))\\n\\n # Add 5 minutes for a new timestamp of predictions\\n last_datetime = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(last_datetime_epoch))\\n last_datetime = datetime.strptime(last_datetime, \\\"%Y-%m-%d %H:%M:%S\\\")\\n new_datetime = last_datetime + timedelta(seconds=300)\\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\\n new_datetime = new_datetime.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n # Concatenate datetime and price forecast\\n new_entry = \\\"coin_name,\\\" + str(new_datetime_epoch) + \\\",\\\" \\\\\\n + str(next_timestamp_raw) + \\\",coin_supply\\\" + \\\",coin_mc\\\" + \\\"\\\\n\\\"\\n\\n # Write to CSV file of new prediction\\n fd = open(train_file, \\\"a\\\")\\n fd.write(new_entry)\\n fd.close()\\n\\n # Return new prediction\\n return [new_datetime, str(next_timestamp_raw)]\",\n \"def read300yr(period):\\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\\n file300 = 'U10_1700-2000.nc'\\n filename = directory300 + file300\\n \\n data = Dataset(filename)\\n lat = data.variables['latitude'][:]\\n lon = data.variables['longitude'][:]\\n u10q = data.variables['U10'][:]\\n data.close()\\n \\n ### Reshape in year/month\\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\\n \\n ### Calculate over particular months\\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\\n \\n ### Slice U10 at 65N\\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\\n lat = lat[latq].squeeze()\\n u10 = u10[:,latq,:].squeeze()\\n \\n ### Take zonal mean \\n u10z = np.nanmean(u10,axis=1)\\n \\n ### Remove missing data\\n mask = np.where(u10z > -1e5)[0]\\n \\n ### Detrend\\n u10zdt = sss.detrend(u10z[mask],type='linear')\\n \\n return lat,lon,u10zdt\",\n \"def parseAndPopulateTrainRunningDates(jsonData, trainNumber):\\n if not jsonData:\\n logger.error(\\\"TrainNumber[%s] doesn't run on any day.\\\", trainNumber)\\n return[]\\n\\n returneddata = json.loads(jsonData)\\n jsonLogger.info(\\\"Days Data[%s] for TrainNumber[%s] *** \\\",returneddata,trainNumber)\\n days = {}\\n\\n if returneddata[\\\"response_code\\\"]==200:\\n trainData = returneddata[\\\"train\\\"]\\n daysData = trainData[\\\"days\\\"]\\n if daysData:\\n for day in trainData[\\\"days\\\"]:\\n days[day[\\\"day-code\\\"]]=day[\\\"runs\\\"]\\n\\n if len(days) == 0:\\n logger.error(\\\"TrainNumber[%s] doesn't run on any day.\\\", trainNumber)\\n return []\\n\\n models.addRunningDaysToTrain(days, trainNumber)\",\n \"def buildAndTrain(trainingData):\\n\\tname = trainingData.drop(['count', 'casual', 'registered'], axis=1).columns\\n\\ttarget = trainingData['count'].values\\n\\tfeature = trainingData.drop(['count', 'casual', 'registered'], axis=1).values\\n\\t# feature scaling\\n\\tfeature_scaled = preprocessing.scale(feature)\\n\\t# 0.5 cross validate\\n\\tcv = cross_validation.ShuffleSplit(len(feature_scaled), n_iter=5, test_size=0.2, random_state=0)\\n\\t# build model, then training and get accuracy of it\\n\\tprint('\\\\n---------岭回归结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregLR = linear_model.Ridge().fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregLR.score(feature_scaled[train], target[train]),\\n\\t\\t regLR.score(feature_scaled[test], target[test])))\\n\\tprint('\\\\n---------svm结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregSvm = svm.SVR().fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregSvm.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregSvm.score(feature_scaled[test], target[test])))\\n\\tprint('\\\\n---------随机森林结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregRF = RandomForestRegressor(n_estimators=100).fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRF.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRF.score(feature_scaled[test], target[test])))\\n\\t# reduce some low correction feature\\n\\tfeatureReduced = trainingData.drop(['count', 'casual', 'registered', 'holiday', 'workingday', 'day'], axis=1).values\\n\\tfeatureReduced_scaled = preprocessing.scale(featureReduced)\\n\\tprint('\\\\n---------减少特征维度以避免过拟合后的随机森林结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregRFImpr = RandomForestRegressor(n_estimators=100).fit(featureReduced_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFImpr.score(featureReduced_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFImpr.score(featureReduced_scaled[test], target[test])))\\n\\t# use grid search algorithm to improve random forest regression\\n\\tX_train, X_test, y_train, y_test = cross_validation.train_test_split(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfeature_scaled, target, test_size=0.2, random_state=0)\\n\\ttuned_parameters = [{'n_estimators': [10,100,500], 'max_depth': [2,3,4,5,6,7,8,9,10]}]\\n\\tscores = ['r2']\\n\\n\\tfor score in scores:\\n\\t\\tprint(score)\\n\\t\\tclf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)\\n\\t\\tclf.fit(X_train, y_train)\\n\\t\\tprint(clf.best_estimator_)\\n\\t\\tprint('each parameter combination is ')\\n\\t\\tfor params, mean_score, scores in clf.grid_scores_:\\n\\t\\t\\tprint('{0:.3f} (+/-{1:.03f}) for {2}'.format(mean_score, scores.std()/2, params))\\n\\n\\tprint('--------最优参数下的随机森林结果--------')\\n\\tfor train, test in cv:\\n\\t\\tregRFBest = RandomForestRegressor(n_estimators=100, max_depth=10).fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFBest.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFBest.score(feature_scaled[test], target[test])))\\n\\treturn regRFBest, feature_scaled, target\",\n \"def prepare_training_data(consumption_series, lag):\\r\\n # scale training data\\r\\n scaler = MinMaxScaler(feature_range=(-1, 1))\\r\\n consumption_vals = scaler.fit_transform(consumption_series.values.reshape(-1, 1))\\r\\n \\r\\n # convert consumption series to lagged features\\r\\n consumption_lagged = create_lagged_features(consumption_vals, lag=lag)\\r\\n\\r\\n # X, y format taking the first column (original time series) to be the y\\r\\n X = consumption_lagged.drop('consumption', axis=1).values\\r\\n y = consumption_lagged.consumption.values\\r\\n \\r\\n # keras expects 3 dimensional X\\r\\n X = X.reshape(X.shape[0], 1, X.shape[1])\\r\\n \\r\\n return X, y, scaler\",\n \"def clean_features(test_data, train_data, undef):\\n \\n test_set = test_data\\n train_set = train_data\\n \\n # Preprocessing of the four jet datasets\\n for jet in range(4):\\n \\n # Remove columns full of undefined values (-999.0 in the datase)\\n train_set[jet][train_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\\n train_set[jet] = train_set[jet][:, ~np.all(np.isnan(train_set[jet]), axis=0)]\\n test_set[jet][test_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\\n test_set[jet] = test_set[jet][:, ~np.all(np.isnan(test_set[jet]), axis=0)]\\n\\n # Remove columns without standard deviation to remove column full of the same value\\n train_id_pred = train_set[jet][:,0:2]\\n train_features = train_set[jet][:,2:]\\n train_features = train_features[:, np.nanstd(train_features, axis=0) != 0]\\n train_set[jet] = np.concatenate((train_id_pred, train_features), axis=1)\\n \\n # Extract prediction column of test before because it has std=0, and re-insert it after at the begining of the table\\n test_id_pred = test_set[jet][:,0:2]\\n test_features = test_set[jet][:,2:]\\n test_features = test_features[:, np.nanstd(test_features, axis=0) != 0]\\n test_set[jet] = np.concatenate((test_id_pred, test_features), axis=1)\\n \\n return test_set, train_set\",\n \"def data_read(variable):\\t\\t\\r\\n\\tdef day2datetime(scenario,days):\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t# convert days from a reference into int datetime \\r\\n\\t\\t# do not take leap years into account\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\tdate_int = np.empty((len(days)));date_int[:]=np.nan\\r\\n\\t\\tif scenario =='T1970C': start_year =1970\\r\\n\\t\\telse: start_year =2010\\r\\n\\t\\tstart =(start_year*365)\\r\\n\\t\\tith=0\\t\\r\\n\\t\\tfor iday in days:\\r\\n\\t\\t\\tmonth_days =np.array([31,28,31,30,31,30,31,31,30,31,30,31])\\r\\n\\t\\t\\tcalendar_days = np.array([0,31,59,90,120,151,181,212,243,273,304,334,365])\\r\\n\\t\\t\\ttotal_days = int(iday) + start; \\r\\n\\t\\t\\tyear = total_days//365; \\r\\n\\t\\t\\tremainder = total_days%365\\r\\n\\t\\t\\tif remainder ==0: year=year-1;month=12;day=31\\r\\n\\t\\t\\telse: \\r\\n\\t\\t\\t\\tmonth = 1+[layer for layer in range(len(calendar_days)) if calendar_days[layer]< remainder and calendar_days[layer+1]>=remainder][0]\\r\\n\\t\\t\\t\\tday = int(remainder - calendar_days[month-1])\\r\\n\\t\\t\\t\\tif day == 0: day = month_days[month-1]\\r\\n\\t\\t\\tdate_int[ith] = year*10000+month*100+day\\r\\n\\t\\t\\tith=ith+1\\r\\n\\t\\treturn date_int.astype(int)\\r\\n\\t\\t\\r\\n\\tdef mon_mean2annual_mean(scenario,time,data):\\r\\n\\t\\tannual_mean=np.empty((30,192,288));annual_mean[:]=np.nan\\r\\n\\t\\tcalendar_day = np.array([31,28,31,30,31,30,31,31,30,31,30,31])\\r\\n\\t\\tif scenario=='T1970RCP':\\r\\n\\t\\t\\tyear_series = range(2020,2050)\\r\\n\\t\\telif scenario=='EdgEne':\\r\\n\\t\\t\\tyear_series = range(2200,2230)\\r\\n\\t\\telif scenario=='Edg70GO':\\r\\n\\t\\t\\tyear_series = range(2070,2100)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tyear_series = range(2130,2160)\\r\\n\\t\\tfor iyear in year_series:\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif (iyear == year_series[0] and time[0]//100 >= year_series[0] *100+1):\\r\\n\\t\\t\\t\\tlayer_b=0\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tlayer_b = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+1][0] #June01\\r\\n\\t\\t\\tif (iyear == year_series[-1] and time[-1]//100 <= year_series[-1] *100+12):\\r\\n\\t\\t\\t\\tlayer_e=-2\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tlayer_e = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+12][0] #August 31\\r\\n\\t\\t\\tdata_cache = data[layer_b:layer_e+1,:,:]\\r\\n\\t\\t\\tannual_mean[iyear-year_series[0],:,:] = stats.nanmean(data_cache,axis=0)\\r\\n\\t\\treturn annual_mean\\r\\n\\r\\n\\tdef data_netcdf(scenario,variable):\\r\\n\\t\\tinput_path ='/exports/csce/datastore/geos/users/s1667168/CESM_EDGAR/ModelOutput/FullCp/'\\r\\n\\t\\tvar_path = input_path+scenario+'/mon/atm/'+scenario+'.atm.mon.'+variable+'.nc'\\r\\n\\t\\t# print var_path\\r\\n\\t\\tnc_fid = nc4.Dataset(var_path,mode='r')\\r\\n\\t\\tlat = nc_fid.variables['lat'][:]\\r\\n\\t\\tlon = nc_fid.variables['lon'][:]\\r\\n\\t\\tdays = nc_fid.variables['time'][:]; time = day2datetime(scenario,days);#print time\\r\\n\\t\\tif variable ==\\\"VQ\\\" or variable == \\\"VT\\\":\\r\\n\\t\\t\\tdata = np.nanmean(nc_fid.variables[variable][:,23:30,:,:],axis=1) # 850hpa\\r\\n\\t\\telse:\\r\\n\\t\\t\\tdata = nc_fid.variables[variable][:]#-273.15\\r\\n\\t\\tnc_fid.close()\\r\\n\\t\\tvar40map = mon_mean2annual_mean(scenario,time,data)\\r\\n\\t\\treturn lon,lat,var40map\\r\\n\\t\\r\\n\\tlon,lat,Edg70GO = data_netcdf('Edg70GO',variable)\\r\\n\\t_,_,T1970 = data_netcdf('T1970RCP',variable)\\r\\n\\t_,_,EdgRef = data_netcdf('EdgRef',variable)\\r\\n\\t_,_,Edg70Oz = data_netcdf('Edg70Oz',variable)\\r\\n\\t_,_,EdgEne = data_netcdf('EdgEne',variable)\\r\\n\\t_,_,EdgTech = data_netcdf('EdgTech',variable)\\r\\n\\treturn lon,lat,T1970,Edg70GO,Edg70Oz,EdgRef,EdgEne,EdgTech\",\n \"def test(self):\\n with torch.no_grad():\\n self.model.eval()\\n p10_forecast, p10_forecast, p90_forecast, target = None, None, None, None\\n\\n t = time()\\n for step, sample in enumerate(self.test_loader):\\n\\n # Hide future predictions from input vector, set to 0 (or 1) values where timestep > encoder_steps\\n steps = self.cnf.all_params['num_encoder_steps']\\n pred_len = sample['outputs'].shape[1]\\n x = sample['inputs'].float().to(self.cnf.device)\\n x[:, steps:, 0] = 1\\n\\n # Feed input to the model\\n if self.cnf.all_params[\\\"model\\\"] == \\\"transformer\\\" or self.cnf.all_params[\\\"model\\\"] == \\\"grn_transformer\\\":\\n\\n # Auto-regressive prediction\\n for i in range(pred_len):\\n output = self.model.forward(x)\\n x[:, steps + i, 0] = output[:, i, 1]\\n output = self.model.forward(x)\\n\\n elif self.cnf.all_params[\\\"model\\\"] == \\\"tf_transformer\\\":\\n output, _, _ = self.model.forward(x)\\n else:\\n raise NameError\\n\\n output = output.squeeze()\\n y, y_pred = sample['outputs'].squeeze().float().to(self.cnf.device), output\\n\\n # Compute loss\\n loss, _ = self.loss(y_pred, y)\\n smape = symmetric_mean_absolute_percentage_error(output[:, :, 1].detach().cpu().numpy(),\\n sample['outputs'][:, :, 0].detach().cpu().numpy())\\n\\n # De-Normalize to compute metrics\\n target = unnormalize_tensor(self.data_formatter, y, sample['identifier'][0][0])\\n p10_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 0], sample['identifier'][0][0])\\n p50_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 1], sample['identifier'][0][0])\\n p90_forecast = unnormalize_tensor(self.data_formatter, y_pred[..., 2], sample['identifier'][0][0])\\n\\n # Compute metrics\\n self.test_losses['p10'].append(self.loss.numpy_normalised_quantile_loss(p10_forecast, target, 0.1))\\n self.test_losses['p50'].append(self.loss.numpy_normalised_quantile_loss(p50_forecast, target, 0.5))\\n self.test_losses['p90'].append(self.loss.numpy_normalised_quantile_loss(p90_forecast, target, 0.9))\\n\\n self.test_loss.append(loss.item())\\n self.test_smape.append(smape)\\n\\n # Plot serie prediction\\n p1, p2, p3, target = np.expand_dims(p10_forecast, axis=-1), np.expand_dims(p50_forecast, axis=-1), \\\\\\n np.expand_dims(p90_forecast, axis=-1), np.expand_dims(target, axis=-1)\\n p = np.concatenate((p1, p2, p3), axis=-1)\\n plot_temporal_serie(p, target)\\n\\n # Log stuff\\n for k in self.test_losses.keys():\\n mean_test_loss = np.mean(self.test_losses[k])\\n print(f'\\\\t● AVG {k} Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\\n\\n # log log log\\n mean_test_loss = np.mean(self.test_loss)\\n mean_smape = np.mean(self.test_smape)\\n print(f'\\\\t● AVG Loss on TEST-set: {mean_test_loss:.6f} │ T: {time() - t:.2f} s')\\n print(f'\\\\t● AVG SMAPE on TEST-set: {mean_smape:.6f} │ T: {time() - t:.2f} s')\",\n \"def load_data():\\r\\n from sklearn.feature_extraction.text import CountVectorizer\\r\\n\\r\\n # Load the data\\r\\n\\r\\n with open(\\\"clean_real.txt\\\", 'r') as RealNews:\\r\\n RealStrAr = RealNews.read().split('\\\\n')\\r\\n\\r\\n with open(\\\"clean_fake.txt\\\", 'r') as FakeNews:\\r\\n FakeStrAr = FakeNews.read().split('\\\\n')\\r\\n\\r\\n # Preprocess it using a vectorizer\\r\\n\\r\\n MyCoolVectorizer = CountVectorizer()\\r\\n X = MyCoolVectorizer.fit_transform(RealStrAr + FakeStrAr)\\r\\n\\r\\n RealLabels = np.ones((len(RealStrAr), 1)) # means real\\r\\n FakeLabels = np.zeros((len(FakeStrAr), 1)) # means fake\\r\\n AllLabels = np.append(RealLabels, FakeLabels, axis=0)\\r\\n\\r\\n FinalTensor = np.append(X.toarray(), AllLabels, axis=1)\\r\\n\\r\\n # Randomize it and split it\\r\\n\\r\\n np.random.shuffle(FinalTensor)\\r\\n\\r\\n # divide and multiply by 2 just to make sure it's even\\r\\n ROUGHLY70 = 2 * ((FinalTensor.shape[0] * 70 / 100) / 2)\\r\\n ROUGHLY15 = (FinalTensor.shape[0] - ROUGHLY70) / 2\\r\\n\\r\\n # TEST SET VALIDATION SET TRAINING SET DICTIONARY\\r\\n return (FinalTensor[:ROUGHLY15], FinalTensor[ROUGHLY15 : 2 * ROUGHLY15], FinalTensor[-ROUGHLY70:], MyCoolVectorizer.get_feature_names())\",\n \"def fit_timeseries(xdates, ydata):\\n\\n pass\",\n \"def test_ARIMA_pred(self, whp_pandas):\\n test_class = Slug_Forecasting(whp_pandas.copy())\\n test_class.stationarity_check()\\n test_class.split_data()\\n test_class.ARIMA_model(1, 0, 1, show=False)\\n test_class.ARIMA_pred(pred_time=60)\\n assert hasattr(test_class, \\\"forecast\\\"), \\\"Forecast attribute must have been created\\\"\",\n \"def test():\\n temp_data = fetch_temp_data(\\n (\\\"https://opendata-download-metobs.smhi.se/api/version/\\\" +\\n \\\"latest/parameter/1/station/52350/period/latest-day/data.json\\\"))\\n data = temp_series(temp_data)\\n print(data)\",\n \"def test_train_test_split_uni_exo(load_uni_exo_data_target):\\n data, target = load_uni_exo_data_target\\n\\n ####################################\\n #### Continuous fh without Gaps ####\\n ####################################\\n\\n #### Integer fh ----\\n exp = TSForecastingExperiment()\\n fh = 12\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test.index == data.iloc[-fh:].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test.index == data.iloc[-fh:].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)\\n assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)\\n assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(1, 10) # 9 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [1, 2, 3, 4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)\\n\\n #################################\\n #### Continuous fh with Gaps ####\\n #################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.arange(7, 13) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n\\n ####################################\\n #### Discontinuous fh with Gaps ####\\n ####################################\\n\\n #### Numpy fh ----\\n exp = TSForecastingExperiment()\\n fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\\n\\n #### List fh ----\\n exp = TSForecastingExperiment()\\n fh = [4, 5, 6, 10, 11, 12]\\n exp.setup(data=data, target=target, fh=fh, seasonal_period=4, session_id=42)\\n assert np.all(exp.dataset.index == data.index)\\n assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)\\n # `test`` call still refers to y_test indices and not X_test indices\\n assert len(exp.test) == len(fh)\\n assert np.all(exp.X.index == data.index)\\n assert np.all(exp.y.index == data.index)\\n assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)\\n assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)\\n assert len(exp.y_test) == len(fh)\\n assert np.all(exp.dataset_transformed.index == data.index)\\n assert np.all(\\n exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.test_transformed) == len(fh)\\n assert np.all(exp.X_transformed.index == data.index)\\n assert np.all(exp.y_transformed.index == data.index)\\n assert np.all(\\n exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n # Exogenous variables will not have any gaps (only target has gaps)\\n assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)\\n assert np.all(\\n exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index\\n )\\n assert len(exp.y_test_transformed) == len(fh)\",\n \"def create_train_set(addition_to_filename = ''):\\n df = f.create_df()\\n print('original df size is ', df.shape)\\n print('original df columns ', df.columns)\\n\\n df = f.calculate_missing_prices_for_train_set(df)\\n print('df size after averaging price ', df.shape)\\n df = f.downcast_dtypes(df)\\n df = f.add_lag(all_data_df = df, df_to_add_lag= df, number_of_months=3)\\n df = f.add_days_stat(df)\\n\\n print(df.columns)\\n\\n # df = h.add_holidays(df)\\n # print('df size with holidays ', df.shape)\\n\\n timestr = time.strftime(\\\"%Y%m%d-%H%M%S\\\")\\n pickle.dump(df, open(f\\\"{timestr}_{addition_to_filename}_train.pickle.dat\\\", \\\"wb\\\"))\\n\\n # save feature names for further use\\n features_list = create_feature_names_list(df)\\n pickle.dump(features_list, open(f\\\"{timestr}_{addition_to_filename}_features.pickle.dat\\\", \\\"wb\\\"))\",\n \"def adf_test(series,title=''):\\n print(f'Augmented Dickey-Fuller Test: {title}')\\n result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\\n \\n labels = ['ADF test statistic','p-value','# lags used','# observations']\\n out = pd.Series(result[0:4],index=labels)\\n\\n for key,val in result[4].items():\\n out[f'critical value ({key})']=val\\n \\n print(out.to_string()) # .to_string() removes the line \\\"dtype: float64\\\"\\n \\n if result[1] <= 0.05:\\n print(\\\"Strong evidence against the null hypothesis\\\")\\n print(\\\"Reject the null hypothesis\\\")\\n print(\\\"Data has no unit root and is stationary\\\")\\n else:\\n print(\\\"Weak evidence against the null hypothesis\\\")\\n print(\\\"Fail to reject the null hypothesis\\\")\\n print(\\\"Data has a unit root and is non-stationary\\\")\",\n \"def test_change_of_year(self):\\n\\n input_ = [\\n self.indicator_record(date=datetime.date(2006, 11, 1), value=0.31),\\n self.indicator_record(date=datetime.date(2006, 12, 1), value=0.48),\\n ]\\n output = self.expander._ipca_from_15_expander(input_)\\n expected = self.indicator_record(date=datetime.date(2007, 1, 1), value=0.35)\\n actual = output[-1]\\n\\n self.assertEqual(expected, actual)\",\n \"def transform_data(data_orig):\\n\\tif isinstance(data_orig, str):\\n\\t\\tdata_orig = pd.read_csv(data_orig)\\n\\n\\tdata = data_orig\\n\\n\\tnum_rows,num_variables = data.shape\\n\\tall_columns = data.columns.tolist()\\n\\tclean_data(data,all_columns,ignore_na=False,fill_mode=\\\"prob\\\")\\n\\texpand_features(data)\\n\\tvariables = ['Pclass','Sex',\\\"Fare\\\",\\\"Age\\\",\\\"SibSp\\\",\\\"Parch\\\",\\\"Embarked\\\",\\\"Fam_size\\\",\\\\\\n\\t\\t\\t\\t \\\"cabin_no\\\",\\\"ticket_no\\\",\\\"friend\\\",\\\"Fare_person\\\",\\\"Child\\\"]\\n\\tX = pd.get_dummies(data[variables])\\n\\n\\t## normalise features to zero man and unit variance\\n\\tscaler = preprocessing.StandardScaler().fit(X)\\n\\tX_scaled = scaler.transform(X)\\n\\tX = pd.DataFrame(X_scaled, columns=X.columns)\\n\\n\\tif \\\"Survived\\\" in data.columns:\\n\\t\\ty = data['Survived']\\n\\telse:\\n\\t\\ty = None\\n\\n\\treturn X, y\",\n \"def prepare_data(train, test):\\n # change the name of the target column\\n train.rename(columns={\\\"revenue\\\": \\\"target\\\"}, inplace=True)\\n # map bool values to yes and no\\n train[\\\"Weekend\\\"] = train[\\\"Weekend\\\"].map({True: \\\"Yes\\\", False: \\\"No\\\"})\\n test[\\\"Weekend\\\"] = test[\\\"Weekend\\\"].map({True: \\\"Yes\\\", False: \\\"No\\\"})\\n # set the id col as index\\n train.set_index(\\\"id\\\", inplace=True)\\n test.set_index(\\\"id\\\", inplace=True)\\n\\n # seperate the fetures and the target\\n X_train = train.drop(\\\"target\\\", axis=1).copy()\\n y_train = train[\\\"target\\\"].copy()\\n X_test = test.copy()\\n\\n # select numerical and categorical columns\\n num_cols = X_train.select_dtypes(exclude=\\\"object\\\").columns.tolist()\\n cat_cols = X_train.select_dtypes(include=\\\"object\\\").columns.tolist()\\n\\n # numerical pipeline\\n num_pipe = make_pipeline(SimpleImputer(strategy=\\\"mean\\\"))\\n\\n # categorical pipeline\\n cat_pipe = make_pipeline(\\n SimpleImputer(strategy=\\\"constant\\\", fill_value=\\\"NA\\\"),\\n OneHotEncoder(handle_unknown=\\\"ignore\\\", sparse=False),\\n )\\n\\n # full pipeline for data preprocessing\\n full_pipe = ColumnTransformer(\\n [(\\\"num\\\", num_pipe, num_cols), (\\\"cat\\\", cat_pipe, cat_cols)]\\n )\\n return X_train, y_train, X_test, full_pipe\",\n \"def run_forecast(data_start_date='1980-01-01', forecast_end_date='2015-12-01',\\n test_date_start='2010-01-01', test_model=True):\\n \\n # 1. Prepare dataframe.\\n df = read_and_prepare_dataframe(start_date=data_start_date)\\n \\n # 2. Split dataframe into train and test.\\n y, y_train, y_test = time_series_train_test_split_y(df=df, test_date_start=test_date_start)\\n \\n # 3. Optimize parameters.\\n opt_order, opt_seasonal_order = optimize_sarima_parameters(y,\\n trend='c', start_p=0, start_q=0, test='adf', \\n max_order=6, stepwise=True, trace=False)\\n \\n # 4. Train SARIMAX model.\\n results = train_sarima_model(y_train=y_train, order=opt_order, seasonal_order=opt_seasonal_order, \\n plot_diagnostics=False,\\n trend='c', enforce_stationarity=False, enforce_invertibility=False)\\n \\n # 5. Test the model (optional).\\n if test_model:\\n test_sarima_model(y=y, y_test=y_test, results=results, dynamic=True)\\n \\n # 6. Make the forecast with the model and return the table containing the results.\\n table = forecast_temperature(y=y, results=results, end_date=forecast_end_date)\\n return table\",\n \"def time_series_train_test_split_y(df, test_date_start):\\n test_date_cutoff = datetime.strptime(test_date_start, '%Y-%m-%d')\\n train_date_cutoff = test_date_cutoff - timedelta(1)\\n \\n y = df['AverageTemperature']\\n y_train = y.loc[:train_date_cutoff]\\n y_test = y.loc[test_date_cutoff:]\\n \\n return y, y_train, y_test\",\n \"def random_forest_test_Data(strat_test_set):\\n logging.info(\\\"Random forest.....\\\")\\n X_test = strat_test_set.drop(\\\"median_house_value\\\", axis=1)\\n y_test = strat_test_set[\\\"median_house_value\\\"].copy()\\n X_test_num = X_test.drop(\\\"ocean_proximity\\\", axis=1)\\n imputer = SimpleImputer(strategy=\\\"median\\\")\\n imputer.fit(X_test_num)\\n X_test_prepared = imputer.transform(X_test_num)\\n X_test_prepared = pd.DataFrame(\\n X_test_prepared, columns=X_test_num.columns, index=X_test.index\\n )\\n X_test_prepared = feature_eng2(X_test_prepared, X_test)\\n return X_test_prepared, y_test\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6474548","0.5763003","0.56484705","0.56113726","0.55643433","0.555857","0.5496503","0.5457722","0.54512095","0.54455984","0.543785","0.5425412","0.5386848","0.5385404","0.53398514","0.5329046","0.5323245","0.5297824","0.5291544","0.5286219","0.527358","0.527273","0.526981","0.52569014","0.52423894","0.5238854","0.52379507","0.52333236","0.52116436","0.51950395","0.5184332","0.51821315","0.51690006","0.5168549","0.5164687","0.5143954","0.513661","0.51302075","0.51131815","0.5080785","0.50785357","0.5077895","0.5064106","0.50605714","0.50602853","0.5049824","0.5040351","0.5029063","0.50203145","0.50138414","0.5005227","0.5001643","0.49895105","0.49854422","0.49831042","0.49814424","0.49707443","0.4968547","0.4968255","0.49600545","0.49534696","0.4949862","0.4946039","0.49442378","0.494396","0.49409926","0.49387893","0.49336466","0.49327463","0.49279335","0.49265173","0.49242586","0.49236467","0.49187768","0.4917959","0.49132174","0.49105155","0.49098","0.49090174","0.49031985","0.4901305","0.4900944","0.48977754","0.48974812","0.48911792","0.4887537","0.48865065","0.48850948","0.48847488","0.48846507","0.4883863","0.48836154","0.48760366","0.48716873","0.48702574","0.48658118","0.48645318","0.4863552","0.48606077","0.48568478"],"string":"[\n \"0.6474548\",\n \"0.5763003\",\n \"0.56484705\",\n \"0.56113726\",\n \"0.55643433\",\n \"0.555857\",\n \"0.5496503\",\n \"0.5457722\",\n \"0.54512095\",\n \"0.54455984\",\n \"0.543785\",\n \"0.5425412\",\n \"0.5386848\",\n \"0.5385404\",\n \"0.53398514\",\n \"0.5329046\",\n \"0.5323245\",\n \"0.5297824\",\n \"0.5291544\",\n \"0.5286219\",\n \"0.527358\",\n \"0.527273\",\n \"0.526981\",\n \"0.52569014\",\n \"0.52423894\",\n \"0.5238854\",\n \"0.52379507\",\n \"0.52333236\",\n \"0.52116436\",\n \"0.51950395\",\n \"0.5184332\",\n \"0.51821315\",\n \"0.51690006\",\n \"0.5168549\",\n \"0.5164687\",\n \"0.5143954\",\n \"0.513661\",\n \"0.51302075\",\n \"0.51131815\",\n \"0.5080785\",\n \"0.50785357\",\n \"0.5077895\",\n \"0.5064106\",\n \"0.50605714\",\n \"0.50602853\",\n \"0.5049824\",\n \"0.5040351\",\n \"0.5029063\",\n \"0.50203145\",\n \"0.50138414\",\n \"0.5005227\",\n \"0.5001643\",\n \"0.49895105\",\n \"0.49854422\",\n \"0.49831042\",\n \"0.49814424\",\n \"0.49707443\",\n \"0.4968547\",\n \"0.4968255\",\n \"0.49600545\",\n \"0.49534696\",\n \"0.4949862\",\n \"0.4946039\",\n \"0.49442378\",\n \"0.494396\",\n \"0.49409926\",\n \"0.49387893\",\n \"0.49336466\",\n \"0.49327463\",\n \"0.49279335\",\n \"0.49265173\",\n \"0.49242586\",\n \"0.49236467\",\n \"0.49187768\",\n \"0.4917959\",\n \"0.49132174\",\n \"0.49105155\",\n \"0.49098\",\n \"0.49090174\",\n \"0.49031985\",\n \"0.4901305\",\n \"0.4900944\",\n \"0.48977754\",\n \"0.48974812\",\n \"0.48911792\",\n \"0.4887537\",\n \"0.48865065\",\n \"0.48850948\",\n \"0.48847488\",\n \"0.48846507\",\n \"0.4883863\",\n \"0.48836154\",\n \"0.48760366\",\n \"0.48716873\",\n \"0.48702574\",\n \"0.48658118\",\n \"0.48645318\",\n \"0.4863552\",\n \"0.48606077\",\n \"0.48568478\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5213099"},"document_rank":{"kind":"string","value":"28"}}},{"rowIdx":79782,"cells":{"query":{"kind":"string","value":"Activate the DHCP agent."},"document":{"kind":"string","value":"def run(self):\n self.sync_state()\n self.periodic_resync()\n self.lease_relay.start()\n self.notifications.run_dispatch(self)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def activate(self):\n super().activate()\n self._change_lease_time(self.runner.config.get(\"dhcp_lease_time\"))\n self._scan_finalize()","def server_activate(self):\n\t\tpass","def activate_controller(self):\n if self.controller_address:\n #print \"Activating controller...\"\n self.controller = Controller(\n self.controller_address,\n self.proxy_address,\n self.migrating)\n self.controller.switch = self\n else:\n print \"[WARNING] Controller undefined\"","def activate(self):\n super(Hipchap, self).activate()","def test_add_dhcp_agent_to_network(self):\n network_id = self._create_and_prepare_network_for_agent(\n self.agent['id'])\n\n with self.override_role():\n self.agents_client.add_dhcp_agent_to_network(\n self.agent['id'], network_id=network_id)\n # Clean up is not necessary and might result in 409 being raised.","def activateButtonClicked(self):\n print(\"trying to start process...\")\n subprocess.Popen(\"/usr/local/bin/g13d --config /usr/local/bin/defaults.bind\", shell=True)\n self.checkProcess()","def activate(connection, args):\n\n iface = sap.adt.Interface(connection, args.name)\n iface.activate()","def activate(self):\r\n self.update_enrollment(is_active=True)","def setup_dhcp_env(device):\n raise NotImplementedError","def activate(self):\n pass","def _activate(self):\n self.active = True","def set_agent_advertise_addr(self):\n if self.advertise_address[0] is not None:\n return\n\n if self.network_interface is None:\n ifaces = self.get_agent_network_interfaces()\n else:\n ifaces = [self.network_interface]\n\n attempts = 0\n while (attempts < self.ip_lookup_attempts):\n for iface in ifaces:\n found_ip = hardware.dispatch_to_managers('get_ipv4_addr',\n iface)\n if found_ip is not None:\n self.advertise_address = (found_ip,\n self.advertise_address[1])\n self.network_interface = iface\n return\n attempts += 1\n time.sleep(self.ip_lookup_sleep)\n\n raise errors.LookupAgentIPError('Agent could not find a valid IP '\n 'address.')","def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def idle_handler(self):\n if self.state == _STATE.INIT:\n self._activate()\n elif self.state == _STATE.ACTIVE:\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\n self.record_result('dhcp', state='run')\n self.dhcp_monitor.start()\n elif self.state == _STATE.BASE:\n self._base_start()","def activate():\n refresh()\n activate_connection_with_mainloop(get_uuid())","def activate(self):\n self._is_active = True","def renew_dhcp_lease(self):\n\t\tresponse = os.system(\"/sbin/dhclient -r;/sbin/dhclient\")\n\t\tif response != 0:\n\t\t\tprint \"Network restart failed. DHCP Lease failed.\"","def start(self):\n if self.isRunning():\n raise Exception('DhcpClientAlreadyStarted')\n cmd = ['sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S']\n if self._logger is not None:\n self._logger.debug('Running command ' + str(cmd))\n #self._slave_dhcp_client_proc = robot.libraries.Process.Process()\n #self._slave_dhcp_client_proc.start_process('sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S')\n self._slave_dhcp_client_proc = subprocess.Popen(cmd)#, stdout=open(os.devnull, 'wb'), stderr=subprocess.STDOUT)\n self._slave_dhcp_client_pid = self._slave_dhcp_client_proc.pid\n self.addSlavePid(self._slave_dhcp_client_proc.pid) # Add the PID of the child to the list of subprocesses (note: we get sudo's PID here, not the slave PID, that we will get later on via D-Bus (see RemoteDhcpClientControl.getPid())","def activate(self):\n super(Pfsense, self).activate()\n default_identifier = self.config.get('DEFAULT_IDENTIFIER_STR', '')\n if not default_identifier:\n self.log.warn('No default identifier set')\n\n self.default_identifier = self.build_identifier(default_identifier)\n self.thread = threading.Thread(target=log_thread, args=(self,))\n\n self.dns_cache = DNSCache()\n self.dns_cache.start()\n self.running = False","def activate(self):\n self.active = True","def activate(self):\n self.active = True","def activate(self):\n self.start()","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def doActivateAgent(registrar_ip: str, registrar_port: str, agent_id: str, key: bytes) -> bool:\n data = {\n \"auth_tag\": crypto.do_hmac(key, agent_id),\n }\n\n # The agent accesses the registrar without mTLS, meaning without client\n # certificate\n # TODO the registrar could be accessed using TLS, but without client\n # certificate verification. Currently it is accessed without TLS at all\n client = RequestsClient(f\"{registrar_ip}:{registrar_port}\", False)\n response = client.put(\n f\"/v{api_version}/agents/{agent_id}/activate\",\n data=json.dumps(data),\n )\n response_body = response.json()\n\n if response.status_code == 200:\n logger.info(\"Registration activated for agent %s.\", agent_id)\n return True\n\n logger.error(\"Error: unexpected http response code from Registrar Server: %s\", str(response.status_code))\n keylime_logging.log_http_response(logger, logging.ERROR, response_body)\n return False","def activate(self):\n raise NotImplementedError(\"Shouldn't be called\")","def activate(self):\n self.robot = self.behavior_system.robot\n self.cozmo = self.robot.cozmo\n\n self._start_loop()","def activate(wait, timeout, logger, client):\n\n if timeout and not wait:\n msg = \"'--timeout' was used without '--wait'.\"\n error = exceptions.CloudifyCliError(msg)\n error.possible_solutions = [\n \"Add the '--wait' flag to the command in order to wait.\"\n ]\n raise error\n\n logger.info('Entering maintenance mode...')\n client.maintenance_mode.activate()\n\n if wait:\n logger.info(\"Cloudify manager will enter Maintenance mode once \"\n \"there are no running or pending executions...\\n\")\n deadline = time.time() + timeout\n\n while True:\n if _is_timeout(timeout, deadline):\n raise exceptions.CloudifyCliError(\n \"Timed out while entering maintenance mode. \"\n \"Note that the manager is still entering maintenance mode\"\n \" in the background. You can run \"\n \"'cfy maintenance-mode status' to check the status.\")\n\n status_response = client.maintenance_mode.status()\n if status_response.status == MAINTENANCE_MODE_ACTIVE:\n logger.info('Manager is in maintenance mode.')\n logger.info('While in maintenance mode most requests will '\n 'be blocked.')\n return\n time.sleep(DEFAULT_TIMEOUT_INTERVAL)\n logger.info(\"Run 'cfy maintenance-mode status' to check the \"\n \"maintenance mode's status.\\n\")","def activate_account(self):\n self.driver.execute_script(\"window.scrollTo(0, 1000)\")\n self.click_on_element_by_css(tep.ACTIVATION_LINK)\n self.click_on_element_by_css(tep.ACTIVATION_BUTTON)","def will_activate(self):\n pass","async def activate(self, item: Any) -> None:\n await self.addr_conn.conn.segment_scan_completed_event.wait()\n # handle variables independently\n if (item in lcn_defs.Var) and (item != lcn_defs.Var.UNKNOWN):\n # wait until we know the software version\n await self.addr_conn.serial_known\n if self.addr_conn.software_serial >= 0x170206:\n timeout_msec = self.settings[\"MAX_STATUS_EVENTBASED_VALUEAGE_MSEC\"]\n else:\n timeout_msec = self.settings[\"MAX_STATUS_POLLED_VALUEAGE_MSEC\"]\n self.request_status_vars[item].set_timeout_msec(timeout_msec)\n self.request_status_vars[item].activate()\n elif item in lcn_defs.OutputPort:\n self.request_status_outputs[item.value].activate()\n elif item in lcn_defs.RelayPort:\n self.request_status_relays.activate()\n elif item in lcn_defs.MotorPort:\n self.request_status_relays.activate()\n elif item in lcn_defs.BinSensorPort:\n self.request_status_bin_sensors.activate()\n elif item in lcn_defs.LedPort:\n self.request_status_leds_and_logic_ops.activate()\n elif item in lcn_defs.Key:\n self.request_status_locked_keys.activate()","def do_activate(self, *args, **kwargs):\n self.register_signals()\n self.perform_setup()\n assert self.main_window\n self.main_window.show()\n self.hold()","def onStart(self, agent):\n\n pass","def activate(self):\n self._activate_event.set()\n self._ready_event.clear()\n if not self._ready_event.wait(settings.SELENIUM_TEST_SERVER_TIMEOUT):\n raise Exception('Test server hung. Timed out after %i seconds' % settings.SELENIUM_TEST_SERVER_TIMEOUT)","def ai(self):\n if not self.alive:\n return\n\n assert hasattr(self, 'net'), \"Bird has ai enabled but does not appear to have any agents\"\n\n self.birth_time += 1\n\n activation = self.net.activate(self.get_inputs())[0]\n\n # if activation > 1e3:\n # print('unusually high activation of', activation)\n\n if activation > 0.9:\n self.flap()","def init_acs_agent(self):\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)","def activate(self):\n super(Selenium, self).activate()","def start_advertising(self):\n self._periph.start_advertising(self._advertisement.advertising_data_bytes,\n scan_response=self._advertisement.scan_response_bytes)","def start(self):\n listener = eventlet.listen(cfg.CONF.dhcp_lease_relay_socket,\n family=socket.AF_UNIX)\n eventlet.spawn(eventlet.serve, listener, self._handler)","def activate(self) -> None:\n self._bot.inject_flows_from(self)\n self.is_activated = True","def activate(self):\n pass","def activate(self):\n if not self.is_active:\n self.is_active = True\n self.activated_at = datetime.datetime.utcnow()\n import messaging # avoid circular import\n messaging.send_activated_emails(self)\n self.save()","def activate(self):\n super(Pixiv_bot, self).activate()","def robotiq_set_activate(self, wait=True, timeout=3):\r\n return self._arm.robotiq_set_activate(wait=wait, timeout=timeout)","def test_add_remove_network_from_dhcp_agent(self):\n # The agent is now bound to the network, we can free the port\n self.ports_client.delete_port(self.port['id'])\n agent = dict()\n agent['agent_type'] = None\n body = self.admin_agents_client.list_agents()\n agents = body['agents']\n for a in agents:\n if a['agent_type'] == 'DHCP agent':\n agent = a\n break\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\n 'DHCP agent in agent list though dhcp_agent_scheduler'\n ' is enabled.')\n network = self.create_network()\n network_id = network['id']\n if self._check_network_in_dhcp_agent(network_id, agent):\n self._remove_network_from_dhcp_agent(network_id, agent)\n self._add_dhcp_agent_to_network(network_id, agent)\n else:\n self._add_dhcp_agent_to_network(network_id, agent)\n self._remove_network_from_dhcp_agent(network_id, agent)","def activate_task(self, task_id):\n if task_id is None:\n print('ERROR: task_id cannot be \"None\".')\n return False\n # Else, the task was specified\n if task_id in self.task_dict:\n self.task_dict[task_id].is_activated = True\n # Update the activation state\n self._update_activation_state()\n return True\n else:\n # Something wrong, agent was not in the dict\n print('ERROR: The task <%s> is not in the task_dict at agent <%d>.' % (str(task_id), self.agent_id))\n return False","def __init__(self):\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State","def activate_onu(self, onu):\n if self.olt.autoactivate:\n self.log.info('activate-onu', onu=onu)\n\n olt = self.olt\n adapter = self.adapter_agent\n channel_id = onu.onu_vid\n\n proxy = onu.proxy_address\n\n # NOTE: The following method will be deprecated. Use xPON\n adapter.child_device_detected(parent_device_id=olt.device_id,\n parent_port_no=self._port_no,\n child_device_type=onu.vendor_id,\n proxy_address=proxy,\n admin_state=AdminState.ENABLED,\n vlan=channel_id)","def activate(self):\n self.robot = self.behavior_system.robot\n self.cozmo = self.robot.cozmo\n\n # Play the animation\n try:\n self.angry_anim = self.cozmo.play_anim_trigger(cozmosdk.anim.Triggers.DriveStartAngry)\n self.angry_anim.on_completed(lambda evt, **kwargs: self._angry_anim_completed(evt))\n except:\n pass","def enter(self, env):\n env = self._find_env(env, new=True)\n env.add_agents(self)","def _on_switch_activate(\n self, entity: Union[str, dict], attribute: str, old: str, new: str, kwargs: dict\n ) -> None:\n self.log(\"No one home; not allowing switch to activate\")\n\n self.toggle(state=\"off\")","def activate(self, detector_name=None):\n if detector_name == None:\n dname = self.name()\n else:\n dname = detector_name\n self.detector_states[dname] = True","def _activate_ssh_coordinator(self, coordinator_constructor):\n self._has_ssh_devices = True\n self._ssh_coord = coordinator_constructor(self)\n\n return","async def enable(self) -> None:\n try:\n await self.adguard.request(\n \"parental/enable\", method=\"POST\", data=\"sensitivity=TEEN\"\n )\n except AdGuardHomeError as exception:\n raise AdGuardHomeError(\n \"Enabling AdGuard Home parental control failed\"\n ) from exception","def _set_activate(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name=\"activate\", rest_name=\"activate\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Allow exchange of route in the current family mode', u'cli-full-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"activate must be of a type compatible with empty\"\"\",\n 'defined-type': \"empty\",\n 'generated-type': \"\"\"YANGDynClass(base=YANGBool, is_leaf=True, yang_name=\"activate\", rest_name=\"activate\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Allow exchange of route in the current family mode', u'cli-full-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True)\"\"\",\n })\n\n self.__activate = t\n if hasattr(self, '_set'):\n self._set()","def _activate_upnp_coordinator(self, coordinator_constructor):\n\n self._has_upnp_devices = True \n self._upnp_coord = coordinator_constructor(self)\n\n return","def activate(self):\n if not self._env.enable_registration:\n return\n legacy_key = '{}:{}'.format(self._env.flask_host, self._env.flask_port)\n self._key = self._env.get('my_ident', legacy_key, 'microservice')\n LoopingCall(self.ping).start(5, now=False)","def activate(self):\n\t\tself.flash()\n\t\tself.do_action()","def activate(x):\n raise NotImplementedError()","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setup_start_agents = False","def do_activate(self):\n\n Gtk.Application.do_activate(self)\n self.initiate_plugins()\n self.other[\"menu_button\"].set_menu_model(self.prepare_menu())\n self.output_window.show_all()\n self.window.show_all()","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def affection_status_switch_on(self):\n self._affection_status_switch = True","def test_list_dhcp_agent_hosting_network(self):\n self.admin_networks_client.list_dhcp_agents_on_hosting_network(\n self.network['id'])","def agent_init(self):\n pass","def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\n self._ifname = ifname\n self._slave_dhcp_process = None\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\n self._new_lease_event = threading.Event() # At initialisation, event is cleared","def __init__(self, agent_id):\n # Properties of the agent\n #--------------------------------------#\n # The following parameters are mandatory\n self.agent_id = int(agent_id)\n\n # The states\n self.task_dict = dict() # Elements are {task_id:TASK(), ...}\n\n # Activation\n self.num_activated_task = 0\n self.is_activated = False # If there is a task being activated, this is set to True","def onSwitchActivate(self, button, state):\n\t\tcommand_string = 'id1 mav.waypoint_actuator switch_hold \\n'\n\t\tcomm.write(bytes(command_string, 'utf8'))","def setup_dhcpmeta_access(self):\n if cfg.CONF.NSX.agent_mode == config.AgentModes.AGENT:\n self._setup_rpc_dhcp_metadata()\n mod = nvp_rpc\n elif cfg.CONF.NSX.agent_mode == config.AgentModes.AGENTLESS:\n self._setup_nvp_dhcp_metadata()\n mod = nvp_svc\n self.handle_network_dhcp_access_delegate = (\n mod.handle_network_dhcp_access\n )\n self.handle_port_dhcp_access_delegate = (\n mod.handle_port_dhcp_access\n )\n self.handle_port_metadata_access_delegate = (\n mod.handle_port_metadata_access\n )\n self.handle_metadata_access_delegate = (\n mod.handle_router_metadata_access\n )","def activate(self, state, action):\n self.state = state\n return Explorer.activate(self, state, action)","def activate(self, state, action):\n self.state = state\n return Explorer.activate(self, state, action)","def enable(self):\n if not self.tm_started:\n for name, tm in self.air_traffic_manager.items():\n logging.debug(\"Starting tm %s\" % name)\n tm.start()\n tm_started = True\n\n logging.debug(\"Enabling switch %s\" % self.name)\n self.disabled = False","def do(self):\n call_command('activate-ssl')","def activate_voice_control(self):\n\t\t\n\t\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/restart_snips.sh'\n\t\tsubprocess.call([path_to_file])\n\t\tprint('Voice control restarted.')\n\n\t\t#set is_active of voice control instance to true and tell the user\n\t\tself.parent.is_active = True\n\t\tself.parent.send_message_to_TTS(\"Die Sprachsteuerung ist aktiv.\")","def tc_start(self, datapath, dpae_port):\n dpid = datapath.id\n self.logger.info(\"Starting TC to DPAE on datapath=%s, dpae_port=%s\",\n dpid, dpae_port)\n switch = self.switches[dpid]\n #*** Check if Active or Passive TC Mode:\n mode = self.main_policy.tc_policies.mode\n self.logger.info(\"TC mode=%s\", mode)\n #*** TBD, deal with context:\n context = self.context_default\n #*** Set up group table to send to DPAE:\n # NEEDS OVS 2.1 OR HIGHER SO COMMENTED OUT FOR THE MOMENT\n # ALSO NEEDS CODE THAT CAN CATER FOR MULTIPLE DPAE\n #switch.flowtables.add_group_dpae(out_port)\n\n if self.main_policy.identity.lldp:\n #*** Install FEs to send LLDP Identity indicators to DPAE:\n switch.flowtables.add_fe_iig_lldp(dpae_port)\n\n if self.main_policy.identity.dhcp:\n #*** Install FEs to send DHCP Identity indicators to DPAE:\n switch.flowtables.add_fe_iig_dhcp(dpae_port)\n\n if self.main_policy.identity.dns:\n #*** Install FEs to send DNS Identity indicators to DPAE:\n switch.flowtables.add_fe_iig_dns(dpae_port)\n\n if mode == 'active':\n #*** Install AMF entries for MACs we already know dest for:\n mac_list = switch.mactable.dump_macs(context)\n for mac in mac_list:\n self.logger.debug(\"Adding previously learned mac=%s dpid=%s \"\n \"dpae_port=%s to Active Mode Filter (amf)\", mac, dpid,\n dpae_port)\n switch.flowtables.add_fe_amf_macport_dst(dpae_port, mac)\n #*** Install FE to so packets returning from DPAE in active mode\n #*** bypass learning tables and go straight to treatment:\n switch.flowtables.add_fe_iim_dpae_active_bypass(dpae_port)\n\n #*** Add any general TC flows to send to DPAE if required by policy\n #*** (i.e. statistical or payload):\n switch.flowtables.add_fe_tc_dpae(\n self.main_policy.optimised_rules.get_rules(),\n dpae_port, mode)\n\n self.logger.info(\"TC started to DPAE on datapath=%s, dpae_port=%s\",\n dpid, dpae_port)\n _results = {\"status\": \"tc_started\",\n \"mode\": mode}\n return _results","def enable_host(self, name):\n from soppa.local import aslocal\n self.guest_ip = self.guest_ip()\n self.guest_host_name = name\n # Host (remote) change\n self.file.set_setting('/etc/hosts', '{0} {1}'.format('127.0.0.1', self.guest_host_name))\n # local change\n aslocal()\n self.file.set_setting('/etc/hosts', '{0} {1}'.format(self.guest_ip, name))","def activate(self):\n self.robot = self.behavior_system.robot\n self.cozmo = self.robot.cozmo\n\n active_drive = self.robot.drive_system.active_drive\n\n if active_drive.name == 'solo-drive':\n # Look for a toy/block\n self.search_behavior = self.cozmo.start_behavior(cozmosdk.behavior.BehaviorTypes.LookAroundInPlace)\n elif active_drive.name == 'social-drive':\n # Look for a face\n self.search_behavior = self.cozmo.start_behavior(cozmosdk.behavior.BehaviorTypes.FindFaces)","def activate_target(targetId: TargetID) -> dict:\n return {\"method\": \"Target.activateTarget\", \"params\": {\"targetId\": str(targetId)}}","def main_configure_ad():\n\n print \"------------- AD Configuration Started --------------\"\n network_settings()\n enable_ad()\n create_user_domain()\n create_groups()\n print \"------------- Setup Finished...Please Check idrac_conf.log for any failures --------------\"","def activate_pair(device_pair):\n command = 'pair_activate \"%s\"' % (device_pair.identifier,)\n _run_command(command)","def notify_activate(self, **kwargs):\n return self.notify(\"notify_activate\", **kwargs)","def _starting(self, sender, **kwargs):\n _log.info('Starting: {}'.format(self.__name__))\n self.vip.heartbeat.start()\n # _log.debug(self.vip.ping('', \"PING ROUTER?\").get(timeout=3))\n #\n q = query.Query(self.core)\n # TODO: Use all addresses for fallback, #114\n self._external_addresses = q.query(b'addresses').get(timeout=30)\n assert self._external_addresses\n self._serverkey = q.query(b'serverkey').get(timeout=30)\n\n _log.debug(\"external addresses are: {}\".format(\n self._external_addresses\n ))\n\n # self._local_address = q.query('local_address').get(timeout=30)\n # _log.debug('Local address is? {}'.format(self._local_address))\n _log.info('Registering jsonrpc and /.* routes with {}'.format(\n MASTER_WEB\n ))\n\n self.vip.rpc.call(MASTER_WEB, 'register_agent_route',\n r'^/jsonrpc.*',\n self.core.identity,\n 'jsonrpc').get(timeout=10)\n\n self.vip.rpc.call(MASTER_WEB, 'register_path_route', VOLTTRON_CENTRAL,\n r'^/.*', self._webroot).get(timeout=20)\n\n self.webaddress = self.vip.rpc.call(\n MASTER_WEB, 'get_bind_web_address').get(timeout=30)\n\n # Remove so that dynamic agents don't inherit the identity.\n os.environ.pop('AGENT_VIP_IDENTITY')","def __init__(self, a):\n # sanity check\n assert(isinstance(a, agent.Agent))\n \n # save the agent reference\n self.a = a\n \n self.a.tc_h1(\"CONNECT TESTS\")\n \n # create the host instance\n self.host = host.Host(a, STA)\n # create the access point instances\n self.ap1 = accesspoint.AccessPoint(a, AP1, channel=5, period=100, ssid=\"louis\")\n self.ap2 = accesspoint.AccessPoint(a, AP2, channel=11, period=100, ssid=\"louis\")\n\n # reset the host (resetting the MIBs)\n self.host.reset(True)\n\n # set the host MAC address\n self.host.dbg_macaddr()","def activate(self) -> bool:\n self.active = True\n return self._activate()","def land_on_asv(self, duration=rospy.Duration(600, 0)):\n start = rospy.Time.now()\n # First step approaching the launchpad (side or behind)\n offset = [\n 0.0,\n rospy.get_param(\"~dist_initial_landing\", -5.0),\n rospy.get_param(\"~altitude_initial_landing\", 10.)\n ]\n if \"behind\" in rospy.get_param(\"~landing_approach\", \"side\"):\n rospy.loginfo(\"Approaching ASV from behind ...\")\n else:\n offset = [offset[1], offset[0], offset[2]]\n rospy.loginfo(\"Approaching ASV from side ...\")\n self.follow_target('%s_launchpad' % self.namespace, True, offset, 0.,\n None, rospy.Duration(3, 0), duration)\n # Second step approaching the launchpad (side or behind)\n landed = self.ACTION_FAIL\n offset = [0.0, 0.0, rospy.get_param(\"~takeoff_altitude\", 10.)]\n rospy.loginfo(\"Trying to land on ASV ...\")\n while (rospy.Time.now() - start <\n duration) and not (rospy.is_shutdown()) and (\n not self.external_intervened) and (not self.landed):\n if landed == self.ACTION_SUCCESS:\n self._rate.sleep()\n continue\n duration = duration - (rospy.Time.now() - start)\n start = rospy.Time.now()\n self.follow_target('%s_launchpad' % self.namespace, True, offset,\n 0., None, rospy.Duration(3, 0), duration)\n rangefinder_ok = (self._min_range > -1)\n rangefinder_ok = rangefinder_ok and (\n (self.rangefinder - self._min_range) <\n (self.MINIMUM_ALTITUDE + 0.1))\n rel_alt_ok = (self._rel_alt[-1] <\n self.MINIMUM_ALTITUDE) and (self._min_range <= -1)\n if rel_alt_ok or rangefinder_ok:\n rospy.loginfo(\"UAV is landing ...\")\n duration = duration - (rospy.Time.now() - start)\n start = rospy.Time.now()\n landed = self.guided_mode(duration, mode='land')\n self._rate.sleep()\n elif offset[-1] <= self.MINIMUM_ALTITUDE:\n offset[-1] -= 0.1\n else:\n offset[-1] = self.MINIMUM_ALTITUDE\n # Status check to report\n landed = int(self.landed)\n if (rospy.Time.now() - start) > duration:\n landed = self.OUT_OF_DURATION\n if self.external_intervened:\n landed = self.EXTERNAL_INTERVENTION\n return landed","def turn_on(self, **kwargs):\n self._is_on = True\n self.schedule_update_ha_state()\n self.hass.data[ZIGATE_DOMAIN].action_onoff(self._device.addr,\n self._endpoint,\n 1)","def setup_dhcp_config(self, board_config):\n raise NotImplementedError","def guest_start(self, userid, timeout=0):\n LOG.info(\"Begin to power on vm %s\", userid)\n self._smtclient.guest_start(userid)\n if timeout > 0:\n self.wait_for_reachable(userid, timeout)\n if not self.is_reachable(userid):\n msg = (\"compute node is not able to connect to the virtual \"\n \"machine in %d seconds\" % timeout)\n raise exception.SDKGuestOperationError(rs=16, userid=userid,\n msg=msg)\n LOG.info(\"Complete power on vm %s\", userid)","def enable(self):\n try:\n self.bus.open(self.BUS_NUMBER)\n self.write(AntennaDeployerCommand.ARM_ANTS, 0x00)\n self.bus.close()\n return True\n except:\n return False","def enable_ad():\n for host in online_hosts:\n with settings(warn_only=True):\n\n result1 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgActiveDirectory -o cfgADEnable 1\")\n if result1.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Enabling Active Directory failed \")\n\n result2 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgActiveDirectory -o cfgADType 2\")\n if result2.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Setting of standard schema for AD failed \")","def bcp_attract_start(self, **kwargs):\n self.events.post('machineflow_Attract_start')","def ethernet_on(self):\n if not self.healthy:\n self.health_check()\n if not self._ethernet_switch:\n raise errors.CapabilityNotReadyError(\n device_name=self._device_name,\n msg=\"Not set up for ethernet switching.\")\n self._ethernet_switch.switch_power.power_on(self.ethernet_port_number)","def activate(self):\n self.robot = self.behavior_system.robot\n self.cozmo = self.robot.cozmo\n\n # Begin the behavior to roll the block over\n try:\n self.happy_anim = self.cozmo.play_anim_trigger(cozmosdk.anim.Triggers.AcknowledgeFaceNamed)\n self.happy_anim.on_completed(lambda evt, **kwargs: self._happy_animation_completed(evt))\n except:\n pass","def elActivateGraphicalLogin(self):\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\n commandSection = self.sectionByName(\"command\")\n commandSection.string = commandSection.string + \"\"\"\n#\n# XWindows configuration information.\nxconfig --startxonboot --defaultdesktop=GNOME\n\"\"\"\n return self","def enable(self):\n # Netmiko reports enable and config mode as being enabled\n if not self.native.check_enable_mode():\n self.native.enable()\n # Ensure device is not in config mode\n if self.native.check_config_mode():\n self.native.exit_config_mode()\n\n log.debug(\"Host %s: Device enabled.\", self.host)","def on_pre_enter(self):\n self.setup()\n self.start()","def on_activate(self) -> None:","def _activate(self, context, id_, resource_inventory, data):\n LOG.info(\"[%s] Activation started \" % id_)\n resource_driver = driver.load_resource_driver(\n resource_inventory[eon_const.EON_RESOURCE_TYPE])\n try:\n run_playbook = data.get(eon_const.RUN_PLAYBOOK, True)\n input_model_data = data.get(eon_const.INPUT_MODEL)\n resource_driver.activate(context,\n id_,\n data,\n resource_inventory=resource_inventory,\n input_model_info=input_model_data,\n run_playbook=run_playbook)\n LOG.info(\"[%s] Activation finished successfully\" % id_)\n try:\n message = {\"resource_id\": id_,\n \"resource_state\": eon_const.EON_RESOURCE_STATE_ACTIVATED,\n \"resource_details\": resource_inventory, }\n message_notifier.notify(context,\n message_notifier.EVENT_PRIORITY_INFO,\n message_notifier.EVENT_TYPE[\n eon_const.EON_RESOURCE_STATE_ACTIVATED],\n message)\n except Exception as ex:\n LOG.exception(\n \"Exception while notifying the message : %s\" % ex)\n except Exception as e:\n LOG.exception(e)\n try:\n self.db_api.delete_property(context, id_,\n eon_const.HYPERVISOR_ID)\n except exception.NotFound:\n pass # ignore\n raise exception.ActivationFailure(\n resource_name=resource_inventory['id'],\n err=str(e.message))","async def test_dhcp(hass: HomeAssistant) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN,\n data=dhcp.DhcpServiceInfo(\n ip=\"1.2.3.4\", macaddress=\"01:23:45:67:89:ab\", hostname=\"mock_hostname\"\n ),\n context={\"source\": config_entries.SOURCE_DHCP},\n )\n\n assert result.get(\"type\") == FlowResultType.FORM\n assert result.get(\"step_id\") == \"user\"","def activate(self, ext_ip, ext_port):\n self.sql_manager.port_update(self.id, external_ip=ext_ip, external_port=ext_port)\n self.external_port = ext_port\n self.external_ip = ext_ip","async def async_turn_on(self, **kwargs):\n self._wrap_device.device.set_duct_zone(self._zone, True)","def _connect(self) -> None:\n if self._agent_thread.is_alive():\n raise ValueError(\"Agent already running.\")\n self._agent_thread.start()\n\n while not self._agent.runtime.is_running: # check agent completely running\n time.sleep(0.01)"],"string":"[\n \"def activate(self):\\n super().activate()\\n self._change_lease_time(self.runner.config.get(\\\"dhcp_lease_time\\\"))\\n self._scan_finalize()\",\n \"def server_activate(self):\\n\\t\\tpass\",\n \"def activate_controller(self):\\n if self.controller_address:\\n #print \\\"Activating controller...\\\"\\n self.controller = Controller(\\n self.controller_address,\\n self.proxy_address,\\n self.migrating)\\n self.controller.switch = self\\n else:\\n print \\\"[WARNING] Controller undefined\\\"\",\n \"def activate(self):\\n super(Hipchap, self).activate()\",\n \"def test_add_dhcp_agent_to_network(self):\\n network_id = self._create_and_prepare_network_for_agent(\\n self.agent['id'])\\n\\n with self.override_role():\\n self.agents_client.add_dhcp_agent_to_network(\\n self.agent['id'], network_id=network_id)\\n # Clean up is not necessary and might result in 409 being raised.\",\n \"def activateButtonClicked(self):\\n print(\\\"trying to start process...\\\")\\n subprocess.Popen(\\\"/usr/local/bin/g13d --config /usr/local/bin/defaults.bind\\\", shell=True)\\n self.checkProcess()\",\n \"def activate(connection, args):\\n\\n iface = sap.adt.Interface(connection, args.name)\\n iface.activate()\",\n \"def activate(self):\\r\\n self.update_enrollment(is_active=True)\",\n \"def setup_dhcp_env(device):\\n raise NotImplementedError\",\n \"def activate(self):\\n pass\",\n \"def _activate(self):\\n self.active = True\",\n \"def set_agent_advertise_addr(self):\\n if self.advertise_address[0] is not None:\\n return\\n\\n if self.network_interface is None:\\n ifaces = self.get_agent_network_interfaces()\\n else:\\n ifaces = [self.network_interface]\\n\\n attempts = 0\\n while (attempts < self.ip_lookup_attempts):\\n for iface in ifaces:\\n found_ip = hardware.dispatch_to_managers('get_ipv4_addr',\\n iface)\\n if found_ip is not None:\\n self.advertise_address = (found_ip,\\n self.advertise_address[1])\\n self.network_interface = iface\\n return\\n attempts += 1\\n time.sleep(self.ip_lookup_sleep)\\n\\n raise errors.LookupAgentIPError('Agent could not find a valid IP '\\n 'address.')\",\n \"def dhcp_agent_network_add(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def idle_handler(self):\\n if self.state == _STATE.INIT:\\n self._activate()\\n elif self.state == _STATE.ACTIVE:\\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\\n self.record_result('dhcp', state='run')\\n self.dhcp_monitor.start()\\n elif self.state == _STATE.BASE:\\n self._base_start()\",\n \"def activate():\\n refresh()\\n activate_connection_with_mainloop(get_uuid())\",\n \"def activate(self):\\n self._is_active = True\",\n \"def renew_dhcp_lease(self):\\n\\t\\tresponse = os.system(\\\"/sbin/dhclient -r;/sbin/dhclient\\\")\\n\\t\\tif response != 0:\\n\\t\\t\\tprint \\\"Network restart failed. DHCP Lease failed.\\\"\",\n \"def start(self):\\n if self.isRunning():\\n raise Exception('DhcpClientAlreadyStarted')\\n cmd = ['sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S']\\n if self._logger is not None:\\n self._logger.debug('Running command ' + str(cmd))\\n #self._slave_dhcp_client_proc = robot.libraries.Process.Process()\\n #self._slave_dhcp_client_proc.start_process('sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S')\\n self._slave_dhcp_client_proc = subprocess.Popen(cmd)#, stdout=open(os.devnull, 'wb'), stderr=subprocess.STDOUT)\\n self._slave_dhcp_client_pid = self._slave_dhcp_client_proc.pid\\n self.addSlavePid(self._slave_dhcp_client_proc.pid) # Add the PID of the child to the list of subprocesses (note: we get sudo's PID here, not the slave PID, that we will get later on via D-Bus (see RemoteDhcpClientControl.getPid())\",\n \"def activate(self):\\n super(Pfsense, self).activate()\\n default_identifier = self.config.get('DEFAULT_IDENTIFIER_STR', '')\\n if not default_identifier:\\n self.log.warn('No default identifier set')\\n\\n self.default_identifier = self.build_identifier(default_identifier)\\n self.thread = threading.Thread(target=log_thread, args=(self,))\\n\\n self.dns_cache = DNSCache()\\n self.dns_cache.start()\\n self.running = False\",\n \"def activate(self):\\n self.active = True\",\n \"def activate(self):\\n self.active = True\",\n \"def activate(self):\\n self.start()\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def doActivateAgent(registrar_ip: str, registrar_port: str, agent_id: str, key: bytes) -> bool:\\n data = {\\n \\\"auth_tag\\\": crypto.do_hmac(key, agent_id),\\n }\\n\\n # The agent accesses the registrar without mTLS, meaning without client\\n # certificate\\n # TODO the registrar could be accessed using TLS, but without client\\n # certificate verification. Currently it is accessed without TLS at all\\n client = RequestsClient(f\\\"{registrar_ip}:{registrar_port}\\\", False)\\n response = client.put(\\n f\\\"/v{api_version}/agents/{agent_id}/activate\\\",\\n data=json.dumps(data),\\n )\\n response_body = response.json()\\n\\n if response.status_code == 200:\\n logger.info(\\\"Registration activated for agent %s.\\\", agent_id)\\n return True\\n\\n logger.error(\\\"Error: unexpected http response code from Registrar Server: %s\\\", str(response.status_code))\\n keylime_logging.log_http_response(logger, logging.ERROR, response_body)\\n return False\",\n \"def activate(self):\\n raise NotImplementedError(\\\"Shouldn't be called\\\")\",\n \"def activate(self):\\n self.robot = self.behavior_system.robot\\n self.cozmo = self.robot.cozmo\\n\\n self._start_loop()\",\n \"def activate(wait, timeout, logger, client):\\n\\n if timeout and not wait:\\n msg = \\\"'--timeout' was used without '--wait'.\\\"\\n error = exceptions.CloudifyCliError(msg)\\n error.possible_solutions = [\\n \\\"Add the '--wait' flag to the command in order to wait.\\\"\\n ]\\n raise error\\n\\n logger.info('Entering maintenance mode...')\\n client.maintenance_mode.activate()\\n\\n if wait:\\n logger.info(\\\"Cloudify manager will enter Maintenance mode once \\\"\\n \\\"there are no running or pending executions...\\\\n\\\")\\n deadline = time.time() + timeout\\n\\n while True:\\n if _is_timeout(timeout, deadline):\\n raise exceptions.CloudifyCliError(\\n \\\"Timed out while entering maintenance mode. \\\"\\n \\\"Note that the manager is still entering maintenance mode\\\"\\n \\\" in the background. You can run \\\"\\n \\\"'cfy maintenance-mode status' to check the status.\\\")\\n\\n status_response = client.maintenance_mode.status()\\n if status_response.status == MAINTENANCE_MODE_ACTIVE:\\n logger.info('Manager is in maintenance mode.')\\n logger.info('While in maintenance mode most requests will '\\n 'be blocked.')\\n return\\n time.sleep(DEFAULT_TIMEOUT_INTERVAL)\\n logger.info(\\\"Run 'cfy maintenance-mode status' to check the \\\"\\n \\\"maintenance mode's status.\\\\n\\\")\",\n \"def activate_account(self):\\n self.driver.execute_script(\\\"window.scrollTo(0, 1000)\\\")\\n self.click_on_element_by_css(tep.ACTIVATION_LINK)\\n self.click_on_element_by_css(tep.ACTIVATION_BUTTON)\",\n \"def will_activate(self):\\n pass\",\n \"async def activate(self, item: Any) -> None:\\n await self.addr_conn.conn.segment_scan_completed_event.wait()\\n # handle variables independently\\n if (item in lcn_defs.Var) and (item != lcn_defs.Var.UNKNOWN):\\n # wait until we know the software version\\n await self.addr_conn.serial_known\\n if self.addr_conn.software_serial >= 0x170206:\\n timeout_msec = self.settings[\\\"MAX_STATUS_EVENTBASED_VALUEAGE_MSEC\\\"]\\n else:\\n timeout_msec = self.settings[\\\"MAX_STATUS_POLLED_VALUEAGE_MSEC\\\"]\\n self.request_status_vars[item].set_timeout_msec(timeout_msec)\\n self.request_status_vars[item].activate()\\n elif item in lcn_defs.OutputPort:\\n self.request_status_outputs[item.value].activate()\\n elif item in lcn_defs.RelayPort:\\n self.request_status_relays.activate()\\n elif item in lcn_defs.MotorPort:\\n self.request_status_relays.activate()\\n elif item in lcn_defs.BinSensorPort:\\n self.request_status_bin_sensors.activate()\\n elif item in lcn_defs.LedPort:\\n self.request_status_leds_and_logic_ops.activate()\\n elif item in lcn_defs.Key:\\n self.request_status_locked_keys.activate()\",\n \"def do_activate(self, *args, **kwargs):\\n self.register_signals()\\n self.perform_setup()\\n assert self.main_window\\n self.main_window.show()\\n self.hold()\",\n \"def onStart(self, agent):\\n\\n pass\",\n \"def activate(self):\\n self._activate_event.set()\\n self._ready_event.clear()\\n if not self._ready_event.wait(settings.SELENIUM_TEST_SERVER_TIMEOUT):\\n raise Exception('Test server hung. Timed out after %i seconds' % settings.SELENIUM_TEST_SERVER_TIMEOUT)\",\n \"def ai(self):\\n if not self.alive:\\n return\\n\\n assert hasattr(self, 'net'), \\\"Bird has ai enabled but does not appear to have any agents\\\"\\n\\n self.birth_time += 1\\n\\n activation = self.net.activate(self.get_inputs())[0]\\n\\n # if activation > 1e3:\\n # print('unusually high activation of', activation)\\n\\n if activation > 0.9:\\n self.flap()\",\n \"def init_acs_agent(self):\\n raise DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)\",\n \"def activate(self):\\n super(Selenium, self).activate()\",\n \"def start_advertising(self):\\n self._periph.start_advertising(self._advertisement.advertising_data_bytes,\\n scan_response=self._advertisement.scan_response_bytes)\",\n \"def start(self):\\n listener = eventlet.listen(cfg.CONF.dhcp_lease_relay_socket,\\n family=socket.AF_UNIX)\\n eventlet.spawn(eventlet.serve, listener, self._handler)\",\n \"def activate(self) -> None:\\n self._bot.inject_flows_from(self)\\n self.is_activated = True\",\n \"def activate(self):\\n pass\",\n \"def activate(self):\\n if not self.is_active:\\n self.is_active = True\\n self.activated_at = datetime.datetime.utcnow()\\n import messaging # avoid circular import\\n messaging.send_activated_emails(self)\\n self.save()\",\n \"def activate(self):\\n super(Pixiv_bot, self).activate()\",\n \"def robotiq_set_activate(self, wait=True, timeout=3):\\r\\n return self._arm.robotiq_set_activate(wait=wait, timeout=timeout)\",\n \"def test_add_remove_network_from_dhcp_agent(self):\\n # The agent is now bound to the network, we can free the port\\n self.ports_client.delete_port(self.port['id'])\\n agent = dict()\\n agent['agent_type'] = None\\n body = self.admin_agents_client.list_agents()\\n agents = body['agents']\\n for a in agents:\\n if a['agent_type'] == 'DHCP agent':\\n agent = a\\n break\\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\\n 'DHCP agent in agent list though dhcp_agent_scheduler'\\n ' is enabled.')\\n network = self.create_network()\\n network_id = network['id']\\n if self._check_network_in_dhcp_agent(network_id, agent):\\n self._remove_network_from_dhcp_agent(network_id, agent)\\n self._add_dhcp_agent_to_network(network_id, agent)\\n else:\\n self._add_dhcp_agent_to_network(network_id, agent)\\n self._remove_network_from_dhcp_agent(network_id, agent)\",\n \"def activate_task(self, task_id):\\n if task_id is None:\\n print('ERROR: task_id cannot be \\\"None\\\".')\\n return False\\n # Else, the task was specified\\n if task_id in self.task_dict:\\n self.task_dict[task_id].is_activated = True\\n # Update the activation state\\n self._update_activation_state()\\n return True\\n else:\\n # Something wrong, agent was not in the dict\\n print('ERROR: The task <%s> is not in the task_dict at agent <%d>.' % (str(task_id), self.agent_id))\\n return False\",\n \"def __init__(self):\\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State\",\n \"def activate_onu(self, onu):\\n if self.olt.autoactivate:\\n self.log.info('activate-onu', onu=onu)\\n\\n olt = self.olt\\n adapter = self.adapter_agent\\n channel_id = onu.onu_vid\\n\\n proxy = onu.proxy_address\\n\\n # NOTE: The following method will be deprecated. Use xPON\\n adapter.child_device_detected(parent_device_id=olt.device_id,\\n parent_port_no=self._port_no,\\n child_device_type=onu.vendor_id,\\n proxy_address=proxy,\\n admin_state=AdminState.ENABLED,\\n vlan=channel_id)\",\n \"def activate(self):\\n self.robot = self.behavior_system.robot\\n self.cozmo = self.robot.cozmo\\n\\n # Play the animation\\n try:\\n self.angry_anim = self.cozmo.play_anim_trigger(cozmosdk.anim.Triggers.DriveStartAngry)\\n self.angry_anim.on_completed(lambda evt, **kwargs: self._angry_anim_completed(evt))\\n except:\\n pass\",\n \"def enter(self, env):\\n env = self._find_env(env, new=True)\\n env.add_agents(self)\",\n \"def _on_switch_activate(\\n self, entity: Union[str, dict], attribute: str, old: str, new: str, kwargs: dict\\n ) -> None:\\n self.log(\\\"No one home; not allowing switch to activate\\\")\\n\\n self.toggle(state=\\\"off\\\")\",\n \"def activate(self, detector_name=None):\\n if detector_name == None:\\n dname = self.name()\\n else:\\n dname = detector_name\\n self.detector_states[dname] = True\",\n \"def _activate_ssh_coordinator(self, coordinator_constructor):\\n self._has_ssh_devices = True\\n self._ssh_coord = coordinator_constructor(self)\\n\\n return\",\n \"async def enable(self) -> None:\\n try:\\n await self.adguard.request(\\n \\\"parental/enable\\\", method=\\\"POST\\\", data=\\\"sensitivity=TEEN\\\"\\n )\\n except AdGuardHomeError as exception:\\n raise AdGuardHomeError(\\n \\\"Enabling AdGuard Home parental control failed\\\"\\n ) from exception\",\n \"def _set_activate(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name=\\\"activate\\\", rest_name=\\\"activate\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Allow exchange of route in the current family mode', u'cli-full-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"activate must be of a type compatible with empty\\\"\\\"\\\",\\n 'defined-type': \\\"empty\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=YANGBool, is_leaf=True, yang_name=\\\"activate\\\", rest_name=\\\"activate\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Allow exchange of route in the current family mode', u'cli-full-command': None}}, namespace='urn:brocade.com:mgmt:brocade-bgp', defining_module='brocade-bgp', yang_type='empty', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__activate = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _activate_upnp_coordinator(self, coordinator_constructor):\\n\\n self._has_upnp_devices = True \\n self._upnp_coord = coordinator_constructor(self)\\n\\n return\",\n \"def activate(self):\\n if not self._env.enable_registration:\\n return\\n legacy_key = '{}:{}'.format(self._env.flask_host, self._env.flask_port)\\n self._key = self._env.get('my_ident', legacy_key, 'microservice')\\n LoopingCall(self.ping).start(5, now=False)\",\n \"def activate(self):\\n\\t\\tself.flash()\\n\\t\\tself.do_action()\",\n \"def activate(x):\\n raise NotImplementedError()\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.setup_start_agents = False\",\n \"def do_activate(self):\\n\\n Gtk.Application.do_activate(self)\\n self.initiate_plugins()\\n self.other[\\\"menu_button\\\"].set_menu_model(self.prepare_menu())\\n self.output_window.show_all()\\n self.window.show_all()\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def affection_status_switch_on(self):\\n self._affection_status_switch = True\",\n \"def test_list_dhcp_agent_hosting_network(self):\\n self.admin_networks_client.list_dhcp_agents_on_hosting_network(\\n self.network['id'])\",\n \"def agent_init(self):\\n pass\",\n \"def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\\n self._ifname = ifname\\n self._slave_dhcp_process = None\\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\\n self._new_lease_event = threading.Event() # At initialisation, event is cleared\",\n \"def __init__(self, agent_id):\\n # Properties of the agent\\n #--------------------------------------#\\n # The following parameters are mandatory\\n self.agent_id = int(agent_id)\\n\\n # The states\\n self.task_dict = dict() # Elements are {task_id:TASK(), ...}\\n\\n # Activation\\n self.num_activated_task = 0\\n self.is_activated = False # If there is a task being activated, this is set to True\",\n \"def onSwitchActivate(self, button, state):\\n\\t\\tcommand_string = 'id1 mav.waypoint_actuator switch_hold \\\\n'\\n\\t\\tcomm.write(bytes(command_string, 'utf8'))\",\n \"def setup_dhcpmeta_access(self):\\n if cfg.CONF.NSX.agent_mode == config.AgentModes.AGENT:\\n self._setup_rpc_dhcp_metadata()\\n mod = nvp_rpc\\n elif cfg.CONF.NSX.agent_mode == config.AgentModes.AGENTLESS:\\n self._setup_nvp_dhcp_metadata()\\n mod = nvp_svc\\n self.handle_network_dhcp_access_delegate = (\\n mod.handle_network_dhcp_access\\n )\\n self.handle_port_dhcp_access_delegate = (\\n mod.handle_port_dhcp_access\\n )\\n self.handle_port_metadata_access_delegate = (\\n mod.handle_port_metadata_access\\n )\\n self.handle_metadata_access_delegate = (\\n mod.handle_router_metadata_access\\n )\",\n \"def activate(self, state, action):\\n self.state = state\\n return Explorer.activate(self, state, action)\",\n \"def activate(self, state, action):\\n self.state = state\\n return Explorer.activate(self, state, action)\",\n \"def enable(self):\\n if not self.tm_started:\\n for name, tm in self.air_traffic_manager.items():\\n logging.debug(\\\"Starting tm %s\\\" % name)\\n tm.start()\\n tm_started = True\\n\\n logging.debug(\\\"Enabling switch %s\\\" % self.name)\\n self.disabled = False\",\n \"def do(self):\\n call_command('activate-ssl')\",\n \"def activate_voice_control(self):\\n\\t\\t\\n\\t\\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/restart_snips.sh'\\n\\t\\tsubprocess.call([path_to_file])\\n\\t\\tprint('Voice control restarted.')\\n\\n\\t\\t#set is_active of voice control instance to true and tell the user\\n\\t\\tself.parent.is_active = True\\n\\t\\tself.parent.send_message_to_TTS(\\\"Die Sprachsteuerung ist aktiv.\\\")\",\n \"def tc_start(self, datapath, dpae_port):\\n dpid = datapath.id\\n self.logger.info(\\\"Starting TC to DPAE on datapath=%s, dpae_port=%s\\\",\\n dpid, dpae_port)\\n switch = self.switches[dpid]\\n #*** Check if Active or Passive TC Mode:\\n mode = self.main_policy.tc_policies.mode\\n self.logger.info(\\\"TC mode=%s\\\", mode)\\n #*** TBD, deal with context:\\n context = self.context_default\\n #*** Set up group table to send to DPAE:\\n # NEEDS OVS 2.1 OR HIGHER SO COMMENTED OUT FOR THE MOMENT\\n # ALSO NEEDS CODE THAT CAN CATER FOR MULTIPLE DPAE\\n #switch.flowtables.add_group_dpae(out_port)\\n\\n if self.main_policy.identity.lldp:\\n #*** Install FEs to send LLDP Identity indicators to DPAE:\\n switch.flowtables.add_fe_iig_lldp(dpae_port)\\n\\n if self.main_policy.identity.dhcp:\\n #*** Install FEs to send DHCP Identity indicators to DPAE:\\n switch.flowtables.add_fe_iig_dhcp(dpae_port)\\n\\n if self.main_policy.identity.dns:\\n #*** Install FEs to send DNS Identity indicators to DPAE:\\n switch.flowtables.add_fe_iig_dns(dpae_port)\\n\\n if mode == 'active':\\n #*** Install AMF entries for MACs we already know dest for:\\n mac_list = switch.mactable.dump_macs(context)\\n for mac in mac_list:\\n self.logger.debug(\\\"Adding previously learned mac=%s dpid=%s \\\"\\n \\\"dpae_port=%s to Active Mode Filter (amf)\\\", mac, dpid,\\n dpae_port)\\n switch.flowtables.add_fe_amf_macport_dst(dpae_port, mac)\\n #*** Install FE to so packets returning from DPAE in active mode\\n #*** bypass learning tables and go straight to treatment:\\n switch.flowtables.add_fe_iim_dpae_active_bypass(dpae_port)\\n\\n #*** Add any general TC flows to send to DPAE if required by policy\\n #*** (i.e. statistical or payload):\\n switch.flowtables.add_fe_tc_dpae(\\n self.main_policy.optimised_rules.get_rules(),\\n dpae_port, mode)\\n\\n self.logger.info(\\\"TC started to DPAE on datapath=%s, dpae_port=%s\\\",\\n dpid, dpae_port)\\n _results = {\\\"status\\\": \\\"tc_started\\\",\\n \\\"mode\\\": mode}\\n return _results\",\n \"def enable_host(self, name):\\n from soppa.local import aslocal\\n self.guest_ip = self.guest_ip()\\n self.guest_host_name = name\\n # Host (remote) change\\n self.file.set_setting('/etc/hosts', '{0} {1}'.format('127.0.0.1', self.guest_host_name))\\n # local change\\n aslocal()\\n self.file.set_setting('/etc/hosts', '{0} {1}'.format(self.guest_ip, name))\",\n \"def activate(self):\\n self.robot = self.behavior_system.robot\\n self.cozmo = self.robot.cozmo\\n\\n active_drive = self.robot.drive_system.active_drive\\n\\n if active_drive.name == 'solo-drive':\\n # Look for a toy/block\\n self.search_behavior = self.cozmo.start_behavior(cozmosdk.behavior.BehaviorTypes.LookAroundInPlace)\\n elif active_drive.name == 'social-drive':\\n # Look for a face\\n self.search_behavior = self.cozmo.start_behavior(cozmosdk.behavior.BehaviorTypes.FindFaces)\",\n \"def activate_target(targetId: TargetID) -> dict:\\n return {\\\"method\\\": \\\"Target.activateTarget\\\", \\\"params\\\": {\\\"targetId\\\": str(targetId)}}\",\n \"def main_configure_ad():\\n\\n print \\\"------------- AD Configuration Started --------------\\\"\\n network_settings()\\n enable_ad()\\n create_user_domain()\\n create_groups()\\n print \\\"------------- Setup Finished...Please Check idrac_conf.log for any failures --------------\\\"\",\n \"def activate_pair(device_pair):\\n command = 'pair_activate \\\"%s\\\"' % (device_pair.identifier,)\\n _run_command(command)\",\n \"def notify_activate(self, **kwargs):\\n return self.notify(\\\"notify_activate\\\", **kwargs)\",\n \"def _starting(self, sender, **kwargs):\\n _log.info('Starting: {}'.format(self.__name__))\\n self.vip.heartbeat.start()\\n # _log.debug(self.vip.ping('', \\\"PING ROUTER?\\\").get(timeout=3))\\n #\\n q = query.Query(self.core)\\n # TODO: Use all addresses for fallback, #114\\n self._external_addresses = q.query(b'addresses').get(timeout=30)\\n assert self._external_addresses\\n self._serverkey = q.query(b'serverkey').get(timeout=30)\\n\\n _log.debug(\\\"external addresses are: {}\\\".format(\\n self._external_addresses\\n ))\\n\\n # self._local_address = q.query('local_address').get(timeout=30)\\n # _log.debug('Local address is? {}'.format(self._local_address))\\n _log.info('Registering jsonrpc and /.* routes with {}'.format(\\n MASTER_WEB\\n ))\\n\\n self.vip.rpc.call(MASTER_WEB, 'register_agent_route',\\n r'^/jsonrpc.*',\\n self.core.identity,\\n 'jsonrpc').get(timeout=10)\\n\\n self.vip.rpc.call(MASTER_WEB, 'register_path_route', VOLTTRON_CENTRAL,\\n r'^/.*', self._webroot).get(timeout=20)\\n\\n self.webaddress = self.vip.rpc.call(\\n MASTER_WEB, 'get_bind_web_address').get(timeout=30)\\n\\n # Remove so that dynamic agents don't inherit the identity.\\n os.environ.pop('AGENT_VIP_IDENTITY')\",\n \"def __init__(self, a):\\n # sanity check\\n assert(isinstance(a, agent.Agent))\\n \\n # save the agent reference\\n self.a = a\\n \\n self.a.tc_h1(\\\"CONNECT TESTS\\\")\\n \\n # create the host instance\\n self.host = host.Host(a, STA)\\n # create the access point instances\\n self.ap1 = accesspoint.AccessPoint(a, AP1, channel=5, period=100, ssid=\\\"louis\\\")\\n self.ap2 = accesspoint.AccessPoint(a, AP2, channel=11, period=100, ssid=\\\"louis\\\")\\n\\n # reset the host (resetting the MIBs)\\n self.host.reset(True)\\n\\n # set the host MAC address\\n self.host.dbg_macaddr()\",\n \"def activate(self) -> bool:\\n self.active = True\\n return self._activate()\",\n \"def land_on_asv(self, duration=rospy.Duration(600, 0)):\\n start = rospy.Time.now()\\n # First step approaching the launchpad (side or behind)\\n offset = [\\n 0.0,\\n rospy.get_param(\\\"~dist_initial_landing\\\", -5.0),\\n rospy.get_param(\\\"~altitude_initial_landing\\\", 10.)\\n ]\\n if \\\"behind\\\" in rospy.get_param(\\\"~landing_approach\\\", \\\"side\\\"):\\n rospy.loginfo(\\\"Approaching ASV from behind ...\\\")\\n else:\\n offset = [offset[1], offset[0], offset[2]]\\n rospy.loginfo(\\\"Approaching ASV from side ...\\\")\\n self.follow_target('%s_launchpad' % self.namespace, True, offset, 0.,\\n None, rospy.Duration(3, 0), duration)\\n # Second step approaching the launchpad (side or behind)\\n landed = self.ACTION_FAIL\\n offset = [0.0, 0.0, rospy.get_param(\\\"~takeoff_altitude\\\", 10.)]\\n rospy.loginfo(\\\"Trying to land on ASV ...\\\")\\n while (rospy.Time.now() - start <\\n duration) and not (rospy.is_shutdown()) and (\\n not self.external_intervened) and (not self.landed):\\n if landed == self.ACTION_SUCCESS:\\n self._rate.sleep()\\n continue\\n duration = duration - (rospy.Time.now() - start)\\n start = rospy.Time.now()\\n self.follow_target('%s_launchpad' % self.namespace, True, offset,\\n 0., None, rospy.Duration(3, 0), duration)\\n rangefinder_ok = (self._min_range > -1)\\n rangefinder_ok = rangefinder_ok and (\\n (self.rangefinder - self._min_range) <\\n (self.MINIMUM_ALTITUDE + 0.1))\\n rel_alt_ok = (self._rel_alt[-1] <\\n self.MINIMUM_ALTITUDE) and (self._min_range <= -1)\\n if rel_alt_ok or rangefinder_ok:\\n rospy.loginfo(\\\"UAV is landing ...\\\")\\n duration = duration - (rospy.Time.now() - start)\\n start = rospy.Time.now()\\n landed = self.guided_mode(duration, mode='land')\\n self._rate.sleep()\\n elif offset[-1] <= self.MINIMUM_ALTITUDE:\\n offset[-1] -= 0.1\\n else:\\n offset[-1] = self.MINIMUM_ALTITUDE\\n # Status check to report\\n landed = int(self.landed)\\n if (rospy.Time.now() - start) > duration:\\n landed = self.OUT_OF_DURATION\\n if self.external_intervened:\\n landed = self.EXTERNAL_INTERVENTION\\n return landed\",\n \"def turn_on(self, **kwargs):\\n self._is_on = True\\n self.schedule_update_ha_state()\\n self.hass.data[ZIGATE_DOMAIN].action_onoff(self._device.addr,\\n self._endpoint,\\n 1)\",\n \"def setup_dhcp_config(self, board_config):\\n raise NotImplementedError\",\n \"def guest_start(self, userid, timeout=0):\\n LOG.info(\\\"Begin to power on vm %s\\\", userid)\\n self._smtclient.guest_start(userid)\\n if timeout > 0:\\n self.wait_for_reachable(userid, timeout)\\n if not self.is_reachable(userid):\\n msg = (\\\"compute node is not able to connect to the virtual \\\"\\n \\\"machine in %d seconds\\\" % timeout)\\n raise exception.SDKGuestOperationError(rs=16, userid=userid,\\n msg=msg)\\n LOG.info(\\\"Complete power on vm %s\\\", userid)\",\n \"def enable(self):\\n try:\\n self.bus.open(self.BUS_NUMBER)\\n self.write(AntennaDeployerCommand.ARM_ANTS, 0x00)\\n self.bus.close()\\n return True\\n except:\\n return False\",\n \"def enable_ad():\\n for host in online_hosts:\\n with settings(warn_only=True):\\n\\n result1 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgActiveDirectory -o cfgADEnable 1\\\")\\n if result1.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Enabling Active Directory failed \\\")\\n\\n result2 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgActiveDirectory -o cfgADType 2\\\")\\n if result2.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Setting of standard schema for AD failed \\\")\",\n \"def bcp_attract_start(self, **kwargs):\\n self.events.post('machineflow_Attract_start')\",\n \"def ethernet_on(self):\\n if not self.healthy:\\n self.health_check()\\n if not self._ethernet_switch:\\n raise errors.CapabilityNotReadyError(\\n device_name=self._device_name,\\n msg=\\\"Not set up for ethernet switching.\\\")\\n self._ethernet_switch.switch_power.power_on(self.ethernet_port_number)\",\n \"def activate(self):\\n self.robot = self.behavior_system.robot\\n self.cozmo = self.robot.cozmo\\n\\n # Begin the behavior to roll the block over\\n try:\\n self.happy_anim = self.cozmo.play_anim_trigger(cozmosdk.anim.Triggers.AcknowledgeFaceNamed)\\n self.happy_anim.on_completed(lambda evt, **kwargs: self._happy_animation_completed(evt))\\n except:\\n pass\",\n \"def elActivateGraphicalLogin(self):\\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\\n commandSection = self.sectionByName(\\\"command\\\")\\n commandSection.string = commandSection.string + \\\"\\\"\\\"\\n#\\n# XWindows configuration information.\\nxconfig --startxonboot --defaultdesktop=GNOME\\n\\\"\\\"\\\"\\n return self\",\n \"def enable(self):\\n # Netmiko reports enable and config mode as being enabled\\n if not self.native.check_enable_mode():\\n self.native.enable()\\n # Ensure device is not in config mode\\n if self.native.check_config_mode():\\n self.native.exit_config_mode()\\n\\n log.debug(\\\"Host %s: Device enabled.\\\", self.host)\",\n \"def on_pre_enter(self):\\n self.setup()\\n self.start()\",\n \"def on_activate(self) -> None:\",\n \"def _activate(self, context, id_, resource_inventory, data):\\n LOG.info(\\\"[%s] Activation started \\\" % id_)\\n resource_driver = driver.load_resource_driver(\\n resource_inventory[eon_const.EON_RESOURCE_TYPE])\\n try:\\n run_playbook = data.get(eon_const.RUN_PLAYBOOK, True)\\n input_model_data = data.get(eon_const.INPUT_MODEL)\\n resource_driver.activate(context,\\n id_,\\n data,\\n resource_inventory=resource_inventory,\\n input_model_info=input_model_data,\\n run_playbook=run_playbook)\\n LOG.info(\\\"[%s] Activation finished successfully\\\" % id_)\\n try:\\n message = {\\\"resource_id\\\": id_,\\n \\\"resource_state\\\": eon_const.EON_RESOURCE_STATE_ACTIVATED,\\n \\\"resource_details\\\": resource_inventory, }\\n message_notifier.notify(context,\\n message_notifier.EVENT_PRIORITY_INFO,\\n message_notifier.EVENT_TYPE[\\n eon_const.EON_RESOURCE_STATE_ACTIVATED],\\n message)\\n except Exception as ex:\\n LOG.exception(\\n \\\"Exception while notifying the message : %s\\\" % ex)\\n except Exception as e:\\n LOG.exception(e)\\n try:\\n self.db_api.delete_property(context, id_,\\n eon_const.HYPERVISOR_ID)\\n except exception.NotFound:\\n pass # ignore\\n raise exception.ActivationFailure(\\n resource_name=resource_inventory['id'],\\n err=str(e.message))\",\n \"async def test_dhcp(hass: HomeAssistant) -> None:\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN,\\n data=dhcp.DhcpServiceInfo(\\n ip=\\\"1.2.3.4\\\", macaddress=\\\"01:23:45:67:89:ab\\\", hostname=\\\"mock_hostname\\\"\\n ),\\n context={\\\"source\\\": config_entries.SOURCE_DHCP},\\n )\\n\\n assert result.get(\\\"type\\\") == FlowResultType.FORM\\n assert result.get(\\\"step_id\\\") == \\\"user\\\"\",\n \"def activate(self, ext_ip, ext_port):\\n self.sql_manager.port_update(self.id, external_ip=ext_ip, external_port=ext_port)\\n self.external_port = ext_port\\n self.external_ip = ext_ip\",\n \"async def async_turn_on(self, **kwargs):\\n self._wrap_device.device.set_duct_zone(self._zone, True)\",\n \"def _connect(self) -> None:\\n if self._agent_thread.is_alive():\\n raise ValueError(\\\"Agent already running.\\\")\\n self._agent_thread.start()\\n\\n while not self._agent.runtime.is_running: # check agent completely running\\n time.sleep(0.01)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7367523","0.6174387","0.6084445","0.6073693","0.6065638","0.6057743","0.59695095","0.5860989","0.5859584","0.5761732","0.5749872","0.5738167","0.57222456","0.5659488","0.56560457","0.56503","0.5624306","0.56200856","0.56175464","0.5599799","0.5599799","0.5568772","0.5525943","0.55123496","0.54791677","0.5470031","0.54410726","0.5431027","0.5415682","0.54104966","0.53850955","0.53793645","0.5368698","0.5355466","0.53439313","0.53368175","0.5331922","0.53300166","0.53174454","0.53140026","0.53004766","0.5299216","0.5279269","0.52547127","0.52523845","0.5242401","0.5235089","0.5229413","0.52187103","0.52129406","0.52082795","0.51613796","0.5149761","0.51454973","0.5118976","0.5111114","0.51089776","0.5096302","0.50958925","0.50914097","0.5088005","0.50822526","0.5052558","0.50459844","0.50280195","0.50234306","0.50225514","0.50215274","0.5012976","0.5012976","0.5005341","0.5004856","0.49944243","0.49711508","0.49705344","0.49704805","0.49632353","0.49576974","0.4949281","0.4947681","0.49254075","0.4918236","0.4917748","0.49116808","0.49037814","0.49004453","0.48969844","0.48920617","0.488529","0.48814666","0.48787418","0.48780617","0.48768646","0.48753884","0.48745236","0.4866791","0.48667625","0.48652944","0.4864389","0.48641542","0.4856884"],"string":"[\n \"0.7367523\",\n \"0.6174387\",\n \"0.6084445\",\n \"0.6073693\",\n \"0.6065638\",\n \"0.6057743\",\n \"0.59695095\",\n \"0.5860989\",\n \"0.5859584\",\n \"0.5761732\",\n \"0.5749872\",\n \"0.5738167\",\n \"0.57222456\",\n \"0.5659488\",\n \"0.56560457\",\n \"0.56503\",\n \"0.5624306\",\n \"0.56200856\",\n \"0.56175464\",\n \"0.5599799\",\n \"0.5599799\",\n \"0.5568772\",\n \"0.5525943\",\n \"0.55123496\",\n \"0.54791677\",\n \"0.5470031\",\n \"0.54410726\",\n \"0.5431027\",\n \"0.5415682\",\n \"0.54104966\",\n \"0.53850955\",\n \"0.53793645\",\n \"0.5368698\",\n \"0.5355466\",\n \"0.53439313\",\n \"0.53368175\",\n \"0.5331922\",\n \"0.53300166\",\n \"0.53174454\",\n \"0.53140026\",\n \"0.53004766\",\n \"0.5299216\",\n \"0.5279269\",\n \"0.52547127\",\n \"0.52523845\",\n \"0.5242401\",\n \"0.5235089\",\n \"0.5229413\",\n \"0.52187103\",\n \"0.52129406\",\n \"0.52082795\",\n \"0.51613796\",\n \"0.5149761\",\n \"0.51454973\",\n \"0.5118976\",\n \"0.5111114\",\n \"0.51089776\",\n \"0.5096302\",\n \"0.50958925\",\n \"0.50914097\",\n \"0.5088005\",\n \"0.50822526\",\n \"0.5052558\",\n \"0.50459844\",\n \"0.50280195\",\n \"0.50234306\",\n \"0.50225514\",\n \"0.50215274\",\n \"0.5012976\",\n \"0.5012976\",\n \"0.5005341\",\n \"0.5004856\",\n \"0.49944243\",\n \"0.49711508\",\n \"0.49705344\",\n \"0.49704805\",\n \"0.49632353\",\n \"0.49576974\",\n \"0.4949281\",\n \"0.4947681\",\n \"0.49254075\",\n \"0.4918236\",\n \"0.4917748\",\n \"0.49116808\",\n \"0.49037814\",\n \"0.49004453\",\n \"0.48969844\",\n \"0.48920617\",\n \"0.488529\",\n \"0.48814666\",\n \"0.48787418\",\n \"0.48780617\",\n \"0.48768646\",\n \"0.48753884\",\n \"0.48745236\",\n \"0.4866791\",\n \"0.48667625\",\n \"0.48652944\",\n \"0.4864389\",\n \"0.48641542\",\n \"0.4856884\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79783,"cells":{"query":{"kind":"string","value":"Invoke an action on a DHCP driver instance."},"document":{"kind":"string","value":"def call_driver(self, action, network):\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n try:\n # the Driver expects something that is duck typed similar to\n # the base models.\n driver = self.dhcp_driver_cls(self.conf,\n network,\n self.conf.root_helper,\n self.device_manager,\n namespace)\n getattr(driver, action)()\n return True\n\n except Exception, e:\n self.needs_resync = True\n LOG.exception('Unable to %s dhcp.' % action)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def device_action(host, details, action):\n if details:\n pprint(cs.get_device_details(host))\n if action:\n pprint(cs.device_action(host, action))","def device_action(self, client, action):\r\n client.deviceAction(action)","def _RunDHCPCD(self, **kwargs):\n del kwargs\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\n interface=self.interface)\n # -K: Don't receive link messages for carrier status. You should\n # only have to use this with buggy device drivers or running\n # dhcpcd through a network manager.\n # -c: Location to the hooks file. If the default location happens to be\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\n timeout=self._dhcp_timeout,\n interface=self.interface)\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\n timeout=self._dhcp_timeout,\n cmd=dhcp_command)\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\n\n logging.info('Killing any existing dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n logging.info('Clearing any existing ifconfig networks...')\n self._device.Call(clear_ifconfig_command)\n\n logging.info('Starting dhcpcd...')\n self._device.CheckCall(dhcp_timeout_command)\n\n logging.info('Verifying IP address...')\n ip = self._LeasedIP()\n if not ip:\n self._device.Call(force_kill_command)\n raise WiFiError('DHCP bind failed')\n logging.info('Success: bound to IP %s', ip)\n\n yield ip # We have bound an IP; yield back to the caller.\n\n logging.info('Killing any remaining dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n yield # We have released the IP.","def dhcp_callback(self, state, target_mac=None, target_ip=None, exception=None):\n self.record_result('dhcp', info=target_mac, ip=target_ip, state=state, exception=exception)\n self.target_mac = target_mac\n self.target_ip = target_ip\n if exception:\n self._state_transition(_STATE.ERROR, _STATE.DHCP)\n self.runner.target_set_error(self.port_set, exception)\n else:\n self._state_transition(_STATE.BASE, _STATE.DHCP)","def action(self, action):\n\n if action not in self.VALID_ACTIONS:\n raise ValueError('Invalid action: {0}'.format(action))\n\n data = self.request_params\n data.update({\n 'action': action,\n 'status': 'true'\n })\n\n response = requests.post(\n self.vendor.endpoint + self._ENDPOINT, data=data)\n if response.status_code != requests.codes.ok or \\\n '<status>success</status>' not in response.text:\n raise RuntimeError(\n 'Unable to {0} host: {1}'.format(action, response.text))","def host_power_action(self, host, action):\n return action","def host_power_action(self, host, action):\n return action","def execute_action(self, agent, action):\n raise NotImplementedError","def execute_action(self, agent, action):\n raise NotImplementedError","def perform_action(self, action):\n method_name = action.text().lower()\n method_name = method_name + \"_action\"\n action_method = getattr(self, method_name)\n action_method()","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def execute_action(self, agent, action):\n abstract","def test_perform_host_action(self):\n pass","def call_action(self, action):\n pass","def action_on_instances(client_method, instance_ids, action):\n try:\n if instance_ids:\n logger.info('{}ing the following instances'.format(action))\n logger.info(instance_ids)\n client_method(InstanceIds=instance_ids)\n logger.info('{} has finished successfully'.format(action))\n else:\n logger.info('No action was taken because one of the following:')\n logger.info('1 - Instances have \"NoShutdown\" flag.')\n logger.info('2 - Instances do not have \"Schedule\" tag.')\n logger.info('3 - Instances have no value in \"Schedule\" tag.')\n except Exception as error:\n logger.error('{} failed with the following output : {}'.format(action, error))","def send_action(action):\n rospy.wait_for_service('/drl/send_command')\n try:\n get_random_action_proxy = rospy.ServiceProxy('/drl/send_command', SendCommand)\n # Call the service\n get_random_action_proxy(action)\n except rospy.ServiceException, ex:\n print \"Service call get_random_action failed: %s\" % ex","def do_action(self, action, **kwargs):\r\n print(action)\r\n action_method = getattr(self, action._method.__name__)\r\n if action_method:\r\n action_method(**kwargs)","def call_method(self, action):\n\n\t\tif action[0] in self.methods:\n\t\t\tself.methods[action[0]](action[0:])\n\t\telse:\n\t\t\tself.no_such_method()","def host_action(self, host, action):\n url = '/os-hosts/{0}/{1}'.format(host, action)\n return self._get(url, response_key='host')","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def perform_action(self, action_id: int) -> None:\r\n ...","def instance_action():\n data = check_args(\n ('cloudProvider', 'apiKey', 'secretKey', 'instanceAction',\n 'instanceId')\n )\n job = jobs.instance_action.apply_async(args=(data,))\n return make_response(job_id=job.id)","def __call__(self, argv, help):\n parser = argparse.ArgumentParser(\n prog=\"%s do\" % self.ctrl.progname,\n description=help)\n parser.add_argument(\"instance\", nargs=1,\n metavar=\"instance\",\n help=\"Name of the instance from the config.\",\n type=str,\n choices=self.get_completion())\n group = parser.add_mutually_exclusive_group(required=True)\n group.add_argument(\"task\", nargs='?',\n help=\"The task to run.\")\n group.add_argument(\"-l\", \"--list\",\n action='store_true',\n help=\"List available tasks.\")\n parser.add_argument(\"task_args\",\n metavar=\"arg|key=value\", nargs=\"*\",\n help=\"Arguments for the task.\")\n args = parser.parse_args(argv)\n\n instance = self.ctrl.instances[args.instance[0]]\n if args.list:\n print(\"Available commands:\")\n print()\n with callables(instance) as tasks:\n for name in sorted(tasks):\n print(\" %s\" % name)\n return\n task_args = []\n task_kwargs = {}\n for arg in args.task_args:\n parts = arg.split('=', 1)\n if len(parts) == 1:\n task_args.append(arg)\n else:\n task_kwargs[parts[0]] = parts[1]\n instance.do(args.task, *task_args, **task_kwargs)","def do_action(self):\n func = self._get_action_func()\n func(self)","def perform_action(self, action_data):\n pass","def _do_action(self):\n pass","def _do_action(self):\n pass","def run_command_on_selected_server(command, host_=None):\n print host_\n if not host_:\n select_instance()\n selected_hosts = [\n 'ubuntu@' + env.active_instance.public_dns_name\n ]\n else:\n selected_hosts = [\n 'ubuntu@' + str(env.myhost)\n ]\n execute(command, hosts=selected_hosts)","def press_on_configure_ssh(driver):\n assert wait_on_element(driver, 5, xpaths.services.ssh_Service_Button, 'clickable')\n driver.find_element_by_xpath(xpaths.services.ssh_Service_Button).click()","def go(self, argv):\n # Read the command line options\n self.get_options(argv)\n self.config = self.validate_dhcp_fixed_with_dns(self.config)\n \n plugin_name = self.config['firewall']['type']\n print(\"Selecting firewall type: %s\" % plugin_name)\n firewall = Plugin.registered[plugin_name](self.config)\n \n firewall.emit_dns()\n firewall.emit_dhcp()\n firewall.emit_portfw()\n firewall.upload_files()","async def test_dhcp(hass: HomeAssistant) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN,\n data=dhcp.DhcpServiceInfo(\n ip=\"1.2.3.4\", macaddress=\"01:23:45:67:89:ab\", hostname=\"mock_hostname\"\n ),\n context={\"source\": config_entries.SOURCE_DHCP},\n )\n\n assert result.get(\"type\") == FlowResultType.FORM\n assert result.get(\"step_id\") == \"user\"","def run(self):\n\n self._action.execute()","def _RunDHCPClient(self, dhclient_script_path=None, **kwargs):\n del kwargs\n PID_FILE = os.path.join(self._tmp_dir, 'dhclient.pid')\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\n interface=self.interface)\n dhcp_command = ('echo \"\" | ' # dhclient expects STDIN for some reason\n 'dhclient -4 ' # only run on IPv4\n '-nw ' # immediately daemonize\n '-pf {pid_file} '\n '-sf {dhclient_script} '\n '-lf /dev/null ' # don't keep a leases file\n '-v {interface}'.format(\n pid_file=PID_FILE,\n dhclient_script=dhclient_script_path,\n interface=self.interface))\n kill_command = 'cat {pid_file} | xargs -r kill; rm {pid_file}'.format(\n pid_file=PID_FILE)\n force_kill_command = 'pgrep dhclient | xargs -r kill -9'\n\n logging.info('Killing any existing dhclient processes...')\n self._device.Call(force_kill_command)\n\n logging.info('Clearing any existing ifconfig networks...')\n self._device.Call(clear_ifconfig_command)\n\n logging.info('Starting dhclient...')\n self._device.CheckCall(dhcp_command)\n\n logging.info('Waiting to lease an IP...')\n ip = sync_utils.WaitFor(self._LeasedIP, self._dhcp_timeout)\n if not ip:\n self._device.Call(kill_command)\n raise WiFiError('DHCP bind failed')\n logging.info('Success: bound to IP %s', ip)\n\n yield ip # We have bound an IP; yield back to the caller.\n\n logging.info('Stopping dhclient...')\n self._device.Call(kill_command)\n self._device.Call(force_kill_command)\n self._device.Call(clear_ifconfig_command)\n\n yield # We have released the IP.","def main(self, cmd_args):\n reservations = self._ec2_client.describe_instances(Filters=[\n {'Name': \"instance-id\", 'Values': [cmd_args.id]},\n {'Name': \"tag:Name\", 'Values': [cmd_args.name]}\n ])['Reservations']\n if not reservations:\n halt.err(\"No instances matching given parameters found.\")\n instance_state = reservations[0]['Instances'][0]['State']['Name']\n if instance_state in (\"shutting-down\", \"terminated\"):\n halt.err(\"Instance has already been terminated.\")\n\n addresses = self._ec2_client.describe_addresses(Filters=[\n {'Name': \"instance-id\", 'Values': [cmd_args.id]}\n ])['Addresses']\n print(\"\")\n if addresses:\n self._disassociate_addresses(addresses, cmd_args.save_ips)\n elif cmd_args.save_ips is True:\n print(\"No elastic IPs associated with instance.\")\n\n self._ec2_client.terminate_instances(InstanceIds=[cmd_args.id])\n print(\"Instance termination process started.\")","def __call__(self, argv, help):\n from ploy.common import sorted_choices\n parser = argparse.ArgumentParser(\n prog=\"%s fab\" % self.ctrl.progname,\n description=help,\n add_help=False,\n )\n instances = self.ctrl.get_instances(command='init_ssh_key')\n parser.add_argument(\"instance\", nargs=1,\n metavar=\"instance\",\n help=\"Name of the instance from the config.\",\n type=str,\n choices=sorted_choices(instances))\n parser.add_argument(\"fabric_opts\",\n metavar=\"...\", nargs=argparse.REMAINDER,\n help=\"Fabric options\")\n args = parser.parse_args(argv)\n\n instance = instances[args.instance[0]]\n with fabric_integration(self.ctrl, instance, fabcmd=True):\n from fabric.main import main\n fabfile = get_fabfile(instance)\n newargv = ['fab', '-f', fabfile]\n if args.fabric_opts:\n newargv = newargv + args.fabric_opts\n with sys_argv(newargv):\n main()","def _execute_action(self, action):\n if action['type'] == 'http':\n self._execute_action_http(action)\n elif action['type'] == 'mail':\n self._execute_action_mail(action)\n elif action['type'] == 'chat':\n pass\n elif action['type'] == 'printer':\n self._execute_action_printer(action)\n elif action['type'] == 'smb':\n self._execute_action_smb(action)\n\n # Wait for a randomized interval.\n time.sleep(random.randint(1, 5))","def perform_fixture_instance_action(self, instance_id, action_name, user=None, arguments=None):\n with lock_instance(instance_id):\n instance = self.get_fixture_instance(id=instance_id)\n fix_class = self.get_fixture_class(id=instance.class_id)\n action = fix_class.actions.get(action_name, None)\n if not action and action_name not in [InstanceAction.CREATE, InstanceAction.DELETE]:\n raise AXApiInvalidParam(\"Action '{}' not defined for class '{}'\".format(action_name, fix_class.name))\n\n status_detail_message = \"Performing '{}' action\".format(action_name)\n if user:\n status_detail_message += \" by {}\".format(user)\n if action_name == InstanceAction.CREATE:\n instance.transition_state(Event.CREATE, status_detail_message)\n elif action_name == InstanceAction.DELETE:\n instance.transition_state(Event.DELETE, status_detail_message)\n else:\n instance.transition_state(Event.ACTION, status_detail_message)\n\n if not action:\n # Short circuit the create/delete workflow since no action is defined\n logger.info(\"Short-circuiting %s action: action not defined\", action_name)\n if action_name == InstanceAction.CREATE:\n instance.transition_state(Event.ACTION_SUCCESS, \"\")\n elif action_name == InstanceAction.DELETE:\n instance.transition_state(Event.ACTION_SUCCESS, \"Deleted by {}\".format(user or \"user\"))\n self._persist_instance_updates(instance)\n if instance.status == InstanceStatus.ACTIVE:\n self.reqproc.trigger_processor()\n return instance\n\n logger.info(\"Launching %s workflow (template: %s) against instance %s\", action_name, action['template'], instance)\n service = {\n 'name': 'Fixture {} {}'.format(instance.name, action_name),\n 'notifications': [{\n \"whom\": [\"fixturemanager\"],\n \"when\": [\"on_completion\"]\n }],\n 'annotations': {\n 'instance_id': instance.id,\n 'action': action_name\n },\n 'fixtures': {\n instance.id: instance.requestdoc()\n }\n }\n if user:\n service['user'] = user\n service['notifications'].append({\n \"whom\": [\"submitter\"],\n \"when\": [\"on_failure\", \"on_success\"]\n })\n # Attempt to get the most up-to-date version of the service template to submit for the action.\n # The template may no longer exist (e.g. the repo was disconnected). If we are unable to find the template\n # fall back to the version we cache with the class.\n service_template = self.axops_client.get_template(fix_class.repo, fix_class.branch, action['template'])\n if service_template:\n service['template_id'] = service_template['id']\n else:\n logger.warning(\"Service template '%s' not found in repo %s branch %s. Falling back to cached version\", action['template'], fix_class.repo, fix_class.branch)\n service['template'] = fix_class.action_templates[action['template']]\n\n default_args = action.get('arguments', {})\n if default_args or arguments:\n service['arguments'] = default_args\n if arguments is not None:\n service['arguments'].update(arguments)\n for arg_name, arg_val in service['arguments'].items():\n service['arguments'][arg_name] = substitute_attributes(arg_val, instance)\n\n logger.debug(\"Submitting service:\\n%s\", pretty_json(service))\n service = self.axops_client.create_service(service)\n instance.operation = {\n 'id': service['id'],\n 'name': service['name'],\n }\n logger.info(\"Service created: %s\", instance.operation)\n self._persist_instance_updates(instance)\n return instance","def main():\n utils.vip_main(actuator_agent, identity='platform.d.actuator')","def on_action(self, message):\n with self.handler.wrapee as wrapee:\n log.debug(\"Calling {method} on {name}\", method=message['action'], name=self.name)\n try:\n func = getattr(wrapee, message['action'])\n except AttributeError as ex:\n log.warn(\"Trying to call a method {method} that does not exsist!\",\n method=ex.args[0])\n return\n res, msg = func(*message['args'])\n if not res:\n log.warn(\"Error while calling {method}: {msg}\", msg=msg,\n method=message['action'])\n else:\n log.debug(\"Called method succesfully\")\n for protocol in self.service.protocols:\n protocol.send_packet()\n if msg != '':\n protocol.send_news(msg)","def do_command(self, args):\n hostops = dbops.Hosts()\n hostops.state(args)","def ssh(ssh, app, command):\n if ssh is None:\n ssh = _DEFAULT_SSH\n\n if app.find('#') == -1:\n # Instance is not specified, list matching and exit.\n raise click.BadParameter('Specify full instance name: xxx#nnn')\n\n app_discovery = discovery.Discovery(context.GLOBAL.zk.conn, app, 'ssh')\n app_discovery.sync()\n\n # Restore default signal mask disabled by python spawning new thread\n # for Zk connection.\n #\n # TODO: should this be done as part of zkutils.connect?\n for sig in range(1, signal.NSIG):\n try:\n signal.signal(sig, signal.SIG_DFL)\n except OSError:\n pass\n\n # TODO: not sure how to handle mutliple instances.\n for (app, hostport) in app_discovery.items():\n _LOGGER.info('%s :: %s', app, hostport)\n if hostport:\n host, port = hostport.split(b':')\n run_ssh(host, port, ssh, list(command))","def launch_instance(ec2, ami, itype, kp_name, sec_group_name):\n\n\n instance = ec2.run_instances(\n ami,\n key_name=kp_name,\n instance_type=itype,\n security_groups=[sec_group_name]\n ).instances[0]\n\n while instance.state != 'running':\n sys.stdout.write('Waiting for instance: {}, at DNS: {} to start\\n'.format(instance.id,\n str(instance.dns_name).split('.')[0]))\n time.sleep(5)\n instance.update()\n\n sys.stdout.write('\\nSuccess! EC2 Instance Launched \\nInstance_Type: {} in {}'.format(instance.instance_type,\n instance.placement))\n return instance","def Execute(self, client_ip, interface_name=None, interface_mac=None, csv=False, bash=False, client_user=sfdefaults.client_user, client_pass=sfdefaults.client_pass, debug=False):\n del self\n dhcp = Get(**locals())\n\n if dhcp:\n if bash or csv:\n sys.stdout.write(\"true\")\n sys.stdout.write(\"\\n\")\n sys.stdout.flush()\n else:\n mylog.info(\"DHCP is enabled\")\n else:\n if bash or csv:\n sys.stdout.write(\"false\")\n sys.stdout.write(\"\\n\")\n sys.stdout.flush()\n else:\n mylog.info(\"DHCP is not enabled\")\n return True","def _action(self, action, osd, info=None, **kwargs):\n body = {action: info}\n self.run_hooks('modify_body_for_action', body, **kwargs)\n url = '/osds/%s/action' % base.getid(osd)\n return self.api.client.post(url, body=body)","def post(self, request, *args, **kwargs): # NOQA\n\n stack = self.get_object()\n\n if stack.status not in models.Stack.SAFE_STATES:\n raise BadRequest('You may not perform an action while the '\n 'stack is in its current state.')\n\n driver_hosts_map = stack.get_driver_hosts_map()\n total_host_count = len(stack.get_hosts().exclude(instance_id=''))\n action = request.DATA.get('action', None)\n args = request.DATA.get('args', [])\n\n if not action:\n raise BadRequest('action is a required parameter.')\n\n # check the individual provider for available actions\n for driver, hosts in driver_hosts_map.iteritems():\n available_actions = driver.get_available_actions()\n if action not in available_actions:\n raise BadRequest('At least one of the hosts in this stack '\n 'does not support the requested action.')\n\n # All actions other than launch require hosts to be available\n if action != BaseCloudProvider.ACTION_LAUNCH and total_host_count == 0:\n raise BadRequest('The submitted action requires the stack to have '\n 'available hosts. Perhaps you meant to run the '\n 'launch action instead.')\n\n # Hosts may be spread accross different providers, so we need to\n # handle them differently based on the provider and its implementation\n driver_hosts_map = stack.get_driver_hosts_map()\n for driver, hosts in driver_hosts_map.iteritems():\n\n # check the action against current states (e.g., starting can't\n # happen unless the hosts are in the stopped state.)\n # XXX: Assuming that host metadata is accurate here\n for host in hosts:\n if action == driver.ACTION_START and \\\n host.state != driver.STATE_STOPPED:\n raise BadRequest('Start action requires all hosts to be '\n 'in the stopped state first. At least '\n 'one host is reporting an invalid state: '\n '{0}'.format(host.state))\n if action == driver.ACTION_STOP and \\\n host.state != driver.STATE_RUNNING:\n raise BadRequest('Stop action requires all hosts to be in '\n 'the running state first. At least one '\n 'host is reporting an invalid state: '\n '{0}'.format(host.state))\n if action == driver.ACTION_TERMINATE and \\\n host.state not in (driver.STATE_RUNNING,\n driver.STATE_STOPPED):\n raise BadRequest('Terminate action requires all hosts to '\n 'be in the either the running or stopped '\n 'state first. At least one host is '\n 'reporting an invalid state: {0}'\n .format(host.state))\n if (\n action == driver.ACTION_PROVISION or\n action == driver.ACTION_ORCHESTRATE or\n action == driver.ACTION_CUSTOM\n ) and host.state not in (driver.STATE_RUNNING,):\n raise BadRequest(\n 'Provisioning actions require all hosts to be in the '\n 'running state first. At least one host is reporting '\n 'an invalid state: {0}'.format(host.state))\n\n # Kick off the celery task for the given action\n stack.set_status(models.Stack.EXECUTING_ACTION,\n models.Stack.PENDING,\n 'Stack is executing action \\'{0}\\''.format(action))\n\n if action == BaseCloudProvider.ACTION_CUSTOM:\n\n task_list = []\n\n action_ids = []\n\n for command in args:\n action = models.StackAction(stack=stack)\n action.host_target = command['host_target']\n action.command = command['command']\n action.type = BaseCloudProvider.ACTION_CUSTOM\n action.start = datetime.now()\n action.save()\n\n action_ids.append(action.id)\n\n task_list.append(tasks.custom_action.si(\n action.id,\n command['host_target'],\n command['command']\n ))\n\n task_chain = reduce(or_, task_list)\n\n task_chain()\n\n ret = {\n \"results_urls\": []\n }\n\n for action_id in action_ids:\n ret['results_urls'].append(reverse(\n 'stackaction-detail',\n kwargs={\n 'pk': action_id,\n },\n request=request\n ))\n\n return Response(ret)\n\n # Keep track of the tasks we need to run for this execution\n task_list = []\n\n # FIXME: not generic\n if action in (BaseCloudProvider.ACTION_STOP,\n BaseCloudProvider.ACTION_TERMINATE):\n # Unregister DNS when executing the above actions\n task_list.append(tasks.unregister_dns.si(stack.id))\n\n # Launch is slightly different than other actions\n if action == BaseCloudProvider.ACTION_LAUNCH:\n task_list.append(tasks.launch_hosts.si(stack.id))\n task_list.append(tasks.update_metadata.si(stack.id))\n task_list.append(tasks.cure_zombies.si(stack.id))\n\n # Terminate should leverage salt-cloud or salt gets confused about\n # the state of things\n elif action == BaseCloudProvider.ACTION_TERMINATE:\n task_list.append(\n tasks.destroy_hosts.si(stack.id,\n delete_hosts=False,\n delete_security_groups=False)\n )\n\n elif action in (BaseCloudProvider.ACTION_PROVISION,\n BaseCloudProvider.ACTION_ORCHESTRATE,):\n # action that gets handled later\n pass\n\n # Execute other actions that may be available on the driver\n else:\n task_list.append(tasks.execute_action.si(stack.id, action, *args))\n\n # Update the metadata after the action has been executed\n if action != BaseCloudProvider.ACTION_TERMINATE:\n task_list.append(tasks.update_metadata.si(stack.id))\n\n # Launching requires us to tag the newly available infrastructure\n if action in (BaseCloudProvider.ACTION_LAUNCH,):\n tasks.tag_infrastructure.si(stack.id)\n\n # Starting and launching requires DNS updates\n if action in (BaseCloudProvider.ACTION_START,\n BaseCloudProvider.ACTION_LAUNCH):\n task_list.append(tasks.register_dns.si(stack.id))\n\n # starting, launching, or reprovisioning requires us to execute the\n # provisioning tasks\n if action in (BaseCloudProvider.ACTION_START,\n BaseCloudProvider.ACTION_LAUNCH,\n BaseCloudProvider.ACTION_PROVISION,\n BaseCloudProvider.ACTION_ORCHESTRATE):\n task_list.append(tasks.ping.si(stack.id))\n task_list.append(tasks.sync_all.si(stack.id))\n\n if action in (BaseCloudProvider.ACTION_START,\n BaseCloudProvider.ACTION_LAUNCH,\n BaseCloudProvider.ACTION_PROVISION):\n task_list.append(tasks.highstate.si(stack.id))\n task_list.append(tasks.global_orchestrate.si(stack.id))\n task_list.append(tasks.orchestrate.si(stack.id))\n\n if action == BaseCloudProvider.ACTION_ORCHESTRATE:\n task_list.append(tasks.orchestrate.si(stack.id, 2))\n\n task_list.append(tasks.finish_stack.si(stack.id))\n\n # chain together our tasks using the bitwise or operator\n task_chain = reduce(or_, task_list)\n\n # execute the chain\n task_chain()\n\n # Update all host states\n stack.get_hosts().update(state='actioning')\n\n serializer = self.get_serializer(stack)\n return Response(serializer.data)","def system_api(self, action, in_min, at, all_re, vmhost, other_re, media, member_id):\n msg = None\n if action != 'zeroize':\n if (at == 'now' or (in_min == 0 and at is None)):\n if self.dev.timeout > 5:\n self.queue_message(\"log\", \"Decreasing device RPC timeout to 5 seconds.\")\n self.dev.timeout = 5\n\n try:\n self.sw = jnpr.junos.utils.sw.SW(self.dev)\n if action == 'reboot':\n if member_id is not None:\n for m_id in member_id:\n got = self.sw.reboot(in_min, at, all_re, None, vmhost, other_re, member_id=m_id)\n else:\n got = self.sw.reboot(in_min, at, all_re, None, vmhost, other_re)\n elif action == 'shutdown':\n got = self.sw.poweroff(in_min, at, None, all_re, other_re)\n elif action == 'halt':\n got = self.sw.halt(in_min, at, all_re, other_re)\n elif action == 'zeroize':\n got = self.sw.zeroize(all_re, media)\n else:\n raise AnsibleError('Relevant action not found')\n\n self.queue_message(\"log\", \"RPC executed\")\n if got is None:\n msg = 'Did not find expected RPC response.'\n else:\n msg = '%s successfully initiated. Response got %s' % (action, got)\n except (self.pyez_exception.RpcTimeoutError) as ex:\n try:\n self.close(raise_exceptions=True)\n # This means the device wasn't already disconnected.\n raise AnsibleError('%s failed. %s may not have been ' \\\n 'initiated.' % (action, action))\n except (self.pyez_exception.RpcError,\n self.pyez_exception.ConnectError):\n # This is expected. The device has already disconnected.\n msg = '%s succeeded.' % (action)\n except (self.pyez_exception.RpcError,\n self.pyez_exception.ConnectError) as ex:\n raise AnsibleError('%s failed. Error: %s' % (action, str(ex)))\n return msg","def _call_service(self, action):\n conf_service = action.get(CONF_SERVICE, action.get(CONF_SERVICE_OLD))\n self._last_action = action.get(CONF_ALIAS, conf_service)\n _LOGGER.info(\"Executing script %s step %s\", self._name,\n self._last_action)\n domain, service = split_entity_id(conf_service)\n data = action.get(CONF_SERVICE_DATA, {})\n self.hass.services.call(domain, service, data)","def invoke_action(body):\n directives = []\n if connexion.request.is_json:\n data = Data.from_dict(body)\n\n msg = \"Invoking {action} on custom server.\".format(action=data.action)\n\n reply = Directive(name=\"reply\", payload={\"text\": msg}, type=\"view\")\n directives.append(reply)\n responder = Responder(directives=directives, frame={})\n return responder","def execute(self, instance: it.MDPDPTWInstance, carrier: slt.AHDSolution, num_requests: int):\n pass","def dev_dial(action):\n\n try:\n client = AMIClient(address=AUTH_CREDS['address'], port=AUTH_CREDS['port'])\n client.login(username=AUTH_CREDS['username'], secret=AUTH_CREDS['secret'])\n\n future = client.send_action(action)\n if VERBOSE:\n print(future.response or \"None\")\n\n client.logoff()\n\n except Exception as e:\n print(\"Error: %s\" % e.strerror)\n sys.exit(1)","def send_command(client, device_label, device_command, device_hold_secs=0):\n device_id = devices[device_label]['id']\n func = client.send_command(device_id, device_command, device_hold_secs)\n run_in_loop_now('send_command', func)\n print(\"Sent: \" + device_command + \" to \" + device_label)\n return","def _invoke(self):\n with self.storlet_logger.activate(),\\\n self._activate_invocation_descriptors():\n self._send_execute_command()","def __init__(self):\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State","def answerDHCP(self, shouldAnswer):\n assert False, \"Deriving class must implement\"","def execute(self):\n self.advertise_and_connect()\n self.display_data()\n self.disconnect()","def take_action(self, action):\n getattr(self, action['func'])(\n *action.get('args', ()), \n **action.get('kwargs', {})\n )","def do_action(self, action, a=None, b=None):\n pass","def _execute(self):\n\n action_name = self.action.lower()\n method_name = action_name.replace('node', 'do')\n method = getattr(self, method_name, None)\n\n if method is None:\n reason = 'Unsupported action: %s' % self.action\n EVENT.error(self, consts.PHASE_ERROR, reason)\n return self.RES_ERROR, reason\n\n return method()","def run(self):\n if not self.stopResponder:\n try:\n if not exploit.is_authorized(self.target_ip):\n print 'Tyring to get authorization to {0}...'.format(self.target_ip)\n exploit.auto_connect(self.src_ip, self.target_ip)\n time.sleep(5)\n self.run()\n except Exception as e:\n #print ('[!] Error occured,' , e)\n if not exploit.is_reachable(self.target_ip):\n \tprint '[!] Target no longer reachable'\n else:\n \tprint ('[!] Error occured,' , e)\n #uncomment line below if you would like it to keep retrying to connect to host even if it was unreachable\n self.run()\n #print('Exiting responder...')","def mainProv(event, portId, ipAddr=''):\n\tif event == 'create':\n\t\thostName = genHostname(ipAddr)\n\t\tktabUpdate(hostName,action='create')\n\t\tdnsUpdate(portId=portId, ipAddr=ipAddr, action='create')\n\tif event == 'destroy':\n\t\thostName = dnsUpdate(portId, action='delete')\n\t\tktabUpdate(hostName, action='delete')","def execute(self) -> None:\n self.command(self.target)","def idle_handler(self):\n if self.state == _STATE.INIT:\n self._activate()\n elif self.state == _STATE.ACTIVE:\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\n self.record_result('dhcp', state='run')\n self.dhcp_monitor.start()\n elif self.state == _STATE.BASE:\n self._base_start()","def main():\n\n args = TrafficScriptArg(['tx_src_ip', 'tx_dst_ip'])\n\n tx_if = args.get_arg('tx_if')\n rx_if = args.get_arg('rx_if')\n\n rxq = RxQueue(rx_if)\n txq = TxQueue(tx_if)\n\n tx_src_ip = args.get_arg('tx_src_ip')\n tx_dst_ip = args.get_arg('tx_dst_ip')\n\n sent_packets = []\n\n dhcp_discover = Ether(dst=\"ff:ff:ff:ff:ff:ff\") / \\\n IP(src=tx_src_ip, dst=tx_dst_ip) / \\\n UDP(sport=UDP_SERVICES.bootpc, dport=UDP_SERVICES.bootps) / \\\n BOOTP(op=1,) / \\\n DHCP(options=[(\"message-type\", \"discover\"),\n \"end\"])\n\n sent_packets.append(dhcp_discover)\n txq.send(dhcp_discover)\n\n for _ in range(10):\n dhcp_discover = rxq.recv(2)\n if is_discover(dhcp_discover):\n break\n else:\n raise RuntimeError(\"DHCP DISCOVER Rx timeout\")\n\n sys.exit(0)","def do_command(self, args):\n chk_arg_count(args, 3)\n hostname, command, value = args\n args = (hostname, value)\n hostops = dbops.Hosts()\n if command == 'mem':\n hostops.memory(args)\n elif command == 'cores':\n hostops.cores(args)\n elif command == 'bits':\n hostops.bitness(args)\n else:\n raise ValueError('Unknown host configuration.')","def run_hook(vmname, action):\n vm = None\n try:\n vm = globals()[vmname]\n except KeyError:\n print(\"No such VM\")\n \n if vm:\n try:\n hook = getattr(vm, action)\n hook()\n except AttributeError:\n print(\"Action not supported\")","def _do_probe(self):\n self._do_expire() \n\n server = self.servers.pop(0)\n self.servers.append(server)\n\n r = arp()\n r.hwtype = r.HW_TYPE_ETHERNET\n r.prototype = r.PROTO_TYPE_IP\n r.opcode = r.REQUEST\n r.hwdst = ETHER_BROADCAST\n r.protodst = server #send arp request to server popped above one by one\n r.hwsrc = self.mac #mac address of switch (connected to controller)\n r.protosrc = self.service_ip #switch is sending arp\n e = ethernet(type=ethernet.ARP_TYPE, src=self.mac,\n dst=ETHER_BROADCAST)\n e.set_payload(r)\n # self.log.debug(\"ARPing for %s\", server)\n msg = of.ofp_packet_out()\n msg.data = e.pack()\n msg.actions.append(of.ofp_action_output(port=of.OFPP_FLOOD)) #flood to all the port \n msg.in_port = of.OFPP_NONE\n self.con.send(msg)\n\n self.outstanding_probes[server] = time.time() + self.arp_timeout ## disctionary of (IP : expire_time)\n # {IPAddr('10.0.0.1'): 1612113642.861805})\n # {IPAddr('10.0.0.2'): 1612113654.816535}) likewise\n\n core.callDelayed(self._probe_wait_time, self._do_probe) ## Alternate way for simple timers:\n # core.callDelayed(10, function to call)","def invoke_action(self, td, name, input_value, timeout=None):\n\n raise NotImplementedError()","def _execute_action(self, action):\n cmd, _self, args_, kwargs_, return_values = (\n action.name,\n action.target, # target is equivalent to the \"self\" in a method\n action.args,\n action.kwargs,\n action.return_ids,\n )\n _self = self._fetch_placeholders_from_ids(_self)\n args_ = self._fetch_placeholders_from_ids(args_)\n kwargs_ = self._fetch_placeholders_from_ids(kwargs_)\n return_values = self._fetch_placeholders_from_ids(return_values)\n\n # We can only instantiate placeholders, filter them\n return_placeholders = []\n Role.nested_object_traversal(\n return_values, lambda ph: return_placeholders.append(ph), PlaceHolder\n )\n\n if _self is None:\n method = self._fetch_package_method(cmd)\n response = method(*args_, **kwargs_)\n else:\n response = getattr(_self, cmd)(*args_, **kwargs_)\n\n if not isinstance(response, (tuple, list)):\n response = (response,)\n\n PlaceHolder.instantiate_placeholders(return_placeholders, response)","def action(self):\n pass","def action(self):\n pass","def take_action(self, action):\n\t\traise NotImplementedError","def dns():\r\n print('''\\n%s at %s acting as user %s\r\n\\nDNS Configuration Menu''' % (PACKETMASTER.model, ADDRESS, USERNAME))\r\n choice = moves.input('''\r\n 1 - Get current DNS configuration\r\n 2 - Change DNS configuration\r\n 3 - Back\r\n 4 - Quit \\n\r\n Enter selection number: ''')\r\n try:\r\n choice = int(choice)\r\n except ValueError as reason:\r\n print(\"That is not a valid selection.\", reason)\r\n dns()\r\n execute = {1: PACKETMASTER.get_dns,\r\n 2: PACKETMASTER.set_dns_guided,\r\n 3: hardwareconfig,\r\n 4: exit}\r\n if choice in execute:\r\n try:\r\n select = execute[choice]\r\n run = select()\r\n print(run)\r\n dns()\r\n except KeyError as reason:\r\n print(reason)\r\n else:\r\n print(\"That is not a valid selection.\")\r\n dns()","async def perform_action(self) -> None:","def apply_action(self, cmd_name, *args):\n\n action = Action(self.tahoma_device.url)\n action.add_command(cmd_name, *args)\n self.controller.apply_actions(\"HomeAssistant\", [action])","def _device_action(self, device_ids, action_type, options=None):\n request = {\"action_type\": action_type, \"device_id\": device_ids}\n if options:\n request[\"options\"] = options\n return self._raw_device_action(request)","def __call__(self):\n action = self.args.get('action', None)\n if not hasattr(self, 'action_%s' % (action,)):\n action = 'plugin_root'\n\n action_method = getattr(self, 'action_%s' % (action, ))\n return action_method()","def test_execute_host_maintenance(self):\n self.addCleanup(self.rollback_compute_nodes_status)\n instances = self._create_one_instance_per_host_with_statistic()\n hostname = instances[0].get('OS-EXT-SRV-ATTR:hypervisor_hostname')\n audit_parameters = {\"maintenance_node\": hostname}\n\n _, goal = self.client.show_goal(self.GOAL)\n _, strategy = self.client.show_strategy(\"host_maintenance\")\n _, audit_template = self.create_audit_template(\n goal['uuid'], strategy=strategy['uuid'])\n\n self.assertTrue(test_utils.call_until_true(\n func=functools.partial(\n self.has_action_plans_finished),\n duration=600,\n sleep_for=2\n ))\n\n _, audit = self.create_audit(\n audit_template['uuid'], parameters=audit_parameters)\n\n try:\n self.assertTrue(test_utils.call_until_true(\n func=functools.partial(\n self.has_audit_finished, audit['uuid']),\n duration=600,\n sleep_for=2\n ))\n except ValueError:\n self.fail(\"The audit has failed!\")\n\n _, finished_audit = self.client.show_audit(audit['uuid'])\n if finished_audit.get('state') in ('FAILED', 'CANCELLED'):\n self.fail(\"The audit ended in unexpected state: %s!\" %\n finished_audit.get('state'))\n\n _, action_plans = self.client.list_action_plans(\n audit_uuid=audit['uuid'])\n action_plan = action_plans['action_plans'][0]\n\n _, action_plan = self.client.show_action_plan(action_plan['uuid'])\n _, action_list = self.client.list_actions(\n action_plan_uuid=action_plan[\"uuid\"])\n\n if action_plan['state'] in ('SUPERSEDED', 'SUCCEEDED'):\n # This means the action plan is superseded so we cannot trigger it,\n # or it is empty.\n return\n for action in action_list['actions']:\n self.assertEqual('PENDING', action.get('state'))\n\n # Execute the action by changing its state to PENDING\n _, updated_ap = self.client.start_action_plan(action_plan['uuid'])\n\n self.assertTrue(test_utils.call_until_true(\n func=functools.partial(\n self.has_action_plan_finished, action_plan['uuid']),\n duration=600,\n sleep_for=2\n ))\n _, finished_ap = self.client.show_action_plan(action_plan['uuid'])\n _, action_list = self.client.list_actions(\n action_plan_uuid=finished_ap[\"uuid\"])\n self.assertIn(updated_ap['state'], ('PENDING', 'ONGOING'))\n self.assertIn(finished_ap['state'], ('SUCCEEDED', 'SUPERSEDED'))\n\n for action in action_list['actions']:\n self.assertEqual('SUCCEEDED', action.get('state'))","def run(self):\n try:\n self.ssh_connection.connect()\n dns_response = self.query_dns_server()\n result = self.process_dns_response(dns_response)\n self.handle_result(result)\n\n except Exception as e:\n print(f\"Error: {str(e)}\")\n sys.exit(2)","def handle_action_received(msg: ReceiveMessage) -> None:\n payload = self.render_template(msg, CONF_ACTION_TEMPLATE)\n if not payload or payload == PAYLOAD_NONE:\n _LOGGER.debug(\n \"Invalid %s action: %s, ignoring\",\n [e.value for e in HVACAction],\n payload,\n )\n return\n try:\n self._attr_hvac_action = HVACAction(str(payload))\n except ValueError:\n _LOGGER.warning(\n \"Invalid %s action: %s\",\n [e.value for e in HVACAction],\n payload,\n )\n return\n get_mqtt_data(self.hass).state_write_requests.write_state_request(self)","def performOpen(self, options={}):\n self.switch = USB_Digital_Switch() # Create an instance of the switch class\n self.serial_number = str(self.getAddress())\n\n status = self.establish_connection() # Connect the switch (pass the serial number as an argument if required)\n if status > 0:\n resp = self.switch.Send_SCPI(\":MN?\", \"\") # Read model name\n self.model_number = str(resp[2])\n self.setModel(self.model_number)\n self.log(self.model_number, level = 30)","def menu(self, action):\n if action == self.distrib.CONSULTATION:\n self.distrib.distrib_state = AttentComptChoisit(self.distrib)\n return self.distrib.banque.consultation(self.distrib.carte_inseree.obtenir_numero_carte())\n else:\n self.distrib.distrib_state = AttenteInformationTransfer(self.distrib)","def execute_script(self, action, *args):\n self.host.cmd(('./%s' + len(args) * ' %s') % (action, *args))","def main():\n executor(option().host)","def Run(self):\n\n # Execution on the master\n if self.cmd == 'MASTER':\n self.PerformMirroredUpgrade()\n \n # The rest of the options are executed on remote hosts\n elif self.cmd == 'CHKDOWN':\n self.CheckDown()\n elif self.cmd == 'SETCATVERSION':\n self.SetCatVersion((str(self.option)==str(True)))\n else:\n raise Exception('Unknown cmd: ' + str(self.cmd))\n \n if self.pool:\n t = self.pool\n self.pool = None\n del t","def action_run(self):\n pass","def handleAction(self, action):\n\n if action.deviceAction == indigo.kUniversalAction.EnergyReset:\n # Reset energy consumed and returned\n self.resetEnergy()\n\n # \"Reset\" the ui value\n self.device.updateStateOnServer('accumEnergyTotal', 0.0)\n elif action.deviceAction == indigo.kUniversalAction.EnergyUpdate:\n # This will be handled by making a status request\n self.sendStatusRequestCommand()\n elif action.deviceAction == indigo.kDeviceAction.RequestStatus:\n self.sendStatusRequestCommand()\n else:\n Shelly.handleAction(self, action)","def invoke_jsnapy(self, data, action):\n try:\n self.queue_message(\"vvvv\", \"Creating jnpr.jsnapy.SnapAdmin instance.\")\n jsa = jnpr.jsnapy.SnapAdmin()\n self.queue_message(\"vvvv\", 'Executing %s action.' % action)\n if action == 'check':\n responses = jsa.check(data=data,\n dev=self.dev,\n pre_file='PRE',\n post_file='POST')\n elif action == 'snapcheck':\n responses = jsa.snapcheck(data=data,\n dev=self.dev,\n file_name='PRE')\n elif action == 'snap_pre':\n responses = jsa.snap(data=data,\n dev=self.dev,\n file_name='PRE')\n elif action == 'snap_post':\n responses = jsa.snap(data=data,\n dev=self.dev,\n file_name='POST')\n else:\n raise AnsibleError(\"Unexpected action: %s.\" % (action))\n self.queue_message(\"vvvv\", 'The %s action executed successfully' % action)\n except (pyez_exception.RpcError, pyez_exception.ConnectError) as ex:\n raise AnsibleError(\"Error communicating with the device: %s\" % str(ex))\n\n results = {}\n if isinstance(responses, list) and len(responses) == 1:\n if action in ('snapcheck', 'check'):\n for response in responses:\n results['device'] = response.device\n results['router'] = response.device\n results['final_result'] = response.result\n results['total_passed'] = response.no_passed\n results['total_failed'] = response.no_failed\n results['test_results'] = response.test_results\n total_tests = int(response.no_passed) + int(response.no_failed)\n results['total_tests'] = total_tests\n pass_percentage = 0\n if total_tests > 0:\n pass_percentage = ((int(response.no_passed) * 100) //\n total_tests)\n results['passPercentage'] = pass_percentage\n results['pass_percentage'] = pass_percentage\n if results['final_result'] == 'Failed':\n results['msg'] = 'Test Failed: Passed %s, Failed %s' % \\\n (results['total_passed'],\n results['total_failed'])\n else:\n results['msg'] = 'Test Passed: Passed %s, Failed %s' % \\\n (results['total_passed'],\n results['total_failed'])\n elif action in ('snap_pre', 'snap_post'):\n results['msg'] = \"The %s action successfully executed.\" % (action)\n else:\n raise AnsibleError(\"Unexpected JSNAPy responses. Type: %s.\"\n \"Responses: %s\" %\n (type(responses), str(responses)))\n return results","def device_discovery(endless):\r\n click.echo(\"start device discovery ...\")\r\n _device_discovery(endless)","def _action(self):\n pass","def choose_action(self):\r\n pass","def onActionChosen(self, agent, action):\n\n pass","def main():\n kernel_params = _parse_kernel_cmdline()\n api_url = kernel_params.get('ipa-api-url')\n if api_url is None:\n _process_error('Mandatory kernel parameter \"ipa-api-url\" is missing.')\n\n boot_mac = kernel_params.get('BOOTIF')\n if boot_mac is None:\n _process_error('Cannot define boot interface, \"BOOTIF\" kernel '\n 'parameter is missing.')\n\n # There is a difference in syntax in BOOTIF variable between pxe and ipxe\n # boot with Ironic.\n # For pxe boot the the leading `01-' denotes the device type (Ethernet)\n # and is not a part of the MAC address\n if boot_mac.startswith('01-'):\n boot_mac = boot_mac[3:].replace('-', ':')\n\n # FIXME(pas-ha) discover all MACs\n node = lookup(api_url, [boot_mac])\n uuid = node['node']['uuid']\n timeout = node['config']['heartbeat_timeout']\n\n heartbeat_url = '{api_url}/v1/heartbeat/{uuid}'.format(api_url=api_url,\n uuid=uuid)\n for n in range(_GET_ADDR_MAX_ITERATION):\n boot_ip = _get_interface_ip(boot_mac)\n if boot_ip is not None:\n break\n time.sleep(_RETRY_INTERVAL)\n else:\n _process_error('Cannot find IP address of boot interface.')\n\n heartbeat(heartbeat_url, boot_ip, timeout)","def _invoke_cmd(self, cmd):\n if cmd in self.COMMANDS:\n self.COMMANDS[cmd]()\n else:\n print(ERROR_UNKNOWN_COMMAND.format(cmd=cmd))","def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\"\"):\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\n cli_type = \"klish\" if cli_type in [\"rest-put\", \"rest-patch\"] else cli_type\n retry_count = data.retry_count if \"retry_count\" in data and data.retry_count else 0\n iteration = data.iteration if \"iteration\" in data and data.iteration else 300\n delay = data.delay if \"delay\" in data and data.delay else 3\n if \"func_name\" in data:\n syslog_file_names = [\"syslog_1_{}\".format(data.func_name), \"syslog_{}\".format(data.func_name)]\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\n if \"config_file_type\" in data and data.config_file_type == \"text\":\n file_path = \"/tmp/file_temp.json\"\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\"server\")\n elif \"config_file_type\" in data and data.config_file_type == \"EoL\":\n file_path = \"\"\n else:\n file_path = basic_obj.write_to_json_file(data.json_content)\n if file_path:\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"config_db_location\" in data and data.config_db_location == \"json\":\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"scenario\" in data and data.scenario == \"invalid-json\":\n st.log(\"Writing invalid content to make invalid json ...\")\n basic_obj.write_to_file_to_line(ssh_conn_obj, \",\", 5, destination_path, \"server\")\n if data.option_type == \"67\":\n st.log(\"Creating {} file on DHCP server ...\".format(data.config_file))\n data.search_pattern = r'\\s*option\\s+bootfile-name\\s*\\S*\\s*\"\\S+\";'\n data.option_string = \"option bootfile-name\"\n if data.type == \"http\":\n data.option_url = \"http://{}{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"tftp\":\n data.option_url = \"tftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"ftp\":\n data.option_url = \"ftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n write_option_to_dhcp_server(ssh_conn_obj, data)\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\n st.log(\"{} service not running\".format(data.dhcp_service_name))\n st.report_fail(\"service_not_running\", data.dhcp_service_name)\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\n data.device_action = \"reboot\" if cli_type == \"klish\" else data.device_action\n if data.device_action == \"reboot\":\n reboot_type = data.reboot_type if \"reboot_type\" in data and data.reboot_type else \"normal\"\n basic_obj.remove_file(dut, data.config_db_path)\n st.reboot(dut, reboot_type, skip_port_wait=True)\n st.wait_system_status(dut, 500)\n elif data.device_action == \"run\":\n ztp_operations(dut, data.device_action)\n if \"band_type\" in data and data.band_type==\"inband\":\n if not basic_obj.poll_for_system_status(dut):\n st.log(\"Sytem is not ready ..\")\n st.report_env_fail(\"system_not_ready\")\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\"up\"):\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \"down\")\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \"up\")\n if interface_status is not None:\n if not interface_status:\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\n if \"service\" in data and data.service == \"disable\":\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"stop\")\n if basic_obj.verify_service_status(dut, \"ztp\"):\n st.log(\"ZTP status is not stopped\")\n st.report_fail(\"service_not_stopped\", \"ztp\")\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"start\")\n if not poll_ztp_status(dut, [\"IN-PROGRESS\", \"Not Started\", \"SUCCESS\"], cli_type=cli_type):\n st.report_fail(\"ztp_max_polling_interval\")\n if \"check\" in data and data.check == \"not\":\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n else:\n st.log(\"Iteration count {}\".format(iteration))\n st.log(\"REBOOT ON SUCCESS - {}\".format(reboot_on_success))\n if reboot_on_success:\n if \"configdb-json\" in reboot_on_success:\n st.wait_system_reboot(dut)\n st.wait_system_status(dut, 300)\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\n else:\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\n if not result:\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n if reboot_on_success:\n output = show_ztp_status(dut, cli_type=cli_type)\n if output[\"status\"] != \"SUCCESS\":\n st.wait(300, \"Waiting for device to reboot after success...\")\n st.wait_system_status(dut, 300)\n # st.wait_system_reboot(dut)\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n st.banner(boot_up_obj.sonic_installer_list(dut))\n verify_ztp_filename_logs(dut, data)\n if \"ztp_log_string\" in data and data.ztp_log_string:\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path, data.ztp_log_string))\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path_1, data.ztp_log_string))\n st.report_fail(\"ztp_log_verification_failed\", data.ztp_log_path_1, data.ztp_log_string)\n if \"result\" in data and data.result == \"pass\":\n st.report_pass(\"test_case_passed\")","def control(self, action):\r\n cfunc = lib_importer.windll.DAQmxTaskControl\r\n if cfunc.argtypes is None:\r\n with cfunc.arglock:\r\n if cfunc.argtypes is None:\r\n cfunc.argtypes = [\r\n lib_importer.task_handle, ctypes.c_int]\r\n\r\n error_code = cfunc(\r\n self._handle, action.value)\r\n check_for_error(error_code)","def act(self, device):\n with open(device, 'r') as fd:\n result = fcntl.ioctl(fd, self.ioctl)\n if result:\n raise Exception(\"ioctl failed with result {0}\".format(result))","def ssh_to_ec2(instance):\n subprocess.Popen(['ssh', instance.dns_name])","def execute(self, virtual_machine):\n self.function(virtual_machine)","def do_action(self, _action: action.Action) -> None:\n if isinstance(_action, action.Attack):\n self.do_attack_action(_action)\n elif isinstance(_action, action.Move):\n self.do_move_action(_action)\n else:\n raise NotImplementedError(f\"Action {type(_action)} not implemented!\")","def action(self):\n self.action_thread = Thread(target=self._action_then_signal, daemon=True)\n self.action_thread.start()"],"string":"[\n \"def device_action(host, details, action):\\n if details:\\n pprint(cs.get_device_details(host))\\n if action:\\n pprint(cs.device_action(host, action))\",\n \"def device_action(self, client, action):\\r\\n client.deviceAction(action)\",\n \"def _RunDHCPCD(self, **kwargs):\\n del kwargs\\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\\n interface=self.interface)\\n # -K: Don't receive link messages for carrier status. You should\\n # only have to use this with buggy device drivers or running\\n # dhcpcd through a network manager.\\n # -c: Location to the hooks file. If the default location happens to be\\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\\n timeout=self._dhcp_timeout,\\n interface=self.interface)\\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\\n timeout=self._dhcp_timeout,\\n cmd=dhcp_command)\\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\\n\\n logging.info('Killing any existing dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n logging.info('Clearing any existing ifconfig networks...')\\n self._device.Call(clear_ifconfig_command)\\n\\n logging.info('Starting dhcpcd...')\\n self._device.CheckCall(dhcp_timeout_command)\\n\\n logging.info('Verifying IP address...')\\n ip = self._LeasedIP()\\n if not ip:\\n self._device.Call(force_kill_command)\\n raise WiFiError('DHCP bind failed')\\n logging.info('Success: bound to IP %s', ip)\\n\\n yield ip # We have bound an IP; yield back to the caller.\\n\\n logging.info('Killing any remaining dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n yield # We have released the IP.\",\n \"def dhcp_callback(self, state, target_mac=None, target_ip=None, exception=None):\\n self.record_result('dhcp', info=target_mac, ip=target_ip, state=state, exception=exception)\\n self.target_mac = target_mac\\n self.target_ip = target_ip\\n if exception:\\n self._state_transition(_STATE.ERROR, _STATE.DHCP)\\n self.runner.target_set_error(self.port_set, exception)\\n else:\\n self._state_transition(_STATE.BASE, _STATE.DHCP)\",\n \"def action(self, action):\\n\\n if action not in self.VALID_ACTIONS:\\n raise ValueError('Invalid action: {0}'.format(action))\\n\\n data = self.request_params\\n data.update({\\n 'action': action,\\n 'status': 'true'\\n })\\n\\n response = requests.post(\\n self.vendor.endpoint + self._ENDPOINT, data=data)\\n if response.status_code != requests.codes.ok or \\\\\\n '<status>success</status>' not in response.text:\\n raise RuntimeError(\\n 'Unable to {0} host: {1}'.format(action, response.text))\",\n \"def host_power_action(self, host, action):\\n return action\",\n \"def host_power_action(self, host, action):\\n return action\",\n \"def execute_action(self, agent, action):\\n raise NotImplementedError\",\n \"def execute_action(self, agent, action):\\n raise NotImplementedError\",\n \"def perform_action(self, action):\\n method_name = action.text().lower()\\n method_name = method_name + \\\"_action\\\"\\n action_method = getattr(self, method_name)\\n action_method()\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def execute_action(self, agent, action):\\n abstract\",\n \"def test_perform_host_action(self):\\n pass\",\n \"def call_action(self, action):\\n pass\",\n \"def action_on_instances(client_method, instance_ids, action):\\n try:\\n if instance_ids:\\n logger.info('{}ing the following instances'.format(action))\\n logger.info(instance_ids)\\n client_method(InstanceIds=instance_ids)\\n logger.info('{} has finished successfully'.format(action))\\n else:\\n logger.info('No action was taken because one of the following:')\\n logger.info('1 - Instances have \\\"NoShutdown\\\" flag.')\\n logger.info('2 - Instances do not have \\\"Schedule\\\" tag.')\\n logger.info('3 - Instances have no value in \\\"Schedule\\\" tag.')\\n except Exception as error:\\n logger.error('{} failed with the following output : {}'.format(action, error))\",\n \"def send_action(action):\\n rospy.wait_for_service('/drl/send_command')\\n try:\\n get_random_action_proxy = rospy.ServiceProxy('/drl/send_command', SendCommand)\\n # Call the service\\n get_random_action_proxy(action)\\n except rospy.ServiceException, ex:\\n print \\\"Service call get_random_action failed: %s\\\" % ex\",\n \"def do_action(self, action, **kwargs):\\r\\n print(action)\\r\\n action_method = getattr(self, action._method.__name__)\\r\\n if action_method:\\r\\n action_method(**kwargs)\",\n \"def call_method(self, action):\\n\\n\\t\\tif action[0] in self.methods:\\n\\t\\t\\tself.methods[action[0]](action[0:])\\n\\t\\telse:\\n\\t\\t\\tself.no_such_method()\",\n \"def host_action(self, host, action):\\n url = '/os-hosts/{0}/{1}'.format(host, action)\\n return self._get(url, response_key='host')\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def perform_action(self, action_id: int) -> None:\\r\\n ...\",\n \"def instance_action():\\n data = check_args(\\n ('cloudProvider', 'apiKey', 'secretKey', 'instanceAction',\\n 'instanceId')\\n )\\n job = jobs.instance_action.apply_async(args=(data,))\\n return make_response(job_id=job.id)\",\n \"def __call__(self, argv, help):\\n parser = argparse.ArgumentParser(\\n prog=\\\"%s do\\\" % self.ctrl.progname,\\n description=help)\\n parser.add_argument(\\\"instance\\\", nargs=1,\\n metavar=\\\"instance\\\",\\n help=\\\"Name of the instance from the config.\\\",\\n type=str,\\n choices=self.get_completion())\\n group = parser.add_mutually_exclusive_group(required=True)\\n group.add_argument(\\\"task\\\", nargs='?',\\n help=\\\"The task to run.\\\")\\n group.add_argument(\\\"-l\\\", \\\"--list\\\",\\n action='store_true',\\n help=\\\"List available tasks.\\\")\\n parser.add_argument(\\\"task_args\\\",\\n metavar=\\\"arg|key=value\\\", nargs=\\\"*\\\",\\n help=\\\"Arguments for the task.\\\")\\n args = parser.parse_args(argv)\\n\\n instance = self.ctrl.instances[args.instance[0]]\\n if args.list:\\n print(\\\"Available commands:\\\")\\n print()\\n with callables(instance) as tasks:\\n for name in sorted(tasks):\\n print(\\\" %s\\\" % name)\\n return\\n task_args = []\\n task_kwargs = {}\\n for arg in args.task_args:\\n parts = arg.split('=', 1)\\n if len(parts) == 1:\\n task_args.append(arg)\\n else:\\n task_kwargs[parts[0]] = parts[1]\\n instance.do(args.task, *task_args, **task_kwargs)\",\n \"def do_action(self):\\n func = self._get_action_func()\\n func(self)\",\n \"def perform_action(self, action_data):\\n pass\",\n \"def _do_action(self):\\n pass\",\n \"def _do_action(self):\\n pass\",\n \"def run_command_on_selected_server(command, host_=None):\\n print host_\\n if not host_:\\n select_instance()\\n selected_hosts = [\\n 'ubuntu@' + env.active_instance.public_dns_name\\n ]\\n else:\\n selected_hosts = [\\n 'ubuntu@' + str(env.myhost)\\n ]\\n execute(command, hosts=selected_hosts)\",\n \"def press_on_configure_ssh(driver):\\n assert wait_on_element(driver, 5, xpaths.services.ssh_Service_Button, 'clickable')\\n driver.find_element_by_xpath(xpaths.services.ssh_Service_Button).click()\",\n \"def go(self, argv):\\n # Read the command line options\\n self.get_options(argv)\\n self.config = self.validate_dhcp_fixed_with_dns(self.config)\\n \\n plugin_name = self.config['firewall']['type']\\n print(\\\"Selecting firewall type: %s\\\" % plugin_name)\\n firewall = Plugin.registered[plugin_name](self.config)\\n \\n firewall.emit_dns()\\n firewall.emit_dhcp()\\n firewall.emit_portfw()\\n firewall.upload_files()\",\n \"async def test_dhcp(hass: HomeAssistant) -> None:\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN,\\n data=dhcp.DhcpServiceInfo(\\n ip=\\\"1.2.3.4\\\", macaddress=\\\"01:23:45:67:89:ab\\\", hostname=\\\"mock_hostname\\\"\\n ),\\n context={\\\"source\\\": config_entries.SOURCE_DHCP},\\n )\\n\\n assert result.get(\\\"type\\\") == FlowResultType.FORM\\n assert result.get(\\\"step_id\\\") == \\\"user\\\"\",\n \"def run(self):\\n\\n self._action.execute()\",\n \"def _RunDHCPClient(self, dhclient_script_path=None, **kwargs):\\n del kwargs\\n PID_FILE = os.path.join(self._tmp_dir, 'dhclient.pid')\\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\\n interface=self.interface)\\n dhcp_command = ('echo \\\"\\\" | ' # dhclient expects STDIN for some reason\\n 'dhclient -4 ' # only run on IPv4\\n '-nw ' # immediately daemonize\\n '-pf {pid_file} '\\n '-sf {dhclient_script} '\\n '-lf /dev/null ' # don't keep a leases file\\n '-v {interface}'.format(\\n pid_file=PID_FILE,\\n dhclient_script=dhclient_script_path,\\n interface=self.interface))\\n kill_command = 'cat {pid_file} | xargs -r kill; rm {pid_file}'.format(\\n pid_file=PID_FILE)\\n force_kill_command = 'pgrep dhclient | xargs -r kill -9'\\n\\n logging.info('Killing any existing dhclient processes...')\\n self._device.Call(force_kill_command)\\n\\n logging.info('Clearing any existing ifconfig networks...')\\n self._device.Call(clear_ifconfig_command)\\n\\n logging.info('Starting dhclient...')\\n self._device.CheckCall(dhcp_command)\\n\\n logging.info('Waiting to lease an IP...')\\n ip = sync_utils.WaitFor(self._LeasedIP, self._dhcp_timeout)\\n if not ip:\\n self._device.Call(kill_command)\\n raise WiFiError('DHCP bind failed')\\n logging.info('Success: bound to IP %s', ip)\\n\\n yield ip # We have bound an IP; yield back to the caller.\\n\\n logging.info('Stopping dhclient...')\\n self._device.Call(kill_command)\\n self._device.Call(force_kill_command)\\n self._device.Call(clear_ifconfig_command)\\n\\n yield # We have released the IP.\",\n \"def main(self, cmd_args):\\n reservations = self._ec2_client.describe_instances(Filters=[\\n {'Name': \\\"instance-id\\\", 'Values': [cmd_args.id]},\\n {'Name': \\\"tag:Name\\\", 'Values': [cmd_args.name]}\\n ])['Reservations']\\n if not reservations:\\n halt.err(\\\"No instances matching given parameters found.\\\")\\n instance_state = reservations[0]['Instances'][0]['State']['Name']\\n if instance_state in (\\\"shutting-down\\\", \\\"terminated\\\"):\\n halt.err(\\\"Instance has already been terminated.\\\")\\n\\n addresses = self._ec2_client.describe_addresses(Filters=[\\n {'Name': \\\"instance-id\\\", 'Values': [cmd_args.id]}\\n ])['Addresses']\\n print(\\\"\\\")\\n if addresses:\\n self._disassociate_addresses(addresses, cmd_args.save_ips)\\n elif cmd_args.save_ips is True:\\n print(\\\"No elastic IPs associated with instance.\\\")\\n\\n self._ec2_client.terminate_instances(InstanceIds=[cmd_args.id])\\n print(\\\"Instance termination process started.\\\")\",\n \"def __call__(self, argv, help):\\n from ploy.common import sorted_choices\\n parser = argparse.ArgumentParser(\\n prog=\\\"%s fab\\\" % self.ctrl.progname,\\n description=help,\\n add_help=False,\\n )\\n instances = self.ctrl.get_instances(command='init_ssh_key')\\n parser.add_argument(\\\"instance\\\", nargs=1,\\n metavar=\\\"instance\\\",\\n help=\\\"Name of the instance from the config.\\\",\\n type=str,\\n choices=sorted_choices(instances))\\n parser.add_argument(\\\"fabric_opts\\\",\\n metavar=\\\"...\\\", nargs=argparse.REMAINDER,\\n help=\\\"Fabric options\\\")\\n args = parser.parse_args(argv)\\n\\n instance = instances[args.instance[0]]\\n with fabric_integration(self.ctrl, instance, fabcmd=True):\\n from fabric.main import main\\n fabfile = get_fabfile(instance)\\n newargv = ['fab', '-f', fabfile]\\n if args.fabric_opts:\\n newargv = newargv + args.fabric_opts\\n with sys_argv(newargv):\\n main()\",\n \"def _execute_action(self, action):\\n if action['type'] == 'http':\\n self._execute_action_http(action)\\n elif action['type'] == 'mail':\\n self._execute_action_mail(action)\\n elif action['type'] == 'chat':\\n pass\\n elif action['type'] == 'printer':\\n self._execute_action_printer(action)\\n elif action['type'] == 'smb':\\n self._execute_action_smb(action)\\n\\n # Wait for a randomized interval.\\n time.sleep(random.randint(1, 5))\",\n \"def perform_fixture_instance_action(self, instance_id, action_name, user=None, arguments=None):\\n with lock_instance(instance_id):\\n instance = self.get_fixture_instance(id=instance_id)\\n fix_class = self.get_fixture_class(id=instance.class_id)\\n action = fix_class.actions.get(action_name, None)\\n if not action and action_name not in [InstanceAction.CREATE, InstanceAction.DELETE]:\\n raise AXApiInvalidParam(\\\"Action '{}' not defined for class '{}'\\\".format(action_name, fix_class.name))\\n\\n status_detail_message = \\\"Performing '{}' action\\\".format(action_name)\\n if user:\\n status_detail_message += \\\" by {}\\\".format(user)\\n if action_name == InstanceAction.CREATE:\\n instance.transition_state(Event.CREATE, status_detail_message)\\n elif action_name == InstanceAction.DELETE:\\n instance.transition_state(Event.DELETE, status_detail_message)\\n else:\\n instance.transition_state(Event.ACTION, status_detail_message)\\n\\n if not action:\\n # Short circuit the create/delete workflow since no action is defined\\n logger.info(\\\"Short-circuiting %s action: action not defined\\\", action_name)\\n if action_name == InstanceAction.CREATE:\\n instance.transition_state(Event.ACTION_SUCCESS, \\\"\\\")\\n elif action_name == InstanceAction.DELETE:\\n instance.transition_state(Event.ACTION_SUCCESS, \\\"Deleted by {}\\\".format(user or \\\"user\\\"))\\n self._persist_instance_updates(instance)\\n if instance.status == InstanceStatus.ACTIVE:\\n self.reqproc.trigger_processor()\\n return instance\\n\\n logger.info(\\\"Launching %s workflow (template: %s) against instance %s\\\", action_name, action['template'], instance)\\n service = {\\n 'name': 'Fixture {} {}'.format(instance.name, action_name),\\n 'notifications': [{\\n \\\"whom\\\": [\\\"fixturemanager\\\"],\\n \\\"when\\\": [\\\"on_completion\\\"]\\n }],\\n 'annotations': {\\n 'instance_id': instance.id,\\n 'action': action_name\\n },\\n 'fixtures': {\\n instance.id: instance.requestdoc()\\n }\\n }\\n if user:\\n service['user'] = user\\n service['notifications'].append({\\n \\\"whom\\\": [\\\"submitter\\\"],\\n \\\"when\\\": [\\\"on_failure\\\", \\\"on_success\\\"]\\n })\\n # Attempt to get the most up-to-date version of the service template to submit for the action.\\n # The template may no longer exist (e.g. the repo was disconnected). If we are unable to find the template\\n # fall back to the version we cache with the class.\\n service_template = self.axops_client.get_template(fix_class.repo, fix_class.branch, action['template'])\\n if service_template:\\n service['template_id'] = service_template['id']\\n else:\\n logger.warning(\\\"Service template '%s' not found in repo %s branch %s. Falling back to cached version\\\", action['template'], fix_class.repo, fix_class.branch)\\n service['template'] = fix_class.action_templates[action['template']]\\n\\n default_args = action.get('arguments', {})\\n if default_args or arguments:\\n service['arguments'] = default_args\\n if arguments is not None:\\n service['arguments'].update(arguments)\\n for arg_name, arg_val in service['arguments'].items():\\n service['arguments'][arg_name] = substitute_attributes(arg_val, instance)\\n\\n logger.debug(\\\"Submitting service:\\\\n%s\\\", pretty_json(service))\\n service = self.axops_client.create_service(service)\\n instance.operation = {\\n 'id': service['id'],\\n 'name': service['name'],\\n }\\n logger.info(\\\"Service created: %s\\\", instance.operation)\\n self._persist_instance_updates(instance)\\n return instance\",\n \"def main():\\n utils.vip_main(actuator_agent, identity='platform.d.actuator')\",\n \"def on_action(self, message):\\n with self.handler.wrapee as wrapee:\\n log.debug(\\\"Calling {method} on {name}\\\", method=message['action'], name=self.name)\\n try:\\n func = getattr(wrapee, message['action'])\\n except AttributeError as ex:\\n log.warn(\\\"Trying to call a method {method} that does not exsist!\\\",\\n method=ex.args[0])\\n return\\n res, msg = func(*message['args'])\\n if not res:\\n log.warn(\\\"Error while calling {method}: {msg}\\\", msg=msg,\\n method=message['action'])\\n else:\\n log.debug(\\\"Called method succesfully\\\")\\n for protocol in self.service.protocols:\\n protocol.send_packet()\\n if msg != '':\\n protocol.send_news(msg)\",\n \"def do_command(self, args):\\n hostops = dbops.Hosts()\\n hostops.state(args)\",\n \"def ssh(ssh, app, command):\\n if ssh is None:\\n ssh = _DEFAULT_SSH\\n\\n if app.find('#') == -1:\\n # Instance is not specified, list matching and exit.\\n raise click.BadParameter('Specify full instance name: xxx#nnn')\\n\\n app_discovery = discovery.Discovery(context.GLOBAL.zk.conn, app, 'ssh')\\n app_discovery.sync()\\n\\n # Restore default signal mask disabled by python spawning new thread\\n # for Zk connection.\\n #\\n # TODO: should this be done as part of zkutils.connect?\\n for sig in range(1, signal.NSIG):\\n try:\\n signal.signal(sig, signal.SIG_DFL)\\n except OSError:\\n pass\\n\\n # TODO: not sure how to handle mutliple instances.\\n for (app, hostport) in app_discovery.items():\\n _LOGGER.info('%s :: %s', app, hostport)\\n if hostport:\\n host, port = hostport.split(b':')\\n run_ssh(host, port, ssh, list(command))\",\n \"def launch_instance(ec2, ami, itype, kp_name, sec_group_name):\\n\\n\\n instance = ec2.run_instances(\\n ami,\\n key_name=kp_name,\\n instance_type=itype,\\n security_groups=[sec_group_name]\\n ).instances[0]\\n\\n while instance.state != 'running':\\n sys.stdout.write('Waiting for instance: {}, at DNS: {} to start\\\\n'.format(instance.id,\\n str(instance.dns_name).split('.')[0]))\\n time.sleep(5)\\n instance.update()\\n\\n sys.stdout.write('\\\\nSuccess! EC2 Instance Launched \\\\nInstance_Type: {} in {}'.format(instance.instance_type,\\n instance.placement))\\n return instance\",\n \"def Execute(self, client_ip, interface_name=None, interface_mac=None, csv=False, bash=False, client_user=sfdefaults.client_user, client_pass=sfdefaults.client_pass, debug=False):\\n del self\\n dhcp = Get(**locals())\\n\\n if dhcp:\\n if bash or csv:\\n sys.stdout.write(\\\"true\\\")\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.flush()\\n else:\\n mylog.info(\\\"DHCP is enabled\\\")\\n else:\\n if bash or csv:\\n sys.stdout.write(\\\"false\\\")\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.flush()\\n else:\\n mylog.info(\\\"DHCP is not enabled\\\")\\n return True\",\n \"def _action(self, action, osd, info=None, **kwargs):\\n body = {action: info}\\n self.run_hooks('modify_body_for_action', body, **kwargs)\\n url = '/osds/%s/action' % base.getid(osd)\\n return self.api.client.post(url, body=body)\",\n \"def post(self, request, *args, **kwargs): # NOQA\\n\\n stack = self.get_object()\\n\\n if stack.status not in models.Stack.SAFE_STATES:\\n raise BadRequest('You may not perform an action while the '\\n 'stack is in its current state.')\\n\\n driver_hosts_map = stack.get_driver_hosts_map()\\n total_host_count = len(stack.get_hosts().exclude(instance_id=''))\\n action = request.DATA.get('action', None)\\n args = request.DATA.get('args', [])\\n\\n if not action:\\n raise BadRequest('action is a required parameter.')\\n\\n # check the individual provider for available actions\\n for driver, hosts in driver_hosts_map.iteritems():\\n available_actions = driver.get_available_actions()\\n if action not in available_actions:\\n raise BadRequest('At least one of the hosts in this stack '\\n 'does not support the requested action.')\\n\\n # All actions other than launch require hosts to be available\\n if action != BaseCloudProvider.ACTION_LAUNCH and total_host_count == 0:\\n raise BadRequest('The submitted action requires the stack to have '\\n 'available hosts. Perhaps you meant to run the '\\n 'launch action instead.')\\n\\n # Hosts may be spread accross different providers, so we need to\\n # handle them differently based on the provider and its implementation\\n driver_hosts_map = stack.get_driver_hosts_map()\\n for driver, hosts in driver_hosts_map.iteritems():\\n\\n # check the action against current states (e.g., starting can't\\n # happen unless the hosts are in the stopped state.)\\n # XXX: Assuming that host metadata is accurate here\\n for host in hosts:\\n if action == driver.ACTION_START and \\\\\\n host.state != driver.STATE_STOPPED:\\n raise BadRequest('Start action requires all hosts to be '\\n 'in the stopped state first. At least '\\n 'one host is reporting an invalid state: '\\n '{0}'.format(host.state))\\n if action == driver.ACTION_STOP and \\\\\\n host.state != driver.STATE_RUNNING:\\n raise BadRequest('Stop action requires all hosts to be in '\\n 'the running state first. At least one '\\n 'host is reporting an invalid state: '\\n '{0}'.format(host.state))\\n if action == driver.ACTION_TERMINATE and \\\\\\n host.state not in (driver.STATE_RUNNING,\\n driver.STATE_STOPPED):\\n raise BadRequest('Terminate action requires all hosts to '\\n 'be in the either the running or stopped '\\n 'state first. At least one host is '\\n 'reporting an invalid state: {0}'\\n .format(host.state))\\n if (\\n action == driver.ACTION_PROVISION or\\n action == driver.ACTION_ORCHESTRATE or\\n action == driver.ACTION_CUSTOM\\n ) and host.state not in (driver.STATE_RUNNING,):\\n raise BadRequest(\\n 'Provisioning actions require all hosts to be in the '\\n 'running state first. At least one host is reporting '\\n 'an invalid state: {0}'.format(host.state))\\n\\n # Kick off the celery task for the given action\\n stack.set_status(models.Stack.EXECUTING_ACTION,\\n models.Stack.PENDING,\\n 'Stack is executing action \\\\'{0}\\\\''.format(action))\\n\\n if action == BaseCloudProvider.ACTION_CUSTOM:\\n\\n task_list = []\\n\\n action_ids = []\\n\\n for command in args:\\n action = models.StackAction(stack=stack)\\n action.host_target = command['host_target']\\n action.command = command['command']\\n action.type = BaseCloudProvider.ACTION_CUSTOM\\n action.start = datetime.now()\\n action.save()\\n\\n action_ids.append(action.id)\\n\\n task_list.append(tasks.custom_action.si(\\n action.id,\\n command['host_target'],\\n command['command']\\n ))\\n\\n task_chain = reduce(or_, task_list)\\n\\n task_chain()\\n\\n ret = {\\n \\\"results_urls\\\": []\\n }\\n\\n for action_id in action_ids:\\n ret['results_urls'].append(reverse(\\n 'stackaction-detail',\\n kwargs={\\n 'pk': action_id,\\n },\\n request=request\\n ))\\n\\n return Response(ret)\\n\\n # Keep track of the tasks we need to run for this execution\\n task_list = []\\n\\n # FIXME: not generic\\n if action in (BaseCloudProvider.ACTION_STOP,\\n BaseCloudProvider.ACTION_TERMINATE):\\n # Unregister DNS when executing the above actions\\n task_list.append(tasks.unregister_dns.si(stack.id))\\n\\n # Launch is slightly different than other actions\\n if action == BaseCloudProvider.ACTION_LAUNCH:\\n task_list.append(tasks.launch_hosts.si(stack.id))\\n task_list.append(tasks.update_metadata.si(stack.id))\\n task_list.append(tasks.cure_zombies.si(stack.id))\\n\\n # Terminate should leverage salt-cloud or salt gets confused about\\n # the state of things\\n elif action == BaseCloudProvider.ACTION_TERMINATE:\\n task_list.append(\\n tasks.destroy_hosts.si(stack.id,\\n delete_hosts=False,\\n delete_security_groups=False)\\n )\\n\\n elif action in (BaseCloudProvider.ACTION_PROVISION,\\n BaseCloudProvider.ACTION_ORCHESTRATE,):\\n # action that gets handled later\\n pass\\n\\n # Execute other actions that may be available on the driver\\n else:\\n task_list.append(tasks.execute_action.si(stack.id, action, *args))\\n\\n # Update the metadata after the action has been executed\\n if action != BaseCloudProvider.ACTION_TERMINATE:\\n task_list.append(tasks.update_metadata.si(stack.id))\\n\\n # Launching requires us to tag the newly available infrastructure\\n if action in (BaseCloudProvider.ACTION_LAUNCH,):\\n tasks.tag_infrastructure.si(stack.id)\\n\\n # Starting and launching requires DNS updates\\n if action in (BaseCloudProvider.ACTION_START,\\n BaseCloudProvider.ACTION_LAUNCH):\\n task_list.append(tasks.register_dns.si(stack.id))\\n\\n # starting, launching, or reprovisioning requires us to execute the\\n # provisioning tasks\\n if action in (BaseCloudProvider.ACTION_START,\\n BaseCloudProvider.ACTION_LAUNCH,\\n BaseCloudProvider.ACTION_PROVISION,\\n BaseCloudProvider.ACTION_ORCHESTRATE):\\n task_list.append(tasks.ping.si(stack.id))\\n task_list.append(tasks.sync_all.si(stack.id))\\n\\n if action in (BaseCloudProvider.ACTION_START,\\n BaseCloudProvider.ACTION_LAUNCH,\\n BaseCloudProvider.ACTION_PROVISION):\\n task_list.append(tasks.highstate.si(stack.id))\\n task_list.append(tasks.global_orchestrate.si(stack.id))\\n task_list.append(tasks.orchestrate.si(stack.id))\\n\\n if action == BaseCloudProvider.ACTION_ORCHESTRATE:\\n task_list.append(tasks.orchestrate.si(stack.id, 2))\\n\\n task_list.append(tasks.finish_stack.si(stack.id))\\n\\n # chain together our tasks using the bitwise or operator\\n task_chain = reduce(or_, task_list)\\n\\n # execute the chain\\n task_chain()\\n\\n # Update all host states\\n stack.get_hosts().update(state='actioning')\\n\\n serializer = self.get_serializer(stack)\\n return Response(serializer.data)\",\n \"def system_api(self, action, in_min, at, all_re, vmhost, other_re, media, member_id):\\n msg = None\\n if action != 'zeroize':\\n if (at == 'now' or (in_min == 0 and at is None)):\\n if self.dev.timeout > 5:\\n self.queue_message(\\\"log\\\", \\\"Decreasing device RPC timeout to 5 seconds.\\\")\\n self.dev.timeout = 5\\n\\n try:\\n self.sw = jnpr.junos.utils.sw.SW(self.dev)\\n if action == 'reboot':\\n if member_id is not None:\\n for m_id in member_id:\\n got = self.sw.reboot(in_min, at, all_re, None, vmhost, other_re, member_id=m_id)\\n else:\\n got = self.sw.reboot(in_min, at, all_re, None, vmhost, other_re)\\n elif action == 'shutdown':\\n got = self.sw.poweroff(in_min, at, None, all_re, other_re)\\n elif action == 'halt':\\n got = self.sw.halt(in_min, at, all_re, other_re)\\n elif action == 'zeroize':\\n got = self.sw.zeroize(all_re, media)\\n else:\\n raise AnsibleError('Relevant action not found')\\n\\n self.queue_message(\\\"log\\\", \\\"RPC executed\\\")\\n if got is None:\\n msg = 'Did not find expected RPC response.'\\n else:\\n msg = '%s successfully initiated. Response got %s' % (action, got)\\n except (self.pyez_exception.RpcTimeoutError) as ex:\\n try:\\n self.close(raise_exceptions=True)\\n # This means the device wasn't already disconnected.\\n raise AnsibleError('%s failed. %s may not have been ' \\\\\\n 'initiated.' % (action, action))\\n except (self.pyez_exception.RpcError,\\n self.pyez_exception.ConnectError):\\n # This is expected. The device has already disconnected.\\n msg = '%s succeeded.' % (action)\\n except (self.pyez_exception.RpcError,\\n self.pyez_exception.ConnectError) as ex:\\n raise AnsibleError('%s failed. Error: %s' % (action, str(ex)))\\n return msg\",\n \"def _call_service(self, action):\\n conf_service = action.get(CONF_SERVICE, action.get(CONF_SERVICE_OLD))\\n self._last_action = action.get(CONF_ALIAS, conf_service)\\n _LOGGER.info(\\\"Executing script %s step %s\\\", self._name,\\n self._last_action)\\n domain, service = split_entity_id(conf_service)\\n data = action.get(CONF_SERVICE_DATA, {})\\n self.hass.services.call(domain, service, data)\",\n \"def invoke_action(body):\\n directives = []\\n if connexion.request.is_json:\\n data = Data.from_dict(body)\\n\\n msg = \\\"Invoking {action} on custom server.\\\".format(action=data.action)\\n\\n reply = Directive(name=\\\"reply\\\", payload={\\\"text\\\": msg}, type=\\\"view\\\")\\n directives.append(reply)\\n responder = Responder(directives=directives, frame={})\\n return responder\",\n \"def execute(self, instance: it.MDPDPTWInstance, carrier: slt.AHDSolution, num_requests: int):\\n pass\",\n \"def dev_dial(action):\\n\\n try:\\n client = AMIClient(address=AUTH_CREDS['address'], port=AUTH_CREDS['port'])\\n client.login(username=AUTH_CREDS['username'], secret=AUTH_CREDS['secret'])\\n\\n future = client.send_action(action)\\n if VERBOSE:\\n print(future.response or \\\"None\\\")\\n\\n client.logoff()\\n\\n except Exception as e:\\n print(\\\"Error: %s\\\" % e.strerror)\\n sys.exit(1)\",\n \"def send_command(client, device_label, device_command, device_hold_secs=0):\\n device_id = devices[device_label]['id']\\n func = client.send_command(device_id, device_command, device_hold_secs)\\n run_in_loop_now('send_command', func)\\n print(\\\"Sent: \\\" + device_command + \\\" to \\\" + device_label)\\n return\",\n \"def _invoke(self):\\n with self.storlet_logger.activate(),\\\\\\n self._activate_invocation_descriptors():\\n self._send_execute_command()\",\n \"def __init__(self):\\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State\",\n \"def answerDHCP(self, shouldAnswer):\\n assert False, \\\"Deriving class must implement\\\"\",\n \"def execute(self):\\n self.advertise_and_connect()\\n self.display_data()\\n self.disconnect()\",\n \"def take_action(self, action):\\n getattr(self, action['func'])(\\n *action.get('args', ()), \\n **action.get('kwargs', {})\\n )\",\n \"def do_action(self, action, a=None, b=None):\\n pass\",\n \"def _execute(self):\\n\\n action_name = self.action.lower()\\n method_name = action_name.replace('node', 'do')\\n method = getattr(self, method_name, None)\\n\\n if method is None:\\n reason = 'Unsupported action: %s' % self.action\\n EVENT.error(self, consts.PHASE_ERROR, reason)\\n return self.RES_ERROR, reason\\n\\n return method()\",\n \"def run(self):\\n if not self.stopResponder:\\n try:\\n if not exploit.is_authorized(self.target_ip):\\n print 'Tyring to get authorization to {0}...'.format(self.target_ip)\\n exploit.auto_connect(self.src_ip, self.target_ip)\\n time.sleep(5)\\n self.run()\\n except Exception as e:\\n #print ('[!] Error occured,' , e)\\n if not exploit.is_reachable(self.target_ip):\\n \\tprint '[!] Target no longer reachable'\\n else:\\n \\tprint ('[!] Error occured,' , e)\\n #uncomment line below if you would like it to keep retrying to connect to host even if it was unreachable\\n self.run()\\n #print('Exiting responder...')\",\n \"def mainProv(event, portId, ipAddr=''):\\n\\tif event == 'create':\\n\\t\\thostName = genHostname(ipAddr)\\n\\t\\tktabUpdate(hostName,action='create')\\n\\t\\tdnsUpdate(portId=portId, ipAddr=ipAddr, action='create')\\n\\tif event == 'destroy':\\n\\t\\thostName = dnsUpdate(portId, action='delete')\\n\\t\\tktabUpdate(hostName, action='delete')\",\n \"def execute(self) -> None:\\n self.command(self.target)\",\n \"def idle_handler(self):\\n if self.state == _STATE.INIT:\\n self._activate()\\n elif self.state == _STATE.ACTIVE:\\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\\n self.record_result('dhcp', state='run')\\n self.dhcp_monitor.start()\\n elif self.state == _STATE.BASE:\\n self._base_start()\",\n \"def main():\\n\\n args = TrafficScriptArg(['tx_src_ip', 'tx_dst_ip'])\\n\\n tx_if = args.get_arg('tx_if')\\n rx_if = args.get_arg('rx_if')\\n\\n rxq = RxQueue(rx_if)\\n txq = TxQueue(tx_if)\\n\\n tx_src_ip = args.get_arg('tx_src_ip')\\n tx_dst_ip = args.get_arg('tx_dst_ip')\\n\\n sent_packets = []\\n\\n dhcp_discover = Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\") / \\\\\\n IP(src=tx_src_ip, dst=tx_dst_ip) / \\\\\\n UDP(sport=UDP_SERVICES.bootpc, dport=UDP_SERVICES.bootps) / \\\\\\n BOOTP(op=1,) / \\\\\\n DHCP(options=[(\\\"message-type\\\", \\\"discover\\\"),\\n \\\"end\\\"])\\n\\n sent_packets.append(dhcp_discover)\\n txq.send(dhcp_discover)\\n\\n for _ in range(10):\\n dhcp_discover = rxq.recv(2)\\n if is_discover(dhcp_discover):\\n break\\n else:\\n raise RuntimeError(\\\"DHCP DISCOVER Rx timeout\\\")\\n\\n sys.exit(0)\",\n \"def do_command(self, args):\\n chk_arg_count(args, 3)\\n hostname, command, value = args\\n args = (hostname, value)\\n hostops = dbops.Hosts()\\n if command == 'mem':\\n hostops.memory(args)\\n elif command == 'cores':\\n hostops.cores(args)\\n elif command == 'bits':\\n hostops.bitness(args)\\n else:\\n raise ValueError('Unknown host configuration.')\",\n \"def run_hook(vmname, action):\\n vm = None\\n try:\\n vm = globals()[vmname]\\n except KeyError:\\n print(\\\"No such VM\\\")\\n \\n if vm:\\n try:\\n hook = getattr(vm, action)\\n hook()\\n except AttributeError:\\n print(\\\"Action not supported\\\")\",\n \"def _do_probe(self):\\n self._do_expire() \\n\\n server = self.servers.pop(0)\\n self.servers.append(server)\\n\\n r = arp()\\n r.hwtype = r.HW_TYPE_ETHERNET\\n r.prototype = r.PROTO_TYPE_IP\\n r.opcode = r.REQUEST\\n r.hwdst = ETHER_BROADCAST\\n r.protodst = server #send arp request to server popped above one by one\\n r.hwsrc = self.mac #mac address of switch (connected to controller)\\n r.protosrc = self.service_ip #switch is sending arp\\n e = ethernet(type=ethernet.ARP_TYPE, src=self.mac,\\n dst=ETHER_BROADCAST)\\n e.set_payload(r)\\n # self.log.debug(\\\"ARPing for %s\\\", server)\\n msg = of.ofp_packet_out()\\n msg.data = e.pack()\\n msg.actions.append(of.ofp_action_output(port=of.OFPP_FLOOD)) #flood to all the port \\n msg.in_port = of.OFPP_NONE\\n self.con.send(msg)\\n\\n self.outstanding_probes[server] = time.time() + self.arp_timeout ## disctionary of (IP : expire_time)\\n # {IPAddr('10.0.0.1'): 1612113642.861805})\\n # {IPAddr('10.0.0.2'): 1612113654.816535}) likewise\\n\\n core.callDelayed(self._probe_wait_time, self._do_probe) ## Alternate way for simple timers:\\n # core.callDelayed(10, function to call)\",\n \"def invoke_action(self, td, name, input_value, timeout=None):\\n\\n raise NotImplementedError()\",\n \"def _execute_action(self, action):\\n cmd, _self, args_, kwargs_, return_values = (\\n action.name,\\n action.target, # target is equivalent to the \\\"self\\\" in a method\\n action.args,\\n action.kwargs,\\n action.return_ids,\\n )\\n _self = self._fetch_placeholders_from_ids(_self)\\n args_ = self._fetch_placeholders_from_ids(args_)\\n kwargs_ = self._fetch_placeholders_from_ids(kwargs_)\\n return_values = self._fetch_placeholders_from_ids(return_values)\\n\\n # We can only instantiate placeholders, filter them\\n return_placeholders = []\\n Role.nested_object_traversal(\\n return_values, lambda ph: return_placeholders.append(ph), PlaceHolder\\n )\\n\\n if _self is None:\\n method = self._fetch_package_method(cmd)\\n response = method(*args_, **kwargs_)\\n else:\\n response = getattr(_self, cmd)(*args_, **kwargs_)\\n\\n if not isinstance(response, (tuple, list)):\\n response = (response,)\\n\\n PlaceHolder.instantiate_placeholders(return_placeholders, response)\",\n \"def action(self):\\n pass\",\n \"def action(self):\\n pass\",\n \"def take_action(self, action):\\n\\t\\traise NotImplementedError\",\n \"def dns():\\r\\n print('''\\\\n%s at %s acting as user %s\\r\\n\\\\nDNS Configuration Menu''' % (PACKETMASTER.model, ADDRESS, USERNAME))\\r\\n choice = moves.input('''\\r\\n 1 - Get current DNS configuration\\r\\n 2 - Change DNS configuration\\r\\n 3 - Back\\r\\n 4 - Quit \\\\n\\r\\n Enter selection number: ''')\\r\\n try:\\r\\n choice = int(choice)\\r\\n except ValueError as reason:\\r\\n print(\\\"That is not a valid selection.\\\", reason)\\r\\n dns()\\r\\n execute = {1: PACKETMASTER.get_dns,\\r\\n 2: PACKETMASTER.set_dns_guided,\\r\\n 3: hardwareconfig,\\r\\n 4: exit}\\r\\n if choice in execute:\\r\\n try:\\r\\n select = execute[choice]\\r\\n run = select()\\r\\n print(run)\\r\\n dns()\\r\\n except KeyError as reason:\\r\\n print(reason)\\r\\n else:\\r\\n print(\\\"That is not a valid selection.\\\")\\r\\n dns()\",\n \"async def perform_action(self) -> None:\",\n \"def apply_action(self, cmd_name, *args):\\n\\n action = Action(self.tahoma_device.url)\\n action.add_command(cmd_name, *args)\\n self.controller.apply_actions(\\\"HomeAssistant\\\", [action])\",\n \"def _device_action(self, device_ids, action_type, options=None):\\n request = {\\\"action_type\\\": action_type, \\\"device_id\\\": device_ids}\\n if options:\\n request[\\\"options\\\"] = options\\n return self._raw_device_action(request)\",\n \"def __call__(self):\\n action = self.args.get('action', None)\\n if not hasattr(self, 'action_%s' % (action,)):\\n action = 'plugin_root'\\n\\n action_method = getattr(self, 'action_%s' % (action, ))\\n return action_method()\",\n \"def test_execute_host_maintenance(self):\\n self.addCleanup(self.rollback_compute_nodes_status)\\n instances = self._create_one_instance_per_host_with_statistic()\\n hostname = instances[0].get('OS-EXT-SRV-ATTR:hypervisor_hostname')\\n audit_parameters = {\\\"maintenance_node\\\": hostname}\\n\\n _, goal = self.client.show_goal(self.GOAL)\\n _, strategy = self.client.show_strategy(\\\"host_maintenance\\\")\\n _, audit_template = self.create_audit_template(\\n goal['uuid'], strategy=strategy['uuid'])\\n\\n self.assertTrue(test_utils.call_until_true(\\n func=functools.partial(\\n self.has_action_plans_finished),\\n duration=600,\\n sleep_for=2\\n ))\\n\\n _, audit = self.create_audit(\\n audit_template['uuid'], parameters=audit_parameters)\\n\\n try:\\n self.assertTrue(test_utils.call_until_true(\\n func=functools.partial(\\n self.has_audit_finished, audit['uuid']),\\n duration=600,\\n sleep_for=2\\n ))\\n except ValueError:\\n self.fail(\\\"The audit has failed!\\\")\\n\\n _, finished_audit = self.client.show_audit(audit['uuid'])\\n if finished_audit.get('state') in ('FAILED', 'CANCELLED'):\\n self.fail(\\\"The audit ended in unexpected state: %s!\\\" %\\n finished_audit.get('state'))\\n\\n _, action_plans = self.client.list_action_plans(\\n audit_uuid=audit['uuid'])\\n action_plan = action_plans['action_plans'][0]\\n\\n _, action_plan = self.client.show_action_plan(action_plan['uuid'])\\n _, action_list = self.client.list_actions(\\n action_plan_uuid=action_plan[\\\"uuid\\\"])\\n\\n if action_plan['state'] in ('SUPERSEDED', 'SUCCEEDED'):\\n # This means the action plan is superseded so we cannot trigger it,\\n # or it is empty.\\n return\\n for action in action_list['actions']:\\n self.assertEqual('PENDING', action.get('state'))\\n\\n # Execute the action by changing its state to PENDING\\n _, updated_ap = self.client.start_action_plan(action_plan['uuid'])\\n\\n self.assertTrue(test_utils.call_until_true(\\n func=functools.partial(\\n self.has_action_plan_finished, action_plan['uuid']),\\n duration=600,\\n sleep_for=2\\n ))\\n _, finished_ap = self.client.show_action_plan(action_plan['uuid'])\\n _, action_list = self.client.list_actions(\\n action_plan_uuid=finished_ap[\\\"uuid\\\"])\\n self.assertIn(updated_ap['state'], ('PENDING', 'ONGOING'))\\n self.assertIn(finished_ap['state'], ('SUCCEEDED', 'SUPERSEDED'))\\n\\n for action in action_list['actions']:\\n self.assertEqual('SUCCEEDED', action.get('state'))\",\n \"def run(self):\\n try:\\n self.ssh_connection.connect()\\n dns_response = self.query_dns_server()\\n result = self.process_dns_response(dns_response)\\n self.handle_result(result)\\n\\n except Exception as e:\\n print(f\\\"Error: {str(e)}\\\")\\n sys.exit(2)\",\n \"def handle_action_received(msg: ReceiveMessage) -> None:\\n payload = self.render_template(msg, CONF_ACTION_TEMPLATE)\\n if not payload or payload == PAYLOAD_NONE:\\n _LOGGER.debug(\\n \\\"Invalid %s action: %s, ignoring\\\",\\n [e.value for e in HVACAction],\\n payload,\\n )\\n return\\n try:\\n self._attr_hvac_action = HVACAction(str(payload))\\n except ValueError:\\n _LOGGER.warning(\\n \\\"Invalid %s action: %s\\\",\\n [e.value for e in HVACAction],\\n payload,\\n )\\n return\\n get_mqtt_data(self.hass).state_write_requests.write_state_request(self)\",\n \"def performOpen(self, options={}):\\n self.switch = USB_Digital_Switch() # Create an instance of the switch class\\n self.serial_number = str(self.getAddress())\\n\\n status = self.establish_connection() # Connect the switch (pass the serial number as an argument if required)\\n if status > 0:\\n resp = self.switch.Send_SCPI(\\\":MN?\\\", \\\"\\\") # Read model name\\n self.model_number = str(resp[2])\\n self.setModel(self.model_number)\\n self.log(self.model_number, level = 30)\",\n \"def menu(self, action):\\n if action == self.distrib.CONSULTATION:\\n self.distrib.distrib_state = AttentComptChoisit(self.distrib)\\n return self.distrib.banque.consultation(self.distrib.carte_inseree.obtenir_numero_carte())\\n else:\\n self.distrib.distrib_state = AttenteInformationTransfer(self.distrib)\",\n \"def execute_script(self, action, *args):\\n self.host.cmd(('./%s' + len(args) * ' %s') % (action, *args))\",\n \"def main():\\n executor(option().host)\",\n \"def Run(self):\\n\\n # Execution on the master\\n if self.cmd == 'MASTER':\\n self.PerformMirroredUpgrade()\\n \\n # The rest of the options are executed on remote hosts\\n elif self.cmd == 'CHKDOWN':\\n self.CheckDown()\\n elif self.cmd == 'SETCATVERSION':\\n self.SetCatVersion((str(self.option)==str(True)))\\n else:\\n raise Exception('Unknown cmd: ' + str(self.cmd))\\n \\n if self.pool:\\n t = self.pool\\n self.pool = None\\n del t\",\n \"def action_run(self):\\n pass\",\n \"def handleAction(self, action):\\n\\n if action.deviceAction == indigo.kUniversalAction.EnergyReset:\\n # Reset energy consumed and returned\\n self.resetEnergy()\\n\\n # \\\"Reset\\\" the ui value\\n self.device.updateStateOnServer('accumEnergyTotal', 0.0)\\n elif action.deviceAction == indigo.kUniversalAction.EnergyUpdate:\\n # This will be handled by making a status request\\n self.sendStatusRequestCommand()\\n elif action.deviceAction == indigo.kDeviceAction.RequestStatus:\\n self.sendStatusRequestCommand()\\n else:\\n Shelly.handleAction(self, action)\",\n \"def invoke_jsnapy(self, data, action):\\n try:\\n self.queue_message(\\\"vvvv\\\", \\\"Creating jnpr.jsnapy.SnapAdmin instance.\\\")\\n jsa = jnpr.jsnapy.SnapAdmin()\\n self.queue_message(\\\"vvvv\\\", 'Executing %s action.' % action)\\n if action == 'check':\\n responses = jsa.check(data=data,\\n dev=self.dev,\\n pre_file='PRE',\\n post_file='POST')\\n elif action == 'snapcheck':\\n responses = jsa.snapcheck(data=data,\\n dev=self.dev,\\n file_name='PRE')\\n elif action == 'snap_pre':\\n responses = jsa.snap(data=data,\\n dev=self.dev,\\n file_name='PRE')\\n elif action == 'snap_post':\\n responses = jsa.snap(data=data,\\n dev=self.dev,\\n file_name='POST')\\n else:\\n raise AnsibleError(\\\"Unexpected action: %s.\\\" % (action))\\n self.queue_message(\\\"vvvv\\\", 'The %s action executed successfully' % action)\\n except (pyez_exception.RpcError, pyez_exception.ConnectError) as ex:\\n raise AnsibleError(\\\"Error communicating with the device: %s\\\" % str(ex))\\n\\n results = {}\\n if isinstance(responses, list) and len(responses) == 1:\\n if action in ('snapcheck', 'check'):\\n for response in responses:\\n results['device'] = response.device\\n results['router'] = response.device\\n results['final_result'] = response.result\\n results['total_passed'] = response.no_passed\\n results['total_failed'] = response.no_failed\\n results['test_results'] = response.test_results\\n total_tests = int(response.no_passed) + int(response.no_failed)\\n results['total_tests'] = total_tests\\n pass_percentage = 0\\n if total_tests > 0:\\n pass_percentage = ((int(response.no_passed) * 100) //\\n total_tests)\\n results['passPercentage'] = pass_percentage\\n results['pass_percentage'] = pass_percentage\\n if results['final_result'] == 'Failed':\\n results['msg'] = 'Test Failed: Passed %s, Failed %s' % \\\\\\n (results['total_passed'],\\n results['total_failed'])\\n else:\\n results['msg'] = 'Test Passed: Passed %s, Failed %s' % \\\\\\n (results['total_passed'],\\n results['total_failed'])\\n elif action in ('snap_pre', 'snap_post'):\\n results['msg'] = \\\"The %s action successfully executed.\\\" % (action)\\n else:\\n raise AnsibleError(\\\"Unexpected JSNAPy responses. Type: %s.\\\"\\n \\\"Responses: %s\\\" %\\n (type(responses), str(responses)))\\n return results\",\n \"def device_discovery(endless):\\r\\n click.echo(\\\"start device discovery ...\\\")\\r\\n _device_discovery(endless)\",\n \"def _action(self):\\n pass\",\n \"def choose_action(self):\\r\\n pass\",\n \"def onActionChosen(self, agent, action):\\n\\n pass\",\n \"def main():\\n kernel_params = _parse_kernel_cmdline()\\n api_url = kernel_params.get('ipa-api-url')\\n if api_url is None:\\n _process_error('Mandatory kernel parameter \\\"ipa-api-url\\\" is missing.')\\n\\n boot_mac = kernel_params.get('BOOTIF')\\n if boot_mac is None:\\n _process_error('Cannot define boot interface, \\\"BOOTIF\\\" kernel '\\n 'parameter is missing.')\\n\\n # There is a difference in syntax in BOOTIF variable between pxe and ipxe\\n # boot with Ironic.\\n # For pxe boot the the leading `01-' denotes the device type (Ethernet)\\n # and is not a part of the MAC address\\n if boot_mac.startswith('01-'):\\n boot_mac = boot_mac[3:].replace('-', ':')\\n\\n # FIXME(pas-ha) discover all MACs\\n node = lookup(api_url, [boot_mac])\\n uuid = node['node']['uuid']\\n timeout = node['config']['heartbeat_timeout']\\n\\n heartbeat_url = '{api_url}/v1/heartbeat/{uuid}'.format(api_url=api_url,\\n uuid=uuid)\\n for n in range(_GET_ADDR_MAX_ITERATION):\\n boot_ip = _get_interface_ip(boot_mac)\\n if boot_ip is not None:\\n break\\n time.sleep(_RETRY_INTERVAL)\\n else:\\n _process_error('Cannot find IP address of boot interface.')\\n\\n heartbeat(heartbeat_url, boot_ip, timeout)\",\n \"def _invoke_cmd(self, cmd):\\n if cmd in self.COMMANDS:\\n self.COMMANDS[cmd]()\\n else:\\n print(ERROR_UNKNOWN_COMMAND.format(cmd=cmd))\",\n \"def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\\\"\\\"):\\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\\n cli_type = \\\"klish\\\" if cli_type in [\\\"rest-put\\\", \\\"rest-patch\\\"] else cli_type\\n retry_count = data.retry_count if \\\"retry_count\\\" in data and data.retry_count else 0\\n iteration = data.iteration if \\\"iteration\\\" in data and data.iteration else 300\\n delay = data.delay if \\\"delay\\\" in data and data.delay else 3\\n if \\\"func_name\\\" in data:\\n syslog_file_names = [\\\"syslog_1_{}\\\".format(data.func_name), \\\"syslog_{}\\\".format(data.func_name)]\\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\\n if \\\"config_file_type\\\" in data and data.config_file_type == \\\"text\\\":\\n file_path = \\\"/tmp/file_temp.json\\\"\\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\\\"server\\\")\\n elif \\\"config_file_type\\\" in data and data.config_file_type == \\\"EoL\\\":\\n file_path = \\\"\\\"\\n else:\\n file_path = basic_obj.write_to_json_file(data.json_content)\\n if file_path:\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"config_db_location\\\" in data and data.config_db_location == \\\"json\\\":\\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"scenario\\\" in data and data.scenario == \\\"invalid-json\\\":\\n st.log(\\\"Writing invalid content to make invalid json ...\\\")\\n basic_obj.write_to_file_to_line(ssh_conn_obj, \\\",\\\", 5, destination_path, \\\"server\\\")\\n if data.option_type == \\\"67\\\":\\n st.log(\\\"Creating {} file on DHCP server ...\\\".format(data.config_file))\\n data.search_pattern = r'\\\\s*option\\\\s+bootfile-name\\\\s*\\\\S*\\\\s*\\\"\\\\S+\\\";'\\n data.option_string = \\\"option bootfile-name\\\"\\n if data.type == \\\"http\\\":\\n data.option_url = \\\"http://{}{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"tftp\\\":\\n data.option_url = \\\"tftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"ftp\\\":\\n data.option_url = \\\"ftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n write_option_to_dhcp_server(ssh_conn_obj, data)\\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\\n st.log(\\\"{} service not running\\\".format(data.dhcp_service_name))\\n st.report_fail(\\\"service_not_running\\\", data.dhcp_service_name)\\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\\n data.device_action = \\\"reboot\\\" if cli_type == \\\"klish\\\" else data.device_action\\n if data.device_action == \\\"reboot\\\":\\n reboot_type = data.reboot_type if \\\"reboot_type\\\" in data and data.reboot_type else \\\"normal\\\"\\n basic_obj.remove_file(dut, data.config_db_path)\\n st.reboot(dut, reboot_type, skip_port_wait=True)\\n st.wait_system_status(dut, 500)\\n elif data.device_action == \\\"run\\\":\\n ztp_operations(dut, data.device_action)\\n if \\\"band_type\\\" in data and data.band_type==\\\"inband\\\":\\n if not basic_obj.poll_for_system_status(dut):\\n st.log(\\\"Sytem is not ready ..\\\")\\n st.report_env_fail(\\\"system_not_ready\\\")\\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\\\"up\\\"):\\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \\\"down\\\")\\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \\\"up\\\")\\n if interface_status is not None:\\n if not interface_status:\\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\\n if \\\"service\\\" in data and data.service == \\\"disable\\\":\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"stop\\\")\\n if basic_obj.verify_service_status(dut, \\\"ztp\\\"):\\n st.log(\\\"ZTP status is not stopped\\\")\\n st.report_fail(\\\"service_not_stopped\\\", \\\"ztp\\\")\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"start\\\")\\n if not poll_ztp_status(dut, [\\\"IN-PROGRESS\\\", \\\"Not Started\\\", \\\"SUCCESS\\\"], cli_type=cli_type):\\n st.report_fail(\\\"ztp_max_polling_interval\\\")\\n if \\\"check\\\" in data and data.check == \\\"not\\\":\\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n else:\\n st.log(\\\"Iteration count {}\\\".format(iteration))\\n st.log(\\\"REBOOT ON SUCCESS - {}\\\".format(reboot_on_success))\\n if reboot_on_success:\\n if \\\"configdb-json\\\" in reboot_on_success:\\n st.wait_system_reboot(dut)\\n st.wait_system_status(dut, 300)\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\\n else:\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\\n if not result:\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n if reboot_on_success:\\n output = show_ztp_status(dut, cli_type=cli_type)\\n if output[\\\"status\\\"] != \\\"SUCCESS\\\":\\n st.wait(300, \\\"Waiting for device to reboot after success...\\\")\\n st.wait_system_status(dut, 300)\\n # st.wait_system_reboot(dut)\\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n st.banner(boot_up_obj.sonic_installer_list(dut))\\n verify_ztp_filename_logs(dut, data)\\n if \\\"ztp_log_string\\\" in data and data.ztp_log_string:\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path, data.ztp_log_string))\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path_1, data.ztp_log_string))\\n st.report_fail(\\\"ztp_log_verification_failed\\\", data.ztp_log_path_1, data.ztp_log_string)\\n if \\\"result\\\" in data and data.result == \\\"pass\\\":\\n st.report_pass(\\\"test_case_passed\\\")\",\n \"def control(self, action):\\r\\n cfunc = lib_importer.windll.DAQmxTaskControl\\r\\n if cfunc.argtypes is None:\\r\\n with cfunc.arglock:\\r\\n if cfunc.argtypes is None:\\r\\n cfunc.argtypes = [\\r\\n lib_importer.task_handle, ctypes.c_int]\\r\\n\\r\\n error_code = cfunc(\\r\\n self._handle, action.value)\\r\\n check_for_error(error_code)\",\n \"def act(self, device):\\n with open(device, 'r') as fd:\\n result = fcntl.ioctl(fd, self.ioctl)\\n if result:\\n raise Exception(\\\"ioctl failed with result {0}\\\".format(result))\",\n \"def ssh_to_ec2(instance):\\n subprocess.Popen(['ssh', instance.dns_name])\",\n \"def execute(self, virtual_machine):\\n self.function(virtual_machine)\",\n \"def do_action(self, _action: action.Action) -> None:\\n if isinstance(_action, action.Attack):\\n self.do_attack_action(_action)\\n elif isinstance(_action, action.Move):\\n self.do_move_action(_action)\\n else:\\n raise NotImplementedError(f\\\"Action {type(_action)} not implemented!\\\")\",\n \"def action(self):\\n self.action_thread = Thread(target=self._action_then_signal, daemon=True)\\n self.action_thread.start()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61030775","0.5921867","0.58629996","0.5762756","0.57204163","0.56761485","0.56761485","0.55990696","0.55990696","0.5535578","0.55226314","0.54966766","0.54925567","0.5439608","0.541157","0.54060113","0.5376972","0.537439","0.53660226","0.5343927","0.52919453","0.5275635","0.5256967","0.52348393","0.52302206","0.52247953","0.52247953","0.52041495","0.5194735","0.51724845","0.51475555","0.5116451","0.5085292","0.5043338","0.5034792","0.50115156","0.49872786","0.49856597","0.49711135","0.4964226","0.49534607","0.4928072","0.4923964","0.49145967","0.49011287","0.48966658","0.48831302","0.4875478","0.48717457","0.48700804","0.48694193","0.4861694","0.48557198","0.4848319","0.48448828","0.4843529","0.48422924","0.4838965","0.4838139","0.48084223","0.4798663","0.4782954","0.47800165","0.47469354","0.47457466","0.47317192","0.472524","0.47115877","0.47089773","0.47089773","0.4691352","0.46867505","0.468425","0.46840742","0.46833667","0.46767932","0.46703964","0.46621028","0.46612966","0.4660797","0.46584153","0.4649524","0.46319678","0.4630115","0.46173865","0.46142825","0.46133295","0.46077624","0.46028072","0.45996878","0.45992526","0.45991674","0.45978832","0.4593522","0.45904988","0.45891264","0.45887342","0.45804054","0.45797393","0.4578455"],"string":"[\n \"0.61030775\",\n \"0.5921867\",\n \"0.58629996\",\n \"0.5762756\",\n \"0.57204163\",\n \"0.56761485\",\n \"0.56761485\",\n \"0.55990696\",\n \"0.55990696\",\n \"0.5535578\",\n \"0.55226314\",\n \"0.54966766\",\n \"0.54925567\",\n \"0.5439608\",\n \"0.541157\",\n \"0.54060113\",\n \"0.5376972\",\n \"0.537439\",\n \"0.53660226\",\n \"0.5343927\",\n \"0.52919453\",\n \"0.5275635\",\n \"0.5256967\",\n \"0.52348393\",\n \"0.52302206\",\n \"0.52247953\",\n \"0.52247953\",\n \"0.52041495\",\n \"0.5194735\",\n \"0.51724845\",\n \"0.51475555\",\n \"0.5116451\",\n \"0.5085292\",\n \"0.5043338\",\n \"0.5034792\",\n \"0.50115156\",\n \"0.49872786\",\n \"0.49856597\",\n \"0.49711135\",\n \"0.4964226\",\n \"0.49534607\",\n \"0.4928072\",\n \"0.4923964\",\n \"0.49145967\",\n \"0.49011287\",\n \"0.48966658\",\n \"0.48831302\",\n \"0.4875478\",\n \"0.48717457\",\n \"0.48700804\",\n \"0.48694193\",\n \"0.4861694\",\n \"0.48557198\",\n \"0.4848319\",\n \"0.48448828\",\n \"0.4843529\",\n \"0.48422924\",\n \"0.4838965\",\n \"0.4838139\",\n \"0.48084223\",\n \"0.4798663\",\n \"0.4782954\",\n \"0.47800165\",\n \"0.47469354\",\n \"0.47457466\",\n \"0.47317192\",\n \"0.472524\",\n \"0.47115877\",\n \"0.47089773\",\n \"0.47089773\",\n \"0.4691352\",\n \"0.46867505\",\n \"0.468425\",\n \"0.46840742\",\n \"0.46833667\",\n \"0.46767932\",\n \"0.46703964\",\n \"0.46621028\",\n \"0.46612966\",\n \"0.4660797\",\n \"0.46584153\",\n \"0.4649524\",\n \"0.46319678\",\n \"0.4630115\",\n \"0.46173865\",\n \"0.46142825\",\n \"0.46133295\",\n \"0.46077624\",\n \"0.46028072\",\n \"0.45996878\",\n \"0.45992526\",\n \"0.45991674\",\n \"0.45978832\",\n \"0.4593522\",\n \"0.45904988\",\n \"0.45891264\",\n \"0.45887342\",\n \"0.45804054\",\n \"0.45797393\",\n \"0.4578455\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7318727"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79784,"cells":{"query":{"kind":"string","value":"Sync the local DHCP state with Quantum."},"document":{"kind":"string","value":"def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n active_networks = set(self.plugin_rpc.get_active_networks())\n for deleted_id in known_networks - active_networks:\n self.disable_dhcp_helper(deleted_id)\n\n for network_id in active_networks:\n self.refresh_dhcp_helper(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Unable to sync network state.'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _sync_experiment_state_with_ddb(self):\n if self.local_mode:\n self.sync_thread.sync_experiment_state_with_ddb()","def update(self):\n self._state = get_local_ip()","def sync():\n sync_ssda()","def update(self):\n try:\n if not self._sysinfo:\n self._sysinfo = self.smartplug.sys_info\n self._mac = self.smartplug.mac\n self._model = self.smartplug.model\n if self.smartplug.context is None:\n self._alias = self.smartplug.alias\n self._device_id = self._mac\n else:\n self._alias = self._plug_from_context[\"alias\"]\n self._device_id = self.smartplug.context\n\n if self.smartplug.context is None:\n self._state = self.smartplug.state == self.smartplug.SWITCH_STATE_ON\n else:\n self._state = self._plug_from_context[\"state\"] == 1\n\n if self.smartplug.has_emeter:\n emeter_readings = self.smartplug.get_emeter_realtime()\n\n self._emeter_params[ATTR_CURRENT_POWER_W] = \"{:.2f}\".format(\n emeter_readings[\"power\"]\n )\n self._emeter_params[ATTR_TOTAL_ENERGY_KWH] = \"{:.3f}\".format(\n emeter_readings[\"total\"]\n )\n self._emeter_params[ATTR_VOLTAGE] = \"{:.1f}\".format(\n emeter_readings[\"voltage\"]\n )\n self._emeter_params[ATTR_CURRENT_A] = \"{:.2f}\".format(\n emeter_readings[\"current\"]\n )\n\n emeter_statics = self.smartplug.get_emeter_daily()\n try:\n self._emeter_params[ATTR_TODAY_ENERGY_KWH] = \"{:.3f}\".format(\n emeter_statics[int(time.strftime(\"%e\"))]\n )\n except KeyError:\n # Device returned no daily history\n pass\n\n self._available = True\n\n except (SmartDeviceException, OSError) as ex:\n if self._available:\n _LOGGER.warning(\n \"Could not read state for %s: %s\", self.smartplug.host, ex\n )\n self._available = False","def __init__(self):\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State","def sync(self) -> None: #\n self.__target.load_state_dict(self.__policy.state_dict())","def update(self):\n try:\n if self._remote.power() == 1:\n self._state = STATE_ON\n else:\n self._state = STATE_OFF\n\n # Set TV to be able to remotely power on\n # self._remote.power_on_command_settings(2)\n if self._remote.mute() == 2:\n self._muted = False\n else:\n self._muted = True\n self._volume = self._remote.volume() / 60\n except OSError:\n self._state = STATE_OFF","def set_state(self, is_on: bool) -> None:\n json_data = self.perform_command(self.commands[\"on\"] if is_on else self.commands[\"off\"])\n\n if json_data[\"system\"][\"set_relay_state\"][\"err_code\"] != 0:\n raise Exception(\"Error: Error from the smartplug: \" + json.dumps(json_data))","def update(self):\r\n self._state = self._dev.state","def prepare_device(self):\n self._get_pairing_status()\n self._update_system_clock()\n self._update_system()\n # Above will block during update process and kill this instance if\n # new software is installed\n\n if self.backend_down:\n self._notify_backend_down()\n else:\n self._display_skill_loading_notification()\n self.bus.emit(Message('mycroft.internet.connected'))\n self._ensure_device_is_paired()\n self._update_device_attributes_on_backend()","def update(self):\n try:\n response = requests.get('{}/digital/{}'.format(\n self._resource, self._pin), timeout=10)\n self.data = {'state': response.json()['return_value']}\n except requests.exceptions.ConnectionError:\n _LOGGER.error(\"No route to device '%s'. Is device offline?\",\n self._resource)","def updateState(self):\n self.state = self.microgridPolicy.computeState();","def magma_device_sync():\n\n _libmagma.magma_device_sync()","async def update(self):\n resp = await self._request('get', 'state')\n if resp:\n for line in resp.splitlines():\n key, val = line.strip().split(None, 1)\n if val == 'on' or val == 'off':\n val = (val == 'on')\n self.state_data[key] = val\n else:\n self.state_data[key] = val","def update_dht(self, d_ip, d_port):\n self.dht_ip = d_ip\n self.dht_port = d_port","def sync() -> None:","def update_target_q_network(self):\n assert self.target_network != None\n self.target_network.run_copy()","def dvs_update_network(self):\n self.show_step(1)\n self.env.revert_snapshot(\"dvs_vcenter_systest_setup\")\n\n cluster_id = self.fuel_web.get_last_created_cluster()\n\n self.show_step(2)\n os_ip = self.fuel_web.get_public_vip(cluster_id)\n os_conn = os_actions.OpenStackActions(\n os_ip, SERVTEST_USERNAME,\n SERVTEST_PASSWORD,\n SERVTEST_TENANT)\n\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\n\n net_1 = os_conn.create_network(\n network_name=self.net_data[0].keys()[0],\n tenant_id=tenant.id)['network']\n\n os_conn.create_subnet(\n subnet_name=net_1['name'],\n network_id=net_1['id'],\n cidr=self.net_data[0][self.net_data[0].keys()[0]],\n ip_version=4)\n\n assert_true(os_conn.get_network(net_1['name'])['id'] == net_1['id'])\n\n self.show_step(3)\n os_conn.neutron.update_network(net_1[\"id\"],\n {\"network\": {\"name\": 'net_2'}})\n\n assert_true(os_conn.get_network('net_2')['id'] == net_1['id'])\n\n self.show_step(4)\n default_net = os_conn.nova.networks.find(label=self.inter_net_name)\n os_conn.neutron.update_network(\n default_net.id, {\"network\": {\"name\": 'spring'}})\n\n assert_true(os_conn.get_network('spring')['id'] == default_net.id)","def local(self):\n logging.info(__name__ + ' : Set control to local & locked')\n self.set_remote_status(0)","def set_remote(self):\n self.write_to_serial(':SYST:REM')","def state(config: dict):\n\n async def state_callback(device):\n if device.basic_info is not None:\n if device.available:\n print_device_details(device)\n\n device.shutdown_event_loop()\n\n logger.info(\"Initialising SonoffSwitch with host %s\" % config[\"host\"])\n SonoffSwitch(\n host=config[\"host\"],\n callback_after_update=state_callback,\n logger=logger,\n device_id=config[\"device_id\"],\n api_key=config[\"api_key\"],\n )","def test_update_pci_switch(self):\n pass","def updateMetaAtom (self):\r\n # print (\"Old state DNS: \\n\")\r\n # self.stateDanglingNodes()\r\n synchList = []\r\n synchListState = []\r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n synchList.append(self.mol[i].nodeArray[j])\r\n synchListState.append(synchList[i].state)\r\n #print (\"The original state is: \\n\" + str(synchListState) + \"\\n\")\r\n # Find new state for every node\r\n newStates = []\r\n for i in range(len(synchList)):\r\n oldState = synchList[i].state\r\n synchList[i].calculateState()\r\n newStates.append(synchList[i].state)\r\n synchList[i].state = oldState\r\n \r\n for i in range(len(synchList)):\r\n synchList[i].state = newStates[i]\r\n synchListState[i] = synchList[i].state\r\n \r\n offSet = 0 \r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n self.mol[i].nodeArray[j].state = synchListState[offSet]\r\n offSet += 1\r\n stateMol = []\r\n \r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n stateMol.append(self.mol[i].nodeArray[j].state)\r\n \r\n # print (\"The new state is: \\n\" + str(synchListState) + \"\\n\")\r\n # print (\"The state of the mol array is: \" + str(stateMol) + \"\\n\")\r\n #print (\"Post update \\n\")\r\n self.stateDanglingNodes()\r\n offSet = 0 \r\n oldStateNodes = [] # Store the old state of nodes in molecule\r\n newStateNodes = [] # Stores the new state\r\n # The code below goes through each metaspike and ensures that the dangling nodes have been updated with the correct\r\n # new state\r\n for i in range(len(self.metaSpikes)):\r\n if self.metaSpikes[i].typeSpike == 1:\r\n #print (\"Inside type 1 \\n\")\r\n #print (\"The number of DNs is: \" + str(len(self.metaSpikes[i].danglingNodeList)) + \"\\n\")\r\n for j in range(len(self.metaSpikes[i].danglingNodeList)):\r\n # Find the location of the dangling node in the synch list and change the dangling nodes state to match\r\n # state locted in the synch list\r\n if self.metaSpikes[i].danglingNodeList[j].node in synchList:\r\n oldStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\r\n indexNode = synchList.index(self.metaSpikes[i].danglingNodeList[j].node)\r\n # print (\"The current value is: \" + str(self.metaSpikes[i].danglingNodeList[j].node.state) + \"\\n\")\r\n # print (\"The index of node is: \" + str(indexNode) + \"\\n\")\r\n # print (\"The new value should be: \" + str(synchListState[indexNode]) + \"\\n\")\r\n self.metaSpikes[i].danglingNodeList[j].changeState(synchListState[indexNode]) \r\n newStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\r\n # print (\"Node in list \\n\")\r\n else:\r\n # print (\"The number of DTs is: \" + str(len(self.metaSpikes[i].danglingTailList)) + \"\\n\")\r\n #print (\"Inside type 2 \\n\")\r\n # With dangling tails we need an extra for loop to iterate across each nodelist of the tail\r\n for j in range(len(self.metaSpikes[i].danglingTailList)):\r\n for k in range(len(self.metaSpikes[i].danglingTailList[j].nodeList)):\r\n if self.metaSpikes[i].danglingTailList[j].nodeList[k].node in synchList:\r\n oldStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\r\n indexNode = synchList.index(self.metaSpikes[i].danglingTailList[j].nodeList[k].node)\r\n self.metaSpikes[i].danglingTailList[j].nodeList[k].changeState(synchListState[indexNode]) \r\n newStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\r\n #print (\"Node in list \\n\")\r\n \r\n # print (\"After running update code \\n\")\r\n self.stateDanglingNodes()\r\n # Recalculate the state of the metaatom\r\n self.calculateState()\r\n #print (\"The old state is:\\n\" + str(oldStateNodes) + \"\\n\")\r\n #print (\"The new state is:\\n\" + str(newStateNodes) + \"\\n\")\r\n \r\n \r\n # Next need to give each node in mol its state \r\n \r\n #print (\"Intensity before update: \" + str(self.metaSpikes[i].intensity) + \"\\n\")\r\n #print (\"Intensity after update: \" + str(self.metaSpikes[i].intensity) + \"\\n\")\r\n # Now need to recalculate state\r","def update_network(self, dbnetwork, qipinfo):\n\n # We don't want to add the plenary to self.plenaries if we aren't going\n # to change anything\n plenary = Plenary.get_plenary(dbnetwork)\n updated = False\n\n if dbnetwork.name != qipinfo.name:\n self.logger.client_info(\"Setting network {0!s} name to {1}\"\n .format(dbnetwork, qipinfo.name))\n dbnetwork.name = qipinfo.name\n if dbnetwork.network_type != qipinfo.network_type:\n self.logger.client_info(\"Setting network {0!s} type to {1}\"\n .format(dbnetwork, qipinfo.network_type))\n dbnetwork.network_type = qipinfo.network_type\n if dbnetwork.location != qipinfo.location:\n self.logger.client_info(\"Setting network {0!s} location to {1:l}\"\n .format(dbnetwork, qipinfo.location))\n dbnetwork.location = qipinfo.location\n if dbnetwork.side != qipinfo.side:\n self.logger.client_info(\"Setting network {0!s} side to {1}\"\n .format(dbnetwork, qipinfo.side))\n dbnetwork.side = qipinfo.side\n if dbnetwork.network_compartment != qipinfo.compartment:\n self.logger.client_info(\"Setting network {0!s} compartment to {1!s}\"\n .format(dbnetwork, qipinfo.compartment))\n dbnetwork.network_compartment = qipinfo.compartment\n\n if dbnetwork in self.session.dirty:\n updated = True\n\n old_rtrs = set(dbnetwork.router_ips)\n new_rtrs = set(qipinfo.routers)\n\n del_routers = []\n for router in dbnetwork.routers:\n if router.ip in old_rtrs - new_rtrs:\n del_routers.append(router)\n\n for router in del_routers:\n self.logger.client_info(\"Removing router {0:s} from \"\n \"{1:l}\".format(router.ip, dbnetwork))\n for dns_rec in router.dns_records:\n if dns_rec.is_unused:\n delete_dns_record(dns_rec)\n dbnetwork.routers.remove(router)\n updated = True\n\n for ip in new_rtrs - old_rtrs:\n self.add_router(dbnetwork, ip)\n updated = True\n\n if updated:\n self.plenaries.append(plenary)\n\n # TODO: add support for updating router locations\n\n return dbnetwork.netmask == qipinfo.address.netmask","def _update(self, host):\n pass","def update_host_ovs(self, context):\n LOG.info(_('Updating Open vSwitch host data...'))\n LOG.debug(\"Current DOM: %s\" % self.current_dom.to_dict())\n LOG.debug(\"Requested DOM: %s\" % self.desired_dom.to_dict())\n\n builder = mob.MicroOperationBuilder(context,\n self.current_dom,\n self.desired_dom,\n self.rollback)\n\n mo_list = builder.get_micro_ops_for_update()\n\n # run validation\n return self._run_micro_op_list(mo_list)","def sync(self):\n pass","def sync(self):\n pass","def doSync (self) :\r\n \r\n self.factory.getSyncFor(self)","def _sync(self, message=None):\n if message[0] == self._multi_level_sensor_property.element_uid:\n self._state = self._device_instance.multi_level_sensor_property[\n message[0]\n ].value\n elif message[0].startswith(\"hdm\"):\n self._available = self._device_instance.is_online()\n else:\n _LOGGER.debug(\"No valid message received: %s\", message)\n self.schedule_update_ha_state()","def execute(self, cmd, state):\n state[:] = interface.execute_arm_command(cmd, 0)\n self.env.update() # note that the sim update is called twice, once here and once by the hand's sim_connection","def set_into_managed_mode(wifi_name):\n \n subprocess.run([\"ip\", \"link\", \"set\", wifi_name, \"down\"])\n subprocess.run([\"iwconfig\", wifi_name, \"mode\", \"managed\"])\n subprocess.run([\"ip\", \"link\", \"set\", wifi_name, \"up\"])\n subprocess.run([\"service\", \"NetworkManager\", \"start\"])","def _sync_power_states(self, context):\n\n # Only fetching the necessary fields, will skip synchronizing if\n # target_power_state is not None.\n\n try:\n nodes = self.driver.get_nodes_power_state()\n except Exception as e:\n LOG.warning(\n (\"Failed to retrieve node list when synchronizing power \"\n \"states: %(msg)s\") % {\"msg\": e})\n # Just return if we fail to get nodes real power state.\n return\n\n node_dict = {node.instance_uuid: node for node in nodes\n if node.target_power_state is None}\n\n if not node_dict:\n LOG.warning(\"While synchronizing server power states, \"\n \"found none server with stable power state \"\n \"on the hypervisor.\")\n return\n\n def _sync(db_server, node_power_state):\n # This must be synchronized as we query state from two separate\n # sources, the driver (ironic) and the database. They are set\n # (in stop_server) and read, in sync.\n @utils.synchronized(db_server.uuid)\n def sync_server_power_state():\n self._sync_server_power_state(context, db_server,\n node_power_state)\n\n try:\n sync_server_power_state()\n except Exception:\n LOG.exception(\"Periodic sync_power_state task had an \"\n \"error while processing a server.\",\n server=db_server)\n\n self._syncs_in_progress.pop(db_server.uuid)\n\n db_servers = objects.Server.list(context)\n for db_server in db_servers:\n # process syncs asynchronously - don't want server locking to\n # block entire periodic task thread\n uuid = db_server.uuid\n if uuid in self._syncs_in_progress:\n LOG.debug('Sync power state already in progress for %s', uuid)\n continue\n\n if db_server.status not in (states.ACTIVE, states.STOPPED):\n if db_server.status in states.UNSTABLE_STATES:\n LOG.info(\"During sync_power_state the server has a \"\n \"pending task (%(task)s). Skip.\",\n {'task': db_server.status},\n server=db_server)\n continue\n\n if uuid not in node_dict:\n continue\n\n node_power_state = node_dict[uuid].power_state\n if db_server.power_state != node_power_state:\n LOG.debug('Triggering sync for uuid %s', uuid)\n self._syncs_in_progress[uuid] = True\n self._sync_power_pool.spawn_n(_sync, db_server,\n node_power_state)","def updateStateImage(self):\n\n if self.device.states['online']:\n self.device.updateStateImageOnServer(indigo.kStateImageSel.EnergyMeterOn)\n else:\n self.device.updateStateImageOnServer(indigo.kStateImageSel.EnergyMeterOff)","def sync_with_database(self):\n # learn from db\n lports = self.nb_api.get_all(l2.LogicalPort)\n for lport in lports:\n port_id = \"{}:{}\".format(lport.lswitch.id, lport.id)\n self.cache_logical_port_by_port_id[port_id] = lport\n lrouters = self.nb_api.get_all(l3.LogicalRouter)\n for lrouter in lrouters:\n self.cache_logical_router_by_dpid[lrouter.id] = lrouter","def send_state(self):\n self.state = self.enigma.get_state()\n messages = self.notify_slaves()\n for message in messages:\n self.network.messages_to_slaves.append(message)","def update(self):\n self._client.update()\n\n self._current_operation_mode = self._device.get_run_mode()\n self._target_temperature = self._device.target_temperature\n self._current_temperature = self._device.current_temperature\n self._min_temp = self._device.min_temp\n self._max_temp = self._device.max_temp\n\n self._update_attributes_from_device()\n\n # set whether device is in away mode\n if (\n self._current_operation_mode == CONST_MODE_AWAY\n or self._current_operation_mode == CONST_MODE_FROST\n ):\n self._away = True\n else:\n self._away = False\n\n # set whether device is on/off\n if self._current_operation_mode == CONST_MODE_OFF:\n self._on = False\n else:\n self._on = True","def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n \r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","def set_state(self, state: bool) -> None:\n payload = self._cfg.state_power_on if state else self._cfg.state_power_off\n command = f\"{COMMAND_POWER}{self._cfg.idx+1}\"\n self._mqtt_client.publish(\n self._cfg.command_topic + command,\n payload,\n )","async def async_turn_on(self, **kwargs):\n self._wrap_device.device.set_duct_zone(self._zone, True)","def storage_reset(self):\n daos_srv_bin = os.path.join(self.daosbinpath, \"daos_server\")\n cmd = \"{} storage prepare -n --reset -f\".format(daos_srv_bin)\n result = pcmd(self._hosts, cmd)\n if len(result) > 1 or 0 not in result:\n raise ServerFailed(\"Error resetting NVMe storage\")","def admin_state_up(self, state):\n self.neutron.update_port(self._provision_port_id, {'port': {'admin_state_up': state}})","def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n\r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","async def async_turn_on(self, **kwargs):\n try:\n if await self._api.set_relay_state(self._dev_id, \"on\"):\n self._is_on = True\n self.async_write_ha_state()\n except Smile.PlugwiseError:\n _LOGGER.error(\"Error while communicating to device\")","def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None\n ):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops caused by a constantly changing state value at each run.\n # Example: state.value += 1\n\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None\n ):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","async def async_update_data():\n for device in juicenet.devices:\n await device.update_state(True)\n return True","def resetDeviceStates(self):","def init(self):\n try:\n yield self._connect_dbus()\n logger.info(\"Request the GSM resource\")\n yield WaitFSOResource('GSM', time_out=30)\n yield WaitDBus(self.ousage.RequestResource, 'GSM')\n yield self._turn_on()\n logger.info(\"register on the network\")\n register = yield self._register()\n #if register:\n #provider = yield tichy.Wait(self, 'provider-modified')\n \n self._keep_alive().start()\n \n ##network selection end\n \n except Exception, ex:\n logger.error(\"Error : %s\", ex)\n raise\n \n try:\n \n yield tichy.Service.get('ConfigService').wait_initialized()\n self.config_service = tichy.Service.get(\"ConfigService\")\n logger.info(\"got config service\")\n \n except Exception, ex:\n logger.error(\"Error in try retrieving config service : %s\", ex)\n \n try:\n \n ##call forwaring setting start\n self.values = self.config_service.get_items(\"call_forwarding\")\n if self.values != None: self.values = dict(self.values)\n logger.info(\"realized values is none\")\n\n except Exception, ex:\n logger.error(\"Error in try call forwarding setting : %s\", ex)\n \n \n try:\n\n self.SettingReason = tichy.settings.ListSetting('Call Forwarding','Reason',tichy.Text,value='unconditional', setter=self.ForwardingSetReason,options=[\"unconditional\",\"mobile busy\",\"no reply\",\"not reachable\",\"all\",\"allconditional\"],model=tichy.List([ ListSettingObject(\"unconditional\", self.action),ListSettingObject(\"mobile busy\",self.action),ListSettingObject(\"no reply\", self.action),ListSettingObject(\"not reachable\", self.action),ListSettingObject(\"all\", self.action),ListSettingObject(\"all conditional\", self.action)]), ListLabel =[('title','name')])\n \n self.SettingForwarding = tichy.settings.ToggleSetting('Call Forwarding', 'active', tichy.Text, value=self.GetForwardingStatus('unconditional'),setter=self.ToggleForwarding, options=['active','inactive'])\n \n \n except Exception, ex:\n logger.error(\"Error in try call forwarding setting list : %s\", ex)\n \n \n try:\n\n self.SettingChannels = tichy.settings.Setting('Call Forwarding', 'channels', tichy.Text, value=self.ForwardingGet('class'), setter=self.ForwardingSetClass, options=[\"voice\",\"data\",\"voice+data\",\"fax\",\"voice+data+fax\"])\n \n self.SettingTargetNumber = tichy.settings.NumberSetting('Call Forwarding', 'Target Number', tichy.Text, value=self.ForwardingGet('number'), setter=self.ForwardingSetNumber)\n \n self.SettingTargetNumber = tichy.settings.NumberSetting('Call Forwarding', 'Timeout', tichy.Text, value=self.ForwardingGet('timeout'), setter=self.ForwardingSetTimeout)\n \n ##call forwaring setting stop\n \n \n except Exception, ex:\n logger.error(\"Error in try Error in try call forwarding setting : %s\", ex)\n \n try:\n\n ##call identifaction setting start\n self.CallIdentification = tichy.settings.Setting('Network', 'Call Identification', tichy.Text, value=self.GetCallIdentification(), setter=self.SetCallIdentifaction, options=[\"on\",\"off\",\"network\"])\n ##call identifaction setting stop\n \n except Exception, ex:\n logger.error(\"Error in network identification setting: %s\", ex)\n \n try: \n ##network selection etc begin\n self.NetworkRegistration = tichy.settings.Setting('Network', 'Registration', tichy.Text, value=self.GetRegStatus(), setter=self.SetRegStatus, options=[\"registered\",\"not registered\"])\n \n \n except Exception, ex:\n logger.error(\"Error in network registration setting : %s\", ex)\n \n \n try:\n \n self.scanning = False\n self.NetworkList = tichy.List()\n self.ListLabel = [('title','name'),('subtitle','status')]\n \n self.scan_setting = tichy.settings.ListSetting('Network', 'List', tichy.Text, value=\"scan\", setter=self.run_scan, options=['scan'], model=self.NetworkList, ListLabel=self.ListLabel)\n \n except Exception, ex:\n logger.error(\"Error in network list setting : %s\", ex)\n #raise","def light_sync(self):","def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None)","def update_target_network(self):\n\n\t\tprint \"Updating Target DQN...\"\n\t\t\n\t\tself.update_operation.run()","def _sync_instance_power_state(self, context, db_instance, vm_power_state,\n use_slave=False):\n\n # We re-query the DB to get the latest instance info to minimize\n # (not eliminate) race condition.\n db_instance.refresh(use_slave=use_slave)\n db_power_state = db_instance.power_state\n vm_state = db_instance.vm_state\n\n if self.host != db_instance.host:\n # on the sending end of cloud-cloud _sync_power_state\n # may have yielded to the greenthread performing a live\n # migration; this in turn has changed the resident-host\n # for the VM; However, the instance is still active, it\n # is just in the process of migrating to another host.\n # This implies that the cloud source must relinquish\n # control to the cloud destination.\n LOG.info(_LI(\"During the sync_power process the \"\n \"instance has moved from \"\n \"host %(src)s to host %(dst)s\"),\n {'src': db_instance.host,\n 'dst': self.host},\n instance=db_instance)\n return\n elif db_instance.task_state is not None:\n # on the receiving end of cloud-cloud, it could happen\n # that the DB instance already report the new resident\n # but the actual VM has not showed up on the hypervisor\n # yet. In this case, let's allow the loop to continue\n # and run the state sync in a later round\n LOG.info(_LI(\"During sync_power_state the instance has a \"\n \"pending task (%(task)s). Skip.\"),\n {'task': db_instance.task_state},\n instance=db_instance)\n return\n\n orig_db_power_state = db_power_state\n if vm_power_state != db_power_state:\n LOG.info(_LI('During _sync_instance_power_state the DB '\n 'power_state (%(db_power_state)s) does not match '\n 'the vm_power_state from the hypervisor '\n '(%(vm_power_state)s). Updating power_state in the '\n 'DB to match the hypervisor.'),\n {'db_power_state': db_power_state,\n 'vm_power_state': vm_power_state},\n instance=db_instance)\n # power_state is always updated from hypervisor to db\n db_instance.power_state = vm_power_state\n db_instance.save()\n db_power_state = vm_power_state\n\n # Note(maoy): Now resolve the discrepancy between vm_state and\n # vm_power_state. We go through all possible vm_states.\n if vm_state in (vm_states.BUILDING,\n vm_states.RESCUED,\n vm_states.RESIZED,\n vm_states.SUSPENDED,\n vm_states.ERROR):\n # TODO(maoy): we ignore these vm_state for now.\n pass\n elif vm_state == vm_states.ACTIVE:\n # The only rational power state should be RUNNING\n if vm_power_state in (power_state.SHUTDOWN,\n power_state.CRASHED):\n LOG.warning(_LW(\"Instance shutdown by itself. Calling the \"\n \"stop API. Current vm_state: %(vm_state)s, \"\n \"current task_state: %(task_state)s, \"\n \"original DB power_state: %(db_power_state)s, \"\n \"current VM power_state: %(vm_power_state)s\"),\n {'vm_state': vm_state,\n 'task_state': db_instance.task_state,\n 'db_power_state': orig_db_power_state,\n 'vm_power_state': vm_power_state},\n instance=db_instance)\n try:\n # Note(maoy): here we call the API instead of\n # brutally updating the vm_state in the database\n # to allow all the hooks and checks to be performed.\n if db_instance.shutdown_terminate:\n self.compute_api.delete(context, db_instance)\n else:\n self.compute_api.stop(context, db_instance)\n except Exception:\n # Note(maoy): there is no need to propagate the error\n # because the same power_state will be retrieved next\n # time and retried.\n # For example, there might be another task scheduled.\n LOG.exception(_LE(\"error during stop() in \"\n \"sync_power_state.\"),\n instance=db_instance)\n elif vm_power_state == power_state.SUSPENDED:\n LOG.warning(_LW(\"Instance is suspended unexpectedly. Calling \"\n \"the stop API.\"), instance=db_instance)\n try:\n self.compute_api.stop(context, db_instance)\n except Exception:\n LOG.exception(_LE(\"error during stop() in \"\n \"sync_power_state.\"),\n instance=db_instance)\n elif vm_power_state == power_state.PAUSED:\n # Note(maoy): a VM may get into the paused state not only\n # because the user request via API calls, but also\n # due to (temporary) external instrumentations.\n # Before the virt layer can reliably report the reason,\n # we simply ignore the state discrepancy. In many cases,\n # the VM state will go back to running after the external\n # instrumentation is done. See bug 1097806 for details.\n LOG.warning(_LW(\"Instance is paused unexpectedly. Ignore.\"),\n instance=db_instance)\n elif vm_power_state == power_state.NOSTATE:\n # Occasionally, depending on the status of the hypervisor,\n # which could be restarting for example, an instance may\n # not be found. Therefore just log the condition.\n LOG.warning(_LW(\"Instance is unexpectedly not found. Ignore.\"),\n instance=db_instance)\n elif vm_state == vm_states.STOPPED:\n if vm_power_state not in (power_state.NOSTATE,\n power_state.SHUTDOWN,\n power_state.CRASHED):\n LOG.warning(_LW(\"Instance is not stopped. Calling \"\n \"the stop API. Current vm_state: %(vm_state)s,\"\n \" current task_state: %(task_state)s, \"\n \"original DB power_state: %(db_power_state)s, \"\n \"current VM power_state: %(vm_power_state)s\"),\n {'vm_state': vm_state,\n 'task_state': db_instance.task_state,\n 'db_power_state': orig_db_power_state,\n 'vm_power_state': vm_power_state},\n instance=db_instance)\n try:\n # NOTE(russellb) Force the stop, because normally the\n # cloud API would not allow an attempt to stop a stopped\n # instance.\n self.compute_api.force_stop(context, db_instance)\n except Exception:\n LOG.exception(_LE(\"error during stop() in \"\n \"sync_power_state.\"),\n instance=db_instance)\n elif vm_state == vm_states.PAUSED:\n if vm_power_state in (power_state.SHUTDOWN,\n power_state.CRASHED):\n LOG.warning(_LW(\"Paused instance shutdown by itself. Calling \"\n \"the stop API.\"), instance=db_instance)\n try:\n self.compute_api.force_stop(context, db_instance)\n except Exception:\n LOG.exception(_LE(\"error during stop() in \"\n \"sync_power_state.\"),\n instance=db_instance)\n elif vm_state in (vm_states.SOFT_DELETED,\n vm_states.DELETED):\n if vm_power_state not in (power_state.NOSTATE,\n power_state.SHUTDOWN):\n # Note(maoy): this should be taken care of periodically in\n # _cleanup_running_deleted_instances().\n LOG.warning(_LW(\"Instance is not (soft-)deleted.\"),\n instance=db_instance)","def init_host(self, host=socket.gethostname()):\n ctxt = context.get_admin_context()\n\n LOG.debug('Hostname: %s' % (host,))\n LOG.debug('Instances: %s' % (db.instance_get_all_by_host(ctxt, host)))\n \n for instance in db.instance_get_all_by_host(ctxt, host):\n try:\n LOG.debug('Checking state of %s' % instance['name'])\n state = self.get_info(instance['name'])['state']\n except exception.NotFound:\n state = power_state.SHUTOFF\n\n LOG.debug('Current state of %s was %s.' %\n (instance['name'], state))\n db.instance_set_state(ctxt, instance['id'], state)\n\n if state == power_state.SHUTOFF:\n db.instance_destroy(ctxt, instance['id'])\n\n if state != power_state.RUNNING:\n continue","def update(self):\n\n self._state = get_balance(self.addresses)","def test_set_state_full(self):\n host = synthetic_host(\"myserver\")\n self.create_simple_filesystem(host)\n mgt_uri = \"/api/target/%s/\" % self.mgt.id\n with mock.patch(\"chroma_core.models.Command.set_state\", mock.Mock(return_value=None)):\n self.api_set_state_full(mgt_uri, \"unmounted\")\n Command.set_state.assert_called_once()","async def async_update(self) -> None:\n if (\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\n == BSH_POWER_ON\n ):\n self._state = True\n elif (\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\n == self.device.power_off_state\n ):\n self._state = False\n elif self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(\n ATTR_VALUE, None\n ) in [\n \"BSH.Common.EnumType.OperationState.Ready\",\n \"BSH.Common.EnumType.OperationState.DelayedStart\",\n \"BSH.Common.EnumType.OperationState.Run\",\n \"BSH.Common.EnumType.OperationState.Pause\",\n \"BSH.Common.EnumType.OperationState.ActionRequired\",\n \"BSH.Common.EnumType.OperationState.Aborting\",\n \"BSH.Common.EnumType.OperationState.Finished\",\n ]:\n self._state = True\n elif (\n self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(ATTR_VALUE)\n == \"BSH.Common.EnumType.OperationState.Inactive\"\n ):\n self._state = False\n else:\n self._state = None\n _LOGGER.debug(\"Updated, new state: %s\", self._state)","def sync_local(self, other):\n pass # TODO","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def _sync_server_power_state(self, context, db_server,\n node_power_state):\n\n # We re-query the DB to get the latest server info to minimize\n # (not eliminate) race condition.\n db_server.refresh()\n db_power_state = db_server.power_state\n\n if db_server.status not in (states.ACTIVE, states.STOPPED):\n # on the receiving end of mogan-engine, it could happen\n # that the DB server already report the new resident\n # but the actual BM has not showed up on the hypervisor\n # yet. In this case, let's allow the loop to continue\n # and run the state sync in a later round\n LOG.info(\"During sync_power_state the server has a \"\n \"pending task (%(task)s). Skip.\",\n {'task': db_server.task_state},\n server=db_server)\n return\n\n if node_power_state != db_power_state:\n LOG.info('During _sync_server_power_state the DB '\n 'power_state (%(db_power_state)s) does not match '\n 'the node_power_state from the hypervisor '\n '(%(node_power_state)s). Updating power_state in the '\n 'DB to match the hypervisor.',\n {'db_power_state': db_power_state,\n 'node_power_state': node_power_state},\n server=db_server)\n # power_state is always updated from hypervisor to db\n db_server.power_state = node_power_state\n db_server.save()","def _update_target(self):\n self.target_dqn.load_state_dict(self.dqn.state_dict())","def update_state(self, dstate):\n pass","def test_update_with_target_state(self):\n self.switch._target_state = True\n self.port.data = {}\n self.port.data[\"output\"] = \"stale\"\n self.switch.update()\n assert 1.0 == self.port.data[\"output\"]\n assert self.switch._target_state is None\n self.port.data[\"output\"] = \"untouched\"\n self.switch.update()\n assert \"untouched\" == self.port.data[\"output\"]","def reconfigure(self):\n log.debug('Reconfiguring and restarting the DHCP daemon...')\n\n # Don't set the daemon running status here, but let the status\n # check take care of that.\n\n p = Properties(self.storage, CONFIG_SECTION)\n p.addCallback(self.changed).\\\n addCallback(lambda trigger: p.load()).\\\n addCallback(self.emit_config, p).\\\n addCallback(self.restart_daemon).\\\n addErrback(self.restart_error)","def sync(self):\n acctManager = self.acctManager\n acct = acctManager.account(0)\n gapPolicy = 5\n acct.generateGapAddresses(gapPolicy)\n watchAddresses = set()\n\n # send the initial balance\n self.signals.balance(acct.balance)\n addresses = acct.allAddresses()\n \n # Update the account with known UTXOs.\n chain = self.blockchain\n blockchainUTXOs = chain.UTXOs(addresses)\n acct.resolveUTXOs(blockchainUTXOs)\n\n # Subscribe to block and address updates.\n chain.subscribeBlocks(self.blockSignal)\n watchAddresses = acct.addressesOfInterest()\n if watchAddresses:\n chain.subscribeAddresses(watchAddresses, self.addressSignal)\n # Signal the new balance.\n b = acct.calcBalance(self.blockchain.tip[\"height\"])\n self.signals.balance(b)\n self.save()\n return True","def _send_all_data(self):\n admin_context = qcontext.get_admin_context()\n networks = {}\n ports = {}\n\n all_networks = super(QuantumRestProxyV2,\n self).get_networks(admin_context) or []\n for net in all_networks:\n networks[net.get('id')] = {\n 'id': net.get('id'),\n 'name': net.get('name'),\n 'op-status': net.get('admin_state_up'),\n }\n\n subnets = net.get('subnets', [])\n for subnet_id in subnets:\n subnet = self.get_subnet(admin_context, subnet_id)\n gateway_ip = subnet.get('gateway_ip')\n if gateway_ip:\n # FIX: For backward compatibility with wire protocol\n networks[net.get('id')]['gateway'] = gateway_ip\n\n ports = []\n net_filter = {'network_id': [net.get('id')]}\n net_ports = super(QuantumRestProxyV2,\n self).get_ports(admin_context,\n filters=net_filter) or []\n for port in net_ports:\n port_details = {\n 'id': port.get('id'),\n 'attachment': {\n 'id': port.get('id') + '00',\n 'mac': port.get('mac_address'),\n },\n 'state': port.get('status'),\n 'op-status': port.get('admin_state_up'),\n 'mac': None\n }\n ports.append(port_details)\n networks[net.get('id')]['ports'] = ports\n try:\n resource = '/topology'\n data = {\n 'networks': networks,\n }\n ret = self.servers.put(resource, data)\n if not self.servers.action_success(ret):\n raise RemoteRestError(ret[2])\n return ret\n except RemoteRestError as e:\n LOG.error(_('QuantumRestProxy: Unable to update remote network: '\n '%s'), e.message)\n raise","def configureMaster(self):\n\t\t\n\t\tfin = open('/opt/google/earth/free/drivers.ini', 'r')\n\t\tfout = open('/etc/X11/ge-drivers.ini', 'w')\n\t\t\n\t\tfor line in fin.readlines():\n\t\t\tfout.write(line)\n\t\t\tif line.find('SETTINGS {') != 0:\n\t\t\t\tcontinue\n\t\t\tfout.write('\\tViewSync/send = true\\n')\n\t\t\tfout.write('\\tViewSync/receive = false\\n')\n\n\t\t\tfout.write('\\tViewSync/hostname = %s\\n' %\n\t\t\t\t self.db.getHostAttr('localhost',\n\t\t\t\t\t\t 'Kickstart_PrivateBroadcast'))\n fout.write('\\tViewSync/port = 21567\\n')\n\t\t\tfout.write('\\n')\n\t\t\tfout.write('\\tViewSync/horizFov = 60\\n')\n fout.write('\\tViewSync/rollOffset = 0\\n')\n fout.write('\\tViewSync/yawOffset = 0\\n')\n\t\t\tfout.write('\\tViewSync/pitchOffset = 0\\n')\n\t\t\tfout.write('\\n')\n\n\n\t\tfin.close()\n\t\tfout.close()\n\n\t\tshutil.copy('/etc/X11/ge-drivers.ini', '/opt/google/earth/free/drivers.ini')","def _set_state_internal(self, state=None, temperature=None, force=False):\n try:\n changed = force\n if state and state != self._state.state:\n self._state.state = state\n changed = True\n if temperature is not None and temperature != self._temperature:\n changed = True\n self._temperature = temperature\n finally:\n if changed:\n service.ServiceStateChangeRegistry.notify(self._path, self.get_state())","def sweep_relay():","def _sync_power_states(self, context):\n db_instances = objects.InstanceList.get_by_host(context, self.host,\n expected_attrs=[],\n use_slave=True)\n\n #num_vm_instances = self.driver.get_num_instances()\n vm_instances_stats = self.driver.list_instances_stats()\n num_vm_instances = len(vm_instances_stats)\n num_db_instances = len(db_instances)\n\n if num_vm_instances != num_db_instances:\n LOG.warning(_LW(\"While synchronizing instance power states, found \"\n \"%(num_db_instances)s instances in the database \"\n \"and %(num_vm_instances)s instances on the \"\n \"hypervisor.\"),\n {'num_db_instances': num_db_instances,\n 'num_vm_instances': num_vm_instances})\n\n def _sync(db_instance, state):\n # NOTE(melwitt): This must be synchronized as we query state from\n # two separate sources, the driver and the database.\n # They are set (in stop_instance) and read, in sync.\n @utils.synchronized(db_instance.uuid)\n def query_driver_power_state_and_sync():\n self._query_driver_power_state_and_sync(context, db_instance, state)\n\n try:\n query_driver_power_state_and_sync()\n except Exception:\n LOG.exception(_LE(\"Periodic sync_power_state task had an \"\n \"error while processing an instance.\"),\n instance=db_instance)\n\n self._syncs_in_progress.pop(db_instance.uuid)\n\n for db_instance in db_instances:\n # process syncs asynchronously - don't want instance locking to\n # block entire periodic task thread\n uuid = db_instance.uuid\n if uuid in self._syncs_in_progress:\n LOG.debug('Sync already in progress for %s' % uuid)\n else:\n LOG.debug('Triggering sync for uuid %s' % uuid)\n provider_instance_id = self._get_provider_instance_id(uuid)\n provider_instance_state = vm_instances_stats.get(provider_instance_id,\n power_state.NOSTATE)\n\n self._syncs_in_progress[uuid] = True\n self._sync_power_pool.spawn_n(_sync, db_instance, provider_instance_state)","def _pre_sync(self):","def update(self):\n self.device = self._api.device_query(self._hardware_address, {})","def sync_local_fabric_env(self):\n env.sync_filename = '/tmp/{0}_env.txt'.format(time.time())\n env_copy = self.env\n env_copy.use_ssh_config = False\n env_copy.host = False\n env_copy.host_string = False\n env_copy.local_deployment = True\n # TODO: add context from each need to repopulate\n with self.file.tmpfile(self.to_json(env_copy, cls=SilentEncoder)) as f:\n self.up(f.name, env.sync_filename)","def set_local(self):\n self.write_to_serial(':SYST:LOC')","def restore_network(self):\n Blockade.blockade_join()","def update_cmts_isc_dhcp_config(self, board_config):\n self.setup_dhcp_config(board_config)\n self.setup_dhcp6_config(board_config)\n raise NotImplementedError","def update_host(self, conf, tenant_id, network_id, host_id, body):\n\t\tpass","def update_config():\n \n dburl = dbconn.DbURL()\n conn = dbconn.connect(dburl, utility=True)\n \n logger.info('Updating catalog...')\n sql = \"SELECT gp_activate_standby()\"\n dbconn.execSQL(conn, sql)\n\n conn.commit()\n conn.close()\n\n logger.info('Database catalog updated successful')","async def sync_master(self):\n if not [entity for entity in self._casatunes_entities() if entity.is_client]:\n await self.coordinator.data.zone_master(self.zone_master, False)\n await self.coordinator.async_refresh()\n _LOGGER.debug(\"%s zone is no longer master.\", self.zone_master)","async def _hardcore_setheist(self, ctx):\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n\r\n if config[\"Hardcore\"]:\r\n config[\"Hardcore\"] = False\r\n msg = \"Hardcore mode now OFF.\"\r\n else:\r\n config[\"Hardcore\"] = True\r\n msg = \"Hardcore mode now ON! **Warning** death will result in credit **and chip wipe**.\"\r\n await self.thief.config.guild(guild).Config.set(config)\r\n await ctx.send(msg)","def update(self):\n #self._switch.odlclient._request_json(self._path, method=\"put\", json={\n # \"flow\": self._odl_inventory()\n #})\n self.remove() # actually, remove only uses self.switch and self.id, so this removes the other entry as well.\n self.deploy()","def do_command(self, args):\n hostops = dbops.Hosts()\n hostops.state(args)","def update_target_network(self):\n self.target_dqn.set_weights.remote(self.dqn.get_weights.remote())","def update(self) -> None:\n self._state = b'\\x00' == self._device.readCharacteristic(self._handle)\n print(\"state\", self._state)","def _setup_communication(self):\n state = self.ui.checkBox_comm.checkState()\n if state:\n try:\n sys.path.append(\"..\")\n from zmq_interface.gui_interface import ZmqInterface\n except ImportError as e:\n self.write_text(\"ZMQ interface failed to import. No remote control for this session.\")\n self.disable_visualizer()\n return\n try:\n ##TODO: let user specify ports\n self.com = ZmqInterface(rep_port=REPLY_PORT,\n gui_handle=self)\n except Exception as e:\n #traceback.print_exc(file=sys.stdout)\n self.write_text(\"ZMQ interface failed to start. No remote control for this session. Reason: %s\" % e)\n self.disable_visualizer()\n return\n self.start = self._start_session\n self.stop = self._stop_session\n self.load_config = self._load_state\n self.save_config = self._save_state\n self.com_timer = QtCore.QTimer()\n self.com_timer.timeout.connect(self._check_coms)\n self.com_timer.start(200)\n self.write_text(\"ZMQ interface set up. Reply port on %s\" % self.com.rep_port)\n self.enable_visualizer()\n else:\n if self.com:\n self.com.close()\n if self.com_timer:\n self.com_timer.stop()\n self.com = None\n self.com_timer = None\n self.enable_visualizer()\n self.write_text(\"ZMQ interface closed.\")","def sync_state_changed(self, state):\n return","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def set_state(self, state):\n self.env.sim.set_state(state)\n mjlib.mj_fwdPosition(self.env.sim.model, self.env.sim.data)\n mjlib.mj_fwdVelocity(self.env.sim.model, self.env.sim.data)\n mjlib.mj_fwdAcceleration(self.env.sim.model, self.env.sim.data)\n mjlib.mj_fwdActuation(self.env.sim.model, self.env.sim.data)\n mjlib.mj_fwdConstraint(self.env.sim.model, self.env.sim.data)\n self._sync_dynmodel()","def sync_remote(self, other):\n pass # TODO","def remote(self):\n logging.info(__name__ + ' : Set control to remote & locked')\n self.set_remote_status(1)","def test_prefer_chassis_as_gw(self):\n expected_key = 'external-ids:ovn-cms-options'\n expected_value = 'enable-chassis-as-gw'\n with self.config_change(\n {}, {'prefer-chassis-as-gw': True},\n reset_to_charm_default=True):\n for unit in zaza.model.get_units(self.application_name):\n self.assertEqual(\n zaza.model.run_on_unit(\n unit.entity_id,\n 'ovs-vsctl get open-vswitch . {}'.format(expected_key)\n )['Stdout'].rstrip(),\n expected_value)\n logging.info(\n '{}: \"{}\" set to \"{}\"'\n .format(unit.entity_id, expected_key, expected_value))\n logging.info('Config restored, checking things went back to normal')\n for unit in zaza.model.get_units(self.application_name):\n self.assertEqual(\n zaza.model.run_on_unit(\n unit.entity_id,\n 'ovs-vsctl get open-vswitch . '\n 'external-ids:ovn-cms-options')['Code'],\n '1')\n logging.info(\n '{}: \"{}\" no longer present'\n .format(unit.entity_id, expected_key))","def update(self):\n self._state = status\n attributes['host'] = host\n attributes['port'] = port\n self.custom_attributes = attributes","def update_target_network(self):\r\n self.send(self.server_conn, (sys._getframe().f_code.co_name, {}))","def testPutNetworkLocalIp(self):\n models.System.objects.all().delete()\n self._saveSystem()\n old_count = models.Network.objects.count()\n self._put('inventory/networks/1/',\n data=testsxml.network_put_xml_opt_ip_addr % \"169.254.4.4\",\n username=\"admin\", password=\"password\")\n self.assertEquals(old_count, models.Network.objects.count())\n\n self._put('inventory/networks/1/',\n data=testsxml.network_put_xml_opt_ip_addr % \"4.4.4.4\",\n username=\"admin\", password=\"password\")\n self.assertEquals(old_count + 1, models.Network.objects.count())","def synchronize(self, new_state_dict: Dict[str, np.ndarray]):\n self._synchronize(self.dqn, new_state_dict)","def change_state(self):\n new_state = 0 if self.state.state == 1 else 1\n answer = UsbHost.send_query(self.state.ser, \"SetState\", str(self.state.device_id), new_state)\n if answer in wrong_answers:\n error_message(\"Не удалось сменить состояние\")\n self.statusbar.showMessage(answer_translate[answer])\n else:\n self.statusbar.clearMessage()\n self.state.state = new_state\n if new_state == 1:\n self.set_auto_active()\n if new_state == 0:\n self.set_hand_active()","def update_target_network(self) -> NoReturn:\n self.target.load_state_dict(self.model.state_dict())","def connect_to_master():","def update(self) -> None:\n self._gateway.update()","def sync_target_network(self):\n for t, e in zip(\n self.target_network.trainable_variables, self.online_network.trainable_variables\n ):\n t.assign(e)"],"string":"[\n \"def _sync_experiment_state_with_ddb(self):\\n if self.local_mode:\\n self.sync_thread.sync_experiment_state_with_ddb()\",\n \"def update(self):\\n self._state = get_local_ip()\",\n \"def sync():\\n sync_ssda()\",\n \"def update(self):\\n try:\\n if not self._sysinfo:\\n self._sysinfo = self.smartplug.sys_info\\n self._mac = self.smartplug.mac\\n self._model = self.smartplug.model\\n if self.smartplug.context is None:\\n self._alias = self.smartplug.alias\\n self._device_id = self._mac\\n else:\\n self._alias = self._plug_from_context[\\\"alias\\\"]\\n self._device_id = self.smartplug.context\\n\\n if self.smartplug.context is None:\\n self._state = self.smartplug.state == self.smartplug.SWITCH_STATE_ON\\n else:\\n self._state = self._plug_from_context[\\\"state\\\"] == 1\\n\\n if self.smartplug.has_emeter:\\n emeter_readings = self.smartplug.get_emeter_realtime()\\n\\n self._emeter_params[ATTR_CURRENT_POWER_W] = \\\"{:.2f}\\\".format(\\n emeter_readings[\\\"power\\\"]\\n )\\n self._emeter_params[ATTR_TOTAL_ENERGY_KWH] = \\\"{:.3f}\\\".format(\\n emeter_readings[\\\"total\\\"]\\n )\\n self._emeter_params[ATTR_VOLTAGE] = \\\"{:.1f}\\\".format(\\n emeter_readings[\\\"voltage\\\"]\\n )\\n self._emeter_params[ATTR_CURRENT_A] = \\\"{:.2f}\\\".format(\\n emeter_readings[\\\"current\\\"]\\n )\\n\\n emeter_statics = self.smartplug.get_emeter_daily()\\n try:\\n self._emeter_params[ATTR_TODAY_ENERGY_KWH] = \\\"{:.3f}\\\".format(\\n emeter_statics[int(time.strftime(\\\"%e\\\"))]\\n )\\n except KeyError:\\n # Device returned no daily history\\n pass\\n\\n self._available = True\\n\\n except (SmartDeviceException, OSError) as ex:\\n if self._available:\\n _LOGGER.warning(\\n \\\"Could not read state for %s: %s\\\", self.smartplug.host, ex\\n )\\n self._available = False\",\n \"def __init__(self):\\n self.dhcp_client_state = store.MacToIP() # mac => DHCP_State\",\n \"def sync(self) -> None: #\\n self.__target.load_state_dict(self.__policy.state_dict())\",\n \"def update(self):\\n try:\\n if self._remote.power() == 1:\\n self._state = STATE_ON\\n else:\\n self._state = STATE_OFF\\n\\n # Set TV to be able to remotely power on\\n # self._remote.power_on_command_settings(2)\\n if self._remote.mute() == 2:\\n self._muted = False\\n else:\\n self._muted = True\\n self._volume = self._remote.volume() / 60\\n except OSError:\\n self._state = STATE_OFF\",\n \"def set_state(self, is_on: bool) -> None:\\n json_data = self.perform_command(self.commands[\\\"on\\\"] if is_on else self.commands[\\\"off\\\"])\\n\\n if json_data[\\\"system\\\"][\\\"set_relay_state\\\"][\\\"err_code\\\"] != 0:\\n raise Exception(\\\"Error: Error from the smartplug: \\\" + json.dumps(json_data))\",\n \"def update(self):\\r\\n self._state = self._dev.state\",\n \"def prepare_device(self):\\n self._get_pairing_status()\\n self._update_system_clock()\\n self._update_system()\\n # Above will block during update process and kill this instance if\\n # new software is installed\\n\\n if self.backend_down:\\n self._notify_backend_down()\\n else:\\n self._display_skill_loading_notification()\\n self.bus.emit(Message('mycroft.internet.connected'))\\n self._ensure_device_is_paired()\\n self._update_device_attributes_on_backend()\",\n \"def update(self):\\n try:\\n response = requests.get('{}/digital/{}'.format(\\n self._resource, self._pin), timeout=10)\\n self.data = {'state': response.json()['return_value']}\\n except requests.exceptions.ConnectionError:\\n _LOGGER.error(\\\"No route to device '%s'. Is device offline?\\\",\\n self._resource)\",\n \"def updateState(self):\\n self.state = self.microgridPolicy.computeState();\",\n \"def magma_device_sync():\\n\\n _libmagma.magma_device_sync()\",\n \"async def update(self):\\n resp = await self._request('get', 'state')\\n if resp:\\n for line in resp.splitlines():\\n key, val = line.strip().split(None, 1)\\n if val == 'on' or val == 'off':\\n val = (val == 'on')\\n self.state_data[key] = val\\n else:\\n self.state_data[key] = val\",\n \"def update_dht(self, d_ip, d_port):\\n self.dht_ip = d_ip\\n self.dht_port = d_port\",\n \"def sync() -> None:\",\n \"def update_target_q_network(self):\\n assert self.target_network != None\\n self.target_network.run_copy()\",\n \"def dvs_update_network(self):\\n self.show_step(1)\\n self.env.revert_snapshot(\\\"dvs_vcenter_systest_setup\\\")\\n\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n\\n self.show_step(2)\\n os_ip = self.fuel_web.get_public_vip(cluster_id)\\n os_conn = os_actions.OpenStackActions(\\n os_ip, SERVTEST_USERNAME,\\n SERVTEST_PASSWORD,\\n SERVTEST_TENANT)\\n\\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\\n\\n net_1 = os_conn.create_network(\\n network_name=self.net_data[0].keys()[0],\\n tenant_id=tenant.id)['network']\\n\\n os_conn.create_subnet(\\n subnet_name=net_1['name'],\\n network_id=net_1['id'],\\n cidr=self.net_data[0][self.net_data[0].keys()[0]],\\n ip_version=4)\\n\\n assert_true(os_conn.get_network(net_1['name'])['id'] == net_1['id'])\\n\\n self.show_step(3)\\n os_conn.neutron.update_network(net_1[\\\"id\\\"],\\n {\\\"network\\\": {\\\"name\\\": 'net_2'}})\\n\\n assert_true(os_conn.get_network('net_2')['id'] == net_1['id'])\\n\\n self.show_step(4)\\n default_net = os_conn.nova.networks.find(label=self.inter_net_name)\\n os_conn.neutron.update_network(\\n default_net.id, {\\\"network\\\": {\\\"name\\\": 'spring'}})\\n\\n assert_true(os_conn.get_network('spring')['id'] == default_net.id)\",\n \"def local(self):\\n logging.info(__name__ + ' : Set control to local & locked')\\n self.set_remote_status(0)\",\n \"def set_remote(self):\\n self.write_to_serial(':SYST:REM')\",\n \"def state(config: dict):\\n\\n async def state_callback(device):\\n if device.basic_info is not None:\\n if device.available:\\n print_device_details(device)\\n\\n device.shutdown_event_loop()\\n\\n logger.info(\\\"Initialising SonoffSwitch with host %s\\\" % config[\\\"host\\\"])\\n SonoffSwitch(\\n host=config[\\\"host\\\"],\\n callback_after_update=state_callback,\\n logger=logger,\\n device_id=config[\\\"device_id\\\"],\\n api_key=config[\\\"api_key\\\"],\\n )\",\n \"def test_update_pci_switch(self):\\n pass\",\n \"def updateMetaAtom (self):\\r\\n # print (\\\"Old state DNS: \\\\n\\\")\\r\\n # self.stateDanglingNodes()\\r\\n synchList = []\\r\\n synchListState = []\\r\\n for i in range(len(self.mol)):\\r\\n for j in range(len(self.mol[i].nodeArray)):\\r\\n synchList.append(self.mol[i].nodeArray[j])\\r\\n synchListState.append(synchList[i].state)\\r\\n #print (\\\"The original state is: \\\\n\\\" + str(synchListState) + \\\"\\\\n\\\")\\r\\n # Find new state for every node\\r\\n newStates = []\\r\\n for i in range(len(synchList)):\\r\\n oldState = synchList[i].state\\r\\n synchList[i].calculateState()\\r\\n newStates.append(synchList[i].state)\\r\\n synchList[i].state = oldState\\r\\n \\r\\n for i in range(len(synchList)):\\r\\n synchList[i].state = newStates[i]\\r\\n synchListState[i] = synchList[i].state\\r\\n \\r\\n offSet = 0 \\r\\n for i in range(len(self.mol)):\\r\\n for j in range(len(self.mol[i].nodeArray)):\\r\\n self.mol[i].nodeArray[j].state = synchListState[offSet]\\r\\n offSet += 1\\r\\n stateMol = []\\r\\n \\r\\n for i in range(len(self.mol)):\\r\\n for j in range(len(self.mol[i].nodeArray)):\\r\\n stateMol.append(self.mol[i].nodeArray[j].state)\\r\\n \\r\\n # print (\\\"The new state is: \\\\n\\\" + str(synchListState) + \\\"\\\\n\\\")\\r\\n # print (\\\"The state of the mol array is: \\\" + str(stateMol) + \\\"\\\\n\\\")\\r\\n #print (\\\"Post update \\\\n\\\")\\r\\n self.stateDanglingNodes()\\r\\n offSet = 0 \\r\\n oldStateNodes = [] # Store the old state of nodes in molecule\\r\\n newStateNodes = [] # Stores the new state\\r\\n # The code below goes through each metaspike and ensures that the dangling nodes have been updated with the correct\\r\\n # new state\\r\\n for i in range(len(self.metaSpikes)):\\r\\n if self.metaSpikes[i].typeSpike == 1:\\r\\n #print (\\\"Inside type 1 \\\\n\\\")\\r\\n #print (\\\"The number of DNs is: \\\" + str(len(self.metaSpikes[i].danglingNodeList)) + \\\"\\\\n\\\")\\r\\n for j in range(len(self.metaSpikes[i].danglingNodeList)):\\r\\n # Find the location of the dangling node in the synch list and change the dangling nodes state to match\\r\\n # state locted in the synch list\\r\\n if self.metaSpikes[i].danglingNodeList[j].node in synchList:\\r\\n oldStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\\r\\n indexNode = synchList.index(self.metaSpikes[i].danglingNodeList[j].node)\\r\\n # print (\\\"The current value is: \\\" + str(self.metaSpikes[i].danglingNodeList[j].node.state) + \\\"\\\\n\\\")\\r\\n # print (\\\"The index of node is: \\\" + str(indexNode) + \\\"\\\\n\\\")\\r\\n # print (\\\"The new value should be: \\\" + str(synchListState[indexNode]) + \\\"\\\\n\\\")\\r\\n self.metaSpikes[i].danglingNodeList[j].changeState(synchListState[indexNode]) \\r\\n newStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\\r\\n # print (\\\"Node in list \\\\n\\\")\\r\\n else:\\r\\n # print (\\\"The number of DTs is: \\\" + str(len(self.metaSpikes[i].danglingTailList)) + \\\"\\\\n\\\")\\r\\n #print (\\\"Inside type 2 \\\\n\\\")\\r\\n # With dangling tails we need an extra for loop to iterate across each nodelist of the tail\\r\\n for j in range(len(self.metaSpikes[i].danglingTailList)):\\r\\n for k in range(len(self.metaSpikes[i].danglingTailList[j].nodeList)):\\r\\n if self.metaSpikes[i].danglingTailList[j].nodeList[k].node in synchList:\\r\\n oldStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\\r\\n indexNode = synchList.index(self.metaSpikes[i].danglingTailList[j].nodeList[k].node)\\r\\n self.metaSpikes[i].danglingTailList[j].nodeList[k].changeState(synchListState[indexNode]) \\r\\n newStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\\r\\n #print (\\\"Node in list \\\\n\\\")\\r\\n \\r\\n # print (\\\"After running update code \\\\n\\\")\\r\\n self.stateDanglingNodes()\\r\\n # Recalculate the state of the metaatom\\r\\n self.calculateState()\\r\\n #print (\\\"The old state is:\\\\n\\\" + str(oldStateNodes) + \\\"\\\\n\\\")\\r\\n #print (\\\"The new state is:\\\\n\\\" + str(newStateNodes) + \\\"\\\\n\\\")\\r\\n \\r\\n \\r\\n # Next need to give each node in mol its state \\r\\n \\r\\n #print (\\\"Intensity before update: \\\" + str(self.metaSpikes[i].intensity) + \\\"\\\\n\\\")\\r\\n #print (\\\"Intensity after update: \\\" + str(self.metaSpikes[i].intensity) + \\\"\\\\n\\\")\\r\\n # Now need to recalculate state\\r\",\n \"def update_network(self, dbnetwork, qipinfo):\\n\\n # We don't want to add the plenary to self.plenaries if we aren't going\\n # to change anything\\n plenary = Plenary.get_plenary(dbnetwork)\\n updated = False\\n\\n if dbnetwork.name != qipinfo.name:\\n self.logger.client_info(\\\"Setting network {0!s} name to {1}\\\"\\n .format(dbnetwork, qipinfo.name))\\n dbnetwork.name = qipinfo.name\\n if dbnetwork.network_type != qipinfo.network_type:\\n self.logger.client_info(\\\"Setting network {0!s} type to {1}\\\"\\n .format(dbnetwork, qipinfo.network_type))\\n dbnetwork.network_type = qipinfo.network_type\\n if dbnetwork.location != qipinfo.location:\\n self.logger.client_info(\\\"Setting network {0!s} location to {1:l}\\\"\\n .format(dbnetwork, qipinfo.location))\\n dbnetwork.location = qipinfo.location\\n if dbnetwork.side != qipinfo.side:\\n self.logger.client_info(\\\"Setting network {0!s} side to {1}\\\"\\n .format(dbnetwork, qipinfo.side))\\n dbnetwork.side = qipinfo.side\\n if dbnetwork.network_compartment != qipinfo.compartment:\\n self.logger.client_info(\\\"Setting network {0!s} compartment to {1!s}\\\"\\n .format(dbnetwork, qipinfo.compartment))\\n dbnetwork.network_compartment = qipinfo.compartment\\n\\n if dbnetwork in self.session.dirty:\\n updated = True\\n\\n old_rtrs = set(dbnetwork.router_ips)\\n new_rtrs = set(qipinfo.routers)\\n\\n del_routers = []\\n for router in dbnetwork.routers:\\n if router.ip in old_rtrs - new_rtrs:\\n del_routers.append(router)\\n\\n for router in del_routers:\\n self.logger.client_info(\\\"Removing router {0:s} from \\\"\\n \\\"{1:l}\\\".format(router.ip, dbnetwork))\\n for dns_rec in router.dns_records:\\n if dns_rec.is_unused:\\n delete_dns_record(dns_rec)\\n dbnetwork.routers.remove(router)\\n updated = True\\n\\n for ip in new_rtrs - old_rtrs:\\n self.add_router(dbnetwork, ip)\\n updated = True\\n\\n if updated:\\n self.plenaries.append(plenary)\\n\\n # TODO: add support for updating router locations\\n\\n return dbnetwork.netmask == qipinfo.address.netmask\",\n \"def _update(self, host):\\n pass\",\n \"def update_host_ovs(self, context):\\n LOG.info(_('Updating Open vSwitch host data...'))\\n LOG.debug(\\\"Current DOM: %s\\\" % self.current_dom.to_dict())\\n LOG.debug(\\\"Requested DOM: %s\\\" % self.desired_dom.to_dict())\\n\\n builder = mob.MicroOperationBuilder(context,\\n self.current_dom,\\n self.desired_dom,\\n self.rollback)\\n\\n mo_list = builder.get_micro_ops_for_update()\\n\\n # run validation\\n return self._run_micro_op_list(mo_list)\",\n \"def sync(self):\\n pass\",\n \"def sync(self):\\n pass\",\n \"def doSync (self) :\\r\\n \\r\\n self.factory.getSyncFor(self)\",\n \"def _sync(self, message=None):\\n if message[0] == self._multi_level_sensor_property.element_uid:\\n self._state = self._device_instance.multi_level_sensor_property[\\n message[0]\\n ].value\\n elif message[0].startswith(\\\"hdm\\\"):\\n self._available = self._device_instance.is_online()\\n else:\\n _LOGGER.debug(\\\"No valid message received: %s\\\", message)\\n self.schedule_update_ha_state()\",\n \"def execute(self, cmd, state):\\n state[:] = interface.execute_arm_command(cmd, 0)\\n self.env.update() # note that the sim update is called twice, once here and once by the hand's sim_connection\",\n \"def set_into_managed_mode(wifi_name):\\n \\n subprocess.run([\\\"ip\\\", \\\"link\\\", \\\"set\\\", wifi_name, \\\"down\\\"])\\n subprocess.run([\\\"iwconfig\\\", wifi_name, \\\"mode\\\", \\\"managed\\\"])\\n subprocess.run([\\\"ip\\\", \\\"link\\\", \\\"set\\\", wifi_name, \\\"up\\\"])\\n subprocess.run([\\\"service\\\", \\\"NetworkManager\\\", \\\"start\\\"])\",\n \"def _sync_power_states(self, context):\\n\\n # Only fetching the necessary fields, will skip synchronizing if\\n # target_power_state is not None.\\n\\n try:\\n nodes = self.driver.get_nodes_power_state()\\n except Exception as e:\\n LOG.warning(\\n (\\\"Failed to retrieve node list when synchronizing power \\\"\\n \\\"states: %(msg)s\\\") % {\\\"msg\\\": e})\\n # Just return if we fail to get nodes real power state.\\n return\\n\\n node_dict = {node.instance_uuid: node for node in nodes\\n if node.target_power_state is None}\\n\\n if not node_dict:\\n LOG.warning(\\\"While synchronizing server power states, \\\"\\n \\\"found none server with stable power state \\\"\\n \\\"on the hypervisor.\\\")\\n return\\n\\n def _sync(db_server, node_power_state):\\n # This must be synchronized as we query state from two separate\\n # sources, the driver (ironic) and the database. They are set\\n # (in stop_server) and read, in sync.\\n @utils.synchronized(db_server.uuid)\\n def sync_server_power_state():\\n self._sync_server_power_state(context, db_server,\\n node_power_state)\\n\\n try:\\n sync_server_power_state()\\n except Exception:\\n LOG.exception(\\\"Periodic sync_power_state task had an \\\"\\n \\\"error while processing a server.\\\",\\n server=db_server)\\n\\n self._syncs_in_progress.pop(db_server.uuid)\\n\\n db_servers = objects.Server.list(context)\\n for db_server in db_servers:\\n # process syncs asynchronously - don't want server locking to\\n # block entire periodic task thread\\n uuid = db_server.uuid\\n if uuid in self._syncs_in_progress:\\n LOG.debug('Sync power state already in progress for %s', uuid)\\n continue\\n\\n if db_server.status not in (states.ACTIVE, states.STOPPED):\\n if db_server.status in states.UNSTABLE_STATES:\\n LOG.info(\\\"During sync_power_state the server has a \\\"\\n \\\"pending task (%(task)s). Skip.\\\",\\n {'task': db_server.status},\\n server=db_server)\\n continue\\n\\n if uuid not in node_dict:\\n continue\\n\\n node_power_state = node_dict[uuid].power_state\\n if db_server.power_state != node_power_state:\\n LOG.debug('Triggering sync for uuid %s', uuid)\\n self._syncs_in_progress[uuid] = True\\n self._sync_power_pool.spawn_n(_sync, db_server,\\n node_power_state)\",\n \"def updateStateImage(self):\\n\\n if self.device.states['online']:\\n self.device.updateStateImageOnServer(indigo.kStateImageSel.EnergyMeterOn)\\n else:\\n self.device.updateStateImageOnServer(indigo.kStateImageSel.EnergyMeterOff)\",\n \"def sync_with_database(self):\\n # learn from db\\n lports = self.nb_api.get_all(l2.LogicalPort)\\n for lport in lports:\\n port_id = \\\"{}:{}\\\".format(lport.lswitch.id, lport.id)\\n self.cache_logical_port_by_port_id[port_id] = lport\\n lrouters = self.nb_api.get_all(l3.LogicalRouter)\\n for lrouter in lrouters:\\n self.cache_logical_router_by_dpid[lrouter.id] = lrouter\",\n \"def send_state(self):\\n self.state = self.enigma.get_state()\\n messages = self.notify_slaves()\\n for message in messages:\\n self.network.messages_to_slaves.append(message)\",\n \"def update(self):\\n self._client.update()\\n\\n self._current_operation_mode = self._device.get_run_mode()\\n self._target_temperature = self._device.target_temperature\\n self._current_temperature = self._device.current_temperature\\n self._min_temp = self._device.min_temp\\n self._max_temp = self._device.max_temp\\n\\n self._update_attributes_from_device()\\n\\n # set whether device is in away mode\\n if (\\n self._current_operation_mode == CONST_MODE_AWAY\\n or self._current_operation_mode == CONST_MODE_FROST\\n ):\\n self._away = True\\n else:\\n self._away = False\\n\\n # set whether device is on/off\\n if self._current_operation_mode == CONST_MODE_OFF:\\n self._on = False\\n else:\\n self._on = True\",\n \"def sync(self):\\r\\n\\r\\n # Ensure to rerun only once to avoid infinite loops\\r\\n # caused by a constantly changing state value at each run.\\r\\n #\\r\\n # Example: state.value += 1\\r\\n if self._state[\\\"is_rerun\\\"]:\\r\\n self._state[\\\"is_rerun\\\"] = False\\r\\n \\r\\n elif self._state[\\\"hash\\\"] is not None:\\r\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\r\\n self._state[\\\"is_rerun\\\"] = True\\r\\n self._state[\\\"session\\\"].request_rerun()\\r\\n\\r\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"def set_state(self, state: bool) -> None:\\n payload = self._cfg.state_power_on if state else self._cfg.state_power_off\\n command = f\\\"{COMMAND_POWER}{self._cfg.idx+1}\\\"\\n self._mqtt_client.publish(\\n self._cfg.command_topic + command,\\n payload,\\n )\",\n \"async def async_turn_on(self, **kwargs):\\n self._wrap_device.device.set_duct_zone(self._zone, True)\",\n \"def storage_reset(self):\\n daos_srv_bin = os.path.join(self.daosbinpath, \\\"daos_server\\\")\\n cmd = \\\"{} storage prepare -n --reset -f\\\".format(daos_srv_bin)\\n result = pcmd(self._hosts, cmd)\\n if len(result) > 1 or 0 not in result:\\n raise ServerFailed(\\\"Error resetting NVMe storage\\\")\",\n \"def admin_state_up(self, state):\\n self.neutron.update_port(self._provision_port_id, {'port': {'admin_state_up': state}})\",\n \"def sync(self):\\r\\n\\r\\n # Ensure to rerun only once to avoid infinite loops\\r\\n # caused by a constantly changing state value at each run.\\r\\n #\\r\\n # Example: state.value += 1\\r\\n if self._state[\\\"is_rerun\\\"]:\\r\\n self._state[\\\"is_rerun\\\"] = False\\r\\n\\r\\n elif self._state[\\\"hash\\\"] is not None:\\r\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\r\\n self._state[\\\"is_rerun\\\"] = True\\r\\n self._state[\\\"session\\\"].request_rerun()\\r\\n\\r\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"async def async_turn_on(self, **kwargs):\\n try:\\n if await self._api.set_relay_state(self._dev_id, \\\"on\\\"):\\n self._is_on = True\\n self.async_write_ha_state()\\n except Smile.PlugwiseError:\\n _LOGGER.error(\\\"Error while communicating to device\\\")\",\n \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None\\n ):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops caused by a constantly changing state value at each run.\\n # Example: state.value += 1\\n\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None\\n ):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"async def async_update_data():\\n for device in juicenet.devices:\\n await device.update_state(True)\\n return True\",\n \"def resetDeviceStates(self):\",\n \"def init(self):\\n try:\\n yield self._connect_dbus()\\n logger.info(\\\"Request the GSM resource\\\")\\n yield WaitFSOResource('GSM', time_out=30)\\n yield WaitDBus(self.ousage.RequestResource, 'GSM')\\n yield self._turn_on()\\n logger.info(\\\"register on the network\\\")\\n register = yield self._register()\\n #if register:\\n #provider = yield tichy.Wait(self, 'provider-modified')\\n \\n self._keep_alive().start()\\n \\n ##network selection end\\n \\n except Exception, ex:\\n logger.error(\\\"Error : %s\\\", ex)\\n raise\\n \\n try:\\n \\n yield tichy.Service.get('ConfigService').wait_initialized()\\n self.config_service = tichy.Service.get(\\\"ConfigService\\\")\\n logger.info(\\\"got config service\\\")\\n \\n except Exception, ex:\\n logger.error(\\\"Error in try retrieving config service : %s\\\", ex)\\n \\n try:\\n \\n ##call forwaring setting start\\n self.values = self.config_service.get_items(\\\"call_forwarding\\\")\\n if self.values != None: self.values = dict(self.values)\\n logger.info(\\\"realized values is none\\\")\\n\\n except Exception, ex:\\n logger.error(\\\"Error in try call forwarding setting : %s\\\", ex)\\n \\n \\n try:\\n\\n self.SettingReason = tichy.settings.ListSetting('Call Forwarding','Reason',tichy.Text,value='unconditional', setter=self.ForwardingSetReason,options=[\\\"unconditional\\\",\\\"mobile busy\\\",\\\"no reply\\\",\\\"not reachable\\\",\\\"all\\\",\\\"allconditional\\\"],model=tichy.List([ ListSettingObject(\\\"unconditional\\\", self.action),ListSettingObject(\\\"mobile busy\\\",self.action),ListSettingObject(\\\"no reply\\\", self.action),ListSettingObject(\\\"not reachable\\\", self.action),ListSettingObject(\\\"all\\\", self.action),ListSettingObject(\\\"all conditional\\\", self.action)]), ListLabel =[('title','name')])\\n \\n self.SettingForwarding = tichy.settings.ToggleSetting('Call Forwarding', 'active', tichy.Text, value=self.GetForwardingStatus('unconditional'),setter=self.ToggleForwarding, options=['active','inactive'])\\n \\n \\n except Exception, ex:\\n logger.error(\\\"Error in try call forwarding setting list : %s\\\", ex)\\n \\n \\n try:\\n\\n self.SettingChannels = tichy.settings.Setting('Call Forwarding', 'channels', tichy.Text, value=self.ForwardingGet('class'), setter=self.ForwardingSetClass, options=[\\\"voice\\\",\\\"data\\\",\\\"voice+data\\\",\\\"fax\\\",\\\"voice+data+fax\\\"])\\n \\n self.SettingTargetNumber = tichy.settings.NumberSetting('Call Forwarding', 'Target Number', tichy.Text, value=self.ForwardingGet('number'), setter=self.ForwardingSetNumber)\\n \\n self.SettingTargetNumber = tichy.settings.NumberSetting('Call Forwarding', 'Timeout', tichy.Text, value=self.ForwardingGet('timeout'), setter=self.ForwardingSetTimeout)\\n \\n ##call forwaring setting stop\\n \\n \\n except Exception, ex:\\n logger.error(\\\"Error in try Error in try call forwarding setting : %s\\\", ex)\\n \\n try:\\n\\n ##call identifaction setting start\\n self.CallIdentification = tichy.settings.Setting('Network', 'Call Identification', tichy.Text, value=self.GetCallIdentification(), setter=self.SetCallIdentifaction, options=[\\\"on\\\",\\\"off\\\",\\\"network\\\"])\\n ##call identifaction setting stop\\n \\n except Exception, ex:\\n logger.error(\\\"Error in network identification setting: %s\\\", ex)\\n \\n try: \\n ##network selection etc begin\\n self.NetworkRegistration = tichy.settings.Setting('Network', 'Registration', tichy.Text, value=self.GetRegStatus(), setter=self.SetRegStatus, options=[\\\"registered\\\",\\\"not registered\\\"])\\n \\n \\n except Exception, ex:\\n logger.error(\\\"Error in network registration setting : %s\\\", ex)\\n \\n \\n try:\\n \\n self.scanning = False\\n self.NetworkList = tichy.List()\\n self.ListLabel = [('title','name'),('subtitle','status')]\\n \\n self.scan_setting = tichy.settings.ListSetting('Network', 'List', tichy.Text, value=\\\"scan\\\", setter=self.run_scan, options=['scan'], model=self.NetworkList, ListLabel=self.ListLabel)\\n \\n except Exception, ex:\\n logger.error(\\\"Error in network list setting : %s\\\", ex)\\n #raise\",\n \"def light_sync(self):\",\n \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None)\",\n \"def update_target_network(self):\\n\\n\\t\\tprint \\\"Updating Target DQN...\\\"\\n\\t\\t\\n\\t\\tself.update_operation.run()\",\n \"def _sync_instance_power_state(self, context, db_instance, vm_power_state,\\n use_slave=False):\\n\\n # We re-query the DB to get the latest instance info to minimize\\n # (not eliminate) race condition.\\n db_instance.refresh(use_slave=use_slave)\\n db_power_state = db_instance.power_state\\n vm_state = db_instance.vm_state\\n\\n if self.host != db_instance.host:\\n # on the sending end of cloud-cloud _sync_power_state\\n # may have yielded to the greenthread performing a live\\n # migration; this in turn has changed the resident-host\\n # for the VM; However, the instance is still active, it\\n # is just in the process of migrating to another host.\\n # This implies that the cloud source must relinquish\\n # control to the cloud destination.\\n LOG.info(_LI(\\\"During the sync_power process the \\\"\\n \\\"instance has moved from \\\"\\n \\\"host %(src)s to host %(dst)s\\\"),\\n {'src': db_instance.host,\\n 'dst': self.host},\\n instance=db_instance)\\n return\\n elif db_instance.task_state is not None:\\n # on the receiving end of cloud-cloud, it could happen\\n # that the DB instance already report the new resident\\n # but the actual VM has not showed up on the hypervisor\\n # yet. In this case, let's allow the loop to continue\\n # and run the state sync in a later round\\n LOG.info(_LI(\\\"During sync_power_state the instance has a \\\"\\n \\\"pending task (%(task)s). Skip.\\\"),\\n {'task': db_instance.task_state},\\n instance=db_instance)\\n return\\n\\n orig_db_power_state = db_power_state\\n if vm_power_state != db_power_state:\\n LOG.info(_LI('During _sync_instance_power_state the DB '\\n 'power_state (%(db_power_state)s) does not match '\\n 'the vm_power_state from the hypervisor '\\n '(%(vm_power_state)s). Updating power_state in the '\\n 'DB to match the hypervisor.'),\\n {'db_power_state': db_power_state,\\n 'vm_power_state': vm_power_state},\\n instance=db_instance)\\n # power_state is always updated from hypervisor to db\\n db_instance.power_state = vm_power_state\\n db_instance.save()\\n db_power_state = vm_power_state\\n\\n # Note(maoy): Now resolve the discrepancy between vm_state and\\n # vm_power_state. We go through all possible vm_states.\\n if vm_state in (vm_states.BUILDING,\\n vm_states.RESCUED,\\n vm_states.RESIZED,\\n vm_states.SUSPENDED,\\n vm_states.ERROR):\\n # TODO(maoy): we ignore these vm_state for now.\\n pass\\n elif vm_state == vm_states.ACTIVE:\\n # The only rational power state should be RUNNING\\n if vm_power_state in (power_state.SHUTDOWN,\\n power_state.CRASHED):\\n LOG.warning(_LW(\\\"Instance shutdown by itself. Calling the \\\"\\n \\\"stop API. Current vm_state: %(vm_state)s, \\\"\\n \\\"current task_state: %(task_state)s, \\\"\\n \\\"original DB power_state: %(db_power_state)s, \\\"\\n \\\"current VM power_state: %(vm_power_state)s\\\"),\\n {'vm_state': vm_state,\\n 'task_state': db_instance.task_state,\\n 'db_power_state': orig_db_power_state,\\n 'vm_power_state': vm_power_state},\\n instance=db_instance)\\n try:\\n # Note(maoy): here we call the API instead of\\n # brutally updating the vm_state in the database\\n # to allow all the hooks and checks to be performed.\\n if db_instance.shutdown_terminate:\\n self.compute_api.delete(context, db_instance)\\n else:\\n self.compute_api.stop(context, db_instance)\\n except Exception:\\n # Note(maoy): there is no need to propagate the error\\n # because the same power_state will be retrieved next\\n # time and retried.\\n # For example, there might be another task scheduled.\\n LOG.exception(_LE(\\\"error during stop() in \\\"\\n \\\"sync_power_state.\\\"),\\n instance=db_instance)\\n elif vm_power_state == power_state.SUSPENDED:\\n LOG.warning(_LW(\\\"Instance is suspended unexpectedly. Calling \\\"\\n \\\"the stop API.\\\"), instance=db_instance)\\n try:\\n self.compute_api.stop(context, db_instance)\\n except Exception:\\n LOG.exception(_LE(\\\"error during stop() in \\\"\\n \\\"sync_power_state.\\\"),\\n instance=db_instance)\\n elif vm_power_state == power_state.PAUSED:\\n # Note(maoy): a VM may get into the paused state not only\\n # because the user request via API calls, but also\\n # due to (temporary) external instrumentations.\\n # Before the virt layer can reliably report the reason,\\n # we simply ignore the state discrepancy. In many cases,\\n # the VM state will go back to running after the external\\n # instrumentation is done. See bug 1097806 for details.\\n LOG.warning(_LW(\\\"Instance is paused unexpectedly. Ignore.\\\"),\\n instance=db_instance)\\n elif vm_power_state == power_state.NOSTATE:\\n # Occasionally, depending on the status of the hypervisor,\\n # which could be restarting for example, an instance may\\n # not be found. Therefore just log the condition.\\n LOG.warning(_LW(\\\"Instance is unexpectedly not found. Ignore.\\\"),\\n instance=db_instance)\\n elif vm_state == vm_states.STOPPED:\\n if vm_power_state not in (power_state.NOSTATE,\\n power_state.SHUTDOWN,\\n power_state.CRASHED):\\n LOG.warning(_LW(\\\"Instance is not stopped. Calling \\\"\\n \\\"the stop API. Current vm_state: %(vm_state)s,\\\"\\n \\\" current task_state: %(task_state)s, \\\"\\n \\\"original DB power_state: %(db_power_state)s, \\\"\\n \\\"current VM power_state: %(vm_power_state)s\\\"),\\n {'vm_state': vm_state,\\n 'task_state': db_instance.task_state,\\n 'db_power_state': orig_db_power_state,\\n 'vm_power_state': vm_power_state},\\n instance=db_instance)\\n try:\\n # NOTE(russellb) Force the stop, because normally the\\n # cloud API would not allow an attempt to stop a stopped\\n # instance.\\n self.compute_api.force_stop(context, db_instance)\\n except Exception:\\n LOG.exception(_LE(\\\"error during stop() in \\\"\\n \\\"sync_power_state.\\\"),\\n instance=db_instance)\\n elif vm_state == vm_states.PAUSED:\\n if vm_power_state in (power_state.SHUTDOWN,\\n power_state.CRASHED):\\n LOG.warning(_LW(\\\"Paused instance shutdown by itself. Calling \\\"\\n \\\"the stop API.\\\"), instance=db_instance)\\n try:\\n self.compute_api.force_stop(context, db_instance)\\n except Exception:\\n LOG.exception(_LE(\\\"error during stop() in \\\"\\n \\\"sync_power_state.\\\"),\\n instance=db_instance)\\n elif vm_state in (vm_states.SOFT_DELETED,\\n vm_states.DELETED):\\n if vm_power_state not in (power_state.NOSTATE,\\n power_state.SHUTDOWN):\\n # Note(maoy): this should be taken care of periodically in\\n # _cleanup_running_deleted_instances().\\n LOG.warning(_LW(\\\"Instance is not (soft-)deleted.\\\"),\\n instance=db_instance)\",\n \"def init_host(self, host=socket.gethostname()):\\n ctxt = context.get_admin_context()\\n\\n LOG.debug('Hostname: %s' % (host,))\\n LOG.debug('Instances: %s' % (db.instance_get_all_by_host(ctxt, host)))\\n \\n for instance in db.instance_get_all_by_host(ctxt, host):\\n try:\\n LOG.debug('Checking state of %s' % instance['name'])\\n state = self.get_info(instance['name'])['state']\\n except exception.NotFound:\\n state = power_state.SHUTOFF\\n\\n LOG.debug('Current state of %s was %s.' %\\n (instance['name'], state))\\n db.instance_set_state(ctxt, instance['id'], state)\\n\\n if state == power_state.SHUTOFF:\\n db.instance_destroy(ctxt, instance['id'])\\n\\n if state != power_state.RUNNING:\\n continue\",\n \"def update(self):\\n\\n self._state = get_balance(self.addresses)\",\n \"def test_set_state_full(self):\\n host = synthetic_host(\\\"myserver\\\")\\n self.create_simple_filesystem(host)\\n mgt_uri = \\\"/api/target/%s/\\\" % self.mgt.id\\n with mock.patch(\\\"chroma_core.models.Command.set_state\\\", mock.Mock(return_value=None)):\\n self.api_set_state_full(mgt_uri, \\\"unmounted\\\")\\n Command.set_state.assert_called_once()\",\n \"async def async_update(self) -> None:\\n if (\\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\\n == BSH_POWER_ON\\n ):\\n self._state = True\\n elif (\\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\\n == self.device.power_off_state\\n ):\\n self._state = False\\n elif self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(\\n ATTR_VALUE, None\\n ) in [\\n \\\"BSH.Common.EnumType.OperationState.Ready\\\",\\n \\\"BSH.Common.EnumType.OperationState.DelayedStart\\\",\\n \\\"BSH.Common.EnumType.OperationState.Run\\\",\\n \\\"BSH.Common.EnumType.OperationState.Pause\\\",\\n \\\"BSH.Common.EnumType.OperationState.ActionRequired\\\",\\n \\\"BSH.Common.EnumType.OperationState.Aborting\\\",\\n \\\"BSH.Common.EnumType.OperationState.Finished\\\",\\n ]:\\n self._state = True\\n elif (\\n self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(ATTR_VALUE)\\n == \\\"BSH.Common.EnumType.OperationState.Inactive\\\"\\n ):\\n self._state = False\\n else:\\n self._state = None\\n _LOGGER.debug(\\\"Updated, new state: %s\\\", self._state)\",\n \"def sync_local(self, other):\\n pass # TODO\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def _sync_server_power_state(self, context, db_server,\\n node_power_state):\\n\\n # We re-query the DB to get the latest server info to minimize\\n # (not eliminate) race condition.\\n db_server.refresh()\\n db_power_state = db_server.power_state\\n\\n if db_server.status not in (states.ACTIVE, states.STOPPED):\\n # on the receiving end of mogan-engine, it could happen\\n # that the DB server already report the new resident\\n # but the actual BM has not showed up on the hypervisor\\n # yet. In this case, let's allow the loop to continue\\n # and run the state sync in a later round\\n LOG.info(\\\"During sync_power_state the server has a \\\"\\n \\\"pending task (%(task)s). Skip.\\\",\\n {'task': db_server.task_state},\\n server=db_server)\\n return\\n\\n if node_power_state != db_power_state:\\n LOG.info('During _sync_server_power_state the DB '\\n 'power_state (%(db_power_state)s) does not match '\\n 'the node_power_state from the hypervisor '\\n '(%(node_power_state)s). Updating power_state in the '\\n 'DB to match the hypervisor.',\\n {'db_power_state': db_power_state,\\n 'node_power_state': node_power_state},\\n server=db_server)\\n # power_state is always updated from hypervisor to db\\n db_server.power_state = node_power_state\\n db_server.save()\",\n \"def _update_target(self):\\n self.target_dqn.load_state_dict(self.dqn.state_dict())\",\n \"def update_state(self, dstate):\\n pass\",\n \"def test_update_with_target_state(self):\\n self.switch._target_state = True\\n self.port.data = {}\\n self.port.data[\\\"output\\\"] = \\\"stale\\\"\\n self.switch.update()\\n assert 1.0 == self.port.data[\\\"output\\\"]\\n assert self.switch._target_state is None\\n self.port.data[\\\"output\\\"] = \\\"untouched\\\"\\n self.switch.update()\\n assert \\\"untouched\\\" == self.port.data[\\\"output\\\"]\",\n \"def reconfigure(self):\\n log.debug('Reconfiguring and restarting the DHCP daemon...')\\n\\n # Don't set the daemon running status here, but let the status\\n # check take care of that.\\n\\n p = Properties(self.storage, CONFIG_SECTION)\\n p.addCallback(self.changed).\\\\\\n addCallback(lambda trigger: p.load()).\\\\\\n addCallback(self.emit_config, p).\\\\\\n addCallback(self.restart_daemon).\\\\\\n addErrback(self.restart_error)\",\n \"def sync(self):\\n acctManager = self.acctManager\\n acct = acctManager.account(0)\\n gapPolicy = 5\\n acct.generateGapAddresses(gapPolicy)\\n watchAddresses = set()\\n\\n # send the initial balance\\n self.signals.balance(acct.balance)\\n addresses = acct.allAddresses()\\n \\n # Update the account with known UTXOs.\\n chain = self.blockchain\\n blockchainUTXOs = chain.UTXOs(addresses)\\n acct.resolveUTXOs(blockchainUTXOs)\\n\\n # Subscribe to block and address updates.\\n chain.subscribeBlocks(self.blockSignal)\\n watchAddresses = acct.addressesOfInterest()\\n if watchAddresses:\\n chain.subscribeAddresses(watchAddresses, self.addressSignal)\\n # Signal the new balance.\\n b = acct.calcBalance(self.blockchain.tip[\\\"height\\\"])\\n self.signals.balance(b)\\n self.save()\\n return True\",\n \"def _send_all_data(self):\\n admin_context = qcontext.get_admin_context()\\n networks = {}\\n ports = {}\\n\\n all_networks = super(QuantumRestProxyV2,\\n self).get_networks(admin_context) or []\\n for net in all_networks:\\n networks[net.get('id')] = {\\n 'id': net.get('id'),\\n 'name': net.get('name'),\\n 'op-status': net.get('admin_state_up'),\\n }\\n\\n subnets = net.get('subnets', [])\\n for subnet_id in subnets:\\n subnet = self.get_subnet(admin_context, subnet_id)\\n gateway_ip = subnet.get('gateway_ip')\\n if gateway_ip:\\n # FIX: For backward compatibility with wire protocol\\n networks[net.get('id')]['gateway'] = gateway_ip\\n\\n ports = []\\n net_filter = {'network_id': [net.get('id')]}\\n net_ports = super(QuantumRestProxyV2,\\n self).get_ports(admin_context,\\n filters=net_filter) or []\\n for port in net_ports:\\n port_details = {\\n 'id': port.get('id'),\\n 'attachment': {\\n 'id': port.get('id') + '00',\\n 'mac': port.get('mac_address'),\\n },\\n 'state': port.get('status'),\\n 'op-status': port.get('admin_state_up'),\\n 'mac': None\\n }\\n ports.append(port_details)\\n networks[net.get('id')]['ports'] = ports\\n try:\\n resource = '/topology'\\n data = {\\n 'networks': networks,\\n }\\n ret = self.servers.put(resource, data)\\n if not self.servers.action_success(ret):\\n raise RemoteRestError(ret[2])\\n return ret\\n except RemoteRestError as e:\\n LOG.error(_('QuantumRestProxy: Unable to update remote network: '\\n '%s'), e.message)\\n raise\",\n \"def configureMaster(self):\\n\\t\\t\\n\\t\\tfin = open('/opt/google/earth/free/drivers.ini', 'r')\\n\\t\\tfout = open('/etc/X11/ge-drivers.ini', 'w')\\n\\t\\t\\n\\t\\tfor line in fin.readlines():\\n\\t\\t\\tfout.write(line)\\n\\t\\t\\tif line.find('SETTINGS {') != 0:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tfout.write('\\\\tViewSync/send = true\\\\n')\\n\\t\\t\\tfout.write('\\\\tViewSync/receive = false\\\\n')\\n\\n\\t\\t\\tfout.write('\\\\tViewSync/hostname = %s\\\\n' %\\n\\t\\t\\t\\t self.db.getHostAttr('localhost',\\n\\t\\t\\t\\t\\t\\t 'Kickstart_PrivateBroadcast'))\\n fout.write('\\\\tViewSync/port = 21567\\\\n')\\n\\t\\t\\tfout.write('\\\\n')\\n\\t\\t\\tfout.write('\\\\tViewSync/horizFov = 60\\\\n')\\n fout.write('\\\\tViewSync/rollOffset = 0\\\\n')\\n fout.write('\\\\tViewSync/yawOffset = 0\\\\n')\\n\\t\\t\\tfout.write('\\\\tViewSync/pitchOffset = 0\\\\n')\\n\\t\\t\\tfout.write('\\\\n')\\n\\n\\n\\t\\tfin.close()\\n\\t\\tfout.close()\\n\\n\\t\\tshutil.copy('/etc/X11/ge-drivers.ini', '/opt/google/earth/free/drivers.ini')\",\n \"def _set_state_internal(self, state=None, temperature=None, force=False):\\n try:\\n changed = force\\n if state and state != self._state.state:\\n self._state.state = state\\n changed = True\\n if temperature is not None and temperature != self._temperature:\\n changed = True\\n self._temperature = temperature\\n finally:\\n if changed:\\n service.ServiceStateChangeRegistry.notify(self._path, self.get_state())\",\n \"def sweep_relay():\",\n \"def _sync_power_states(self, context):\\n db_instances = objects.InstanceList.get_by_host(context, self.host,\\n expected_attrs=[],\\n use_slave=True)\\n\\n #num_vm_instances = self.driver.get_num_instances()\\n vm_instances_stats = self.driver.list_instances_stats()\\n num_vm_instances = len(vm_instances_stats)\\n num_db_instances = len(db_instances)\\n\\n if num_vm_instances != num_db_instances:\\n LOG.warning(_LW(\\\"While synchronizing instance power states, found \\\"\\n \\\"%(num_db_instances)s instances in the database \\\"\\n \\\"and %(num_vm_instances)s instances on the \\\"\\n \\\"hypervisor.\\\"),\\n {'num_db_instances': num_db_instances,\\n 'num_vm_instances': num_vm_instances})\\n\\n def _sync(db_instance, state):\\n # NOTE(melwitt): This must be synchronized as we query state from\\n # two separate sources, the driver and the database.\\n # They are set (in stop_instance) and read, in sync.\\n @utils.synchronized(db_instance.uuid)\\n def query_driver_power_state_and_sync():\\n self._query_driver_power_state_and_sync(context, db_instance, state)\\n\\n try:\\n query_driver_power_state_and_sync()\\n except Exception:\\n LOG.exception(_LE(\\\"Periodic sync_power_state task had an \\\"\\n \\\"error while processing an instance.\\\"),\\n instance=db_instance)\\n\\n self._syncs_in_progress.pop(db_instance.uuid)\\n\\n for db_instance in db_instances:\\n # process syncs asynchronously - don't want instance locking to\\n # block entire periodic task thread\\n uuid = db_instance.uuid\\n if uuid in self._syncs_in_progress:\\n LOG.debug('Sync already in progress for %s' % uuid)\\n else:\\n LOG.debug('Triggering sync for uuid %s' % uuid)\\n provider_instance_id = self._get_provider_instance_id(uuid)\\n provider_instance_state = vm_instances_stats.get(provider_instance_id,\\n power_state.NOSTATE)\\n\\n self._syncs_in_progress[uuid] = True\\n self._sync_power_pool.spawn_n(_sync, db_instance, provider_instance_state)\",\n \"def _pre_sync(self):\",\n \"def update(self):\\n self.device = self._api.device_query(self._hardware_address, {})\",\n \"def sync_local_fabric_env(self):\\n env.sync_filename = '/tmp/{0}_env.txt'.format(time.time())\\n env_copy = self.env\\n env_copy.use_ssh_config = False\\n env_copy.host = False\\n env_copy.host_string = False\\n env_copy.local_deployment = True\\n # TODO: add context from each need to repopulate\\n with self.file.tmpfile(self.to_json(env_copy, cls=SilentEncoder)) as f:\\n self.up(f.name, env.sync_filename)\",\n \"def set_local(self):\\n self.write_to_serial(':SYST:LOC')\",\n \"def restore_network(self):\\n Blockade.blockade_join()\",\n \"def update_cmts_isc_dhcp_config(self, board_config):\\n self.setup_dhcp_config(board_config)\\n self.setup_dhcp6_config(board_config)\\n raise NotImplementedError\",\n \"def update_host(self, conf, tenant_id, network_id, host_id, body):\\n\\t\\tpass\",\n \"def update_config():\\n \\n dburl = dbconn.DbURL()\\n conn = dbconn.connect(dburl, utility=True)\\n \\n logger.info('Updating catalog...')\\n sql = \\\"SELECT gp_activate_standby()\\\"\\n dbconn.execSQL(conn, sql)\\n\\n conn.commit()\\n conn.close()\\n\\n logger.info('Database catalog updated successful')\",\n \"async def sync_master(self):\\n if not [entity for entity in self._casatunes_entities() if entity.is_client]:\\n await self.coordinator.data.zone_master(self.zone_master, False)\\n await self.coordinator.async_refresh()\\n _LOGGER.debug(\\\"%s zone is no longer master.\\\", self.zone_master)\",\n \"async def _hardcore_setheist(self, ctx):\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n\\r\\n if config[\\\"Hardcore\\\"]:\\r\\n config[\\\"Hardcore\\\"] = False\\r\\n msg = \\\"Hardcore mode now OFF.\\\"\\r\\n else:\\r\\n config[\\\"Hardcore\\\"] = True\\r\\n msg = \\\"Hardcore mode now ON! **Warning** death will result in credit **and chip wipe**.\\\"\\r\\n await self.thief.config.guild(guild).Config.set(config)\\r\\n await ctx.send(msg)\",\n \"def update(self):\\n #self._switch.odlclient._request_json(self._path, method=\\\"put\\\", json={\\n # \\\"flow\\\": self._odl_inventory()\\n #})\\n self.remove() # actually, remove only uses self.switch and self.id, so this removes the other entry as well.\\n self.deploy()\",\n \"def do_command(self, args):\\n hostops = dbops.Hosts()\\n hostops.state(args)\",\n \"def update_target_network(self):\\n self.target_dqn.set_weights.remote(self.dqn.get_weights.remote())\",\n \"def update(self) -> None:\\n self._state = b'\\\\x00' == self._device.readCharacteristic(self._handle)\\n print(\\\"state\\\", self._state)\",\n \"def _setup_communication(self):\\n state = self.ui.checkBox_comm.checkState()\\n if state:\\n try:\\n sys.path.append(\\\"..\\\")\\n from zmq_interface.gui_interface import ZmqInterface\\n except ImportError as e:\\n self.write_text(\\\"ZMQ interface failed to import. No remote control for this session.\\\")\\n self.disable_visualizer()\\n return\\n try:\\n ##TODO: let user specify ports\\n self.com = ZmqInterface(rep_port=REPLY_PORT,\\n gui_handle=self)\\n except Exception as e:\\n #traceback.print_exc(file=sys.stdout)\\n self.write_text(\\\"ZMQ interface failed to start. No remote control for this session. Reason: %s\\\" % e)\\n self.disable_visualizer()\\n return\\n self.start = self._start_session\\n self.stop = self._stop_session\\n self.load_config = self._load_state\\n self.save_config = self._save_state\\n self.com_timer = QtCore.QTimer()\\n self.com_timer.timeout.connect(self._check_coms)\\n self.com_timer.start(200)\\n self.write_text(\\\"ZMQ interface set up. Reply port on %s\\\" % self.com.rep_port)\\n self.enable_visualizer()\\n else:\\n if self.com:\\n self.com.close()\\n if self.com_timer:\\n self.com_timer.stop()\\n self.com = None\\n self.com_timer = None\\n self.enable_visualizer()\\n self.write_text(\\\"ZMQ interface closed.\\\")\",\n \"def sync_state_changed(self, state):\\n return\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def set_state(self, state):\\n self.env.sim.set_state(state)\\n mjlib.mj_fwdPosition(self.env.sim.model, self.env.sim.data)\\n mjlib.mj_fwdVelocity(self.env.sim.model, self.env.sim.data)\\n mjlib.mj_fwdAcceleration(self.env.sim.model, self.env.sim.data)\\n mjlib.mj_fwdActuation(self.env.sim.model, self.env.sim.data)\\n mjlib.mj_fwdConstraint(self.env.sim.model, self.env.sim.data)\\n self._sync_dynmodel()\",\n \"def sync_remote(self, other):\\n pass # TODO\",\n \"def remote(self):\\n logging.info(__name__ + ' : Set control to remote & locked')\\n self.set_remote_status(1)\",\n \"def test_prefer_chassis_as_gw(self):\\n expected_key = 'external-ids:ovn-cms-options'\\n expected_value = 'enable-chassis-as-gw'\\n with self.config_change(\\n {}, {'prefer-chassis-as-gw': True},\\n reset_to_charm_default=True):\\n for unit in zaza.model.get_units(self.application_name):\\n self.assertEqual(\\n zaza.model.run_on_unit(\\n unit.entity_id,\\n 'ovs-vsctl get open-vswitch . {}'.format(expected_key)\\n )['Stdout'].rstrip(),\\n expected_value)\\n logging.info(\\n '{}: \\\"{}\\\" set to \\\"{}\\\"'\\n .format(unit.entity_id, expected_key, expected_value))\\n logging.info('Config restored, checking things went back to normal')\\n for unit in zaza.model.get_units(self.application_name):\\n self.assertEqual(\\n zaza.model.run_on_unit(\\n unit.entity_id,\\n 'ovs-vsctl get open-vswitch . '\\n 'external-ids:ovn-cms-options')['Code'],\\n '1')\\n logging.info(\\n '{}: \\\"{}\\\" no longer present'\\n .format(unit.entity_id, expected_key))\",\n \"def update(self):\\n self._state = status\\n attributes['host'] = host\\n attributes['port'] = port\\n self.custom_attributes = attributes\",\n \"def update_target_network(self):\\r\\n self.send(self.server_conn, (sys._getframe().f_code.co_name, {}))\",\n \"def testPutNetworkLocalIp(self):\\n models.System.objects.all().delete()\\n self._saveSystem()\\n old_count = models.Network.objects.count()\\n self._put('inventory/networks/1/',\\n data=testsxml.network_put_xml_opt_ip_addr % \\\"169.254.4.4\\\",\\n username=\\\"admin\\\", password=\\\"password\\\")\\n self.assertEquals(old_count, models.Network.objects.count())\\n\\n self._put('inventory/networks/1/',\\n data=testsxml.network_put_xml_opt_ip_addr % \\\"4.4.4.4\\\",\\n username=\\\"admin\\\", password=\\\"password\\\")\\n self.assertEquals(old_count + 1, models.Network.objects.count())\",\n \"def synchronize(self, new_state_dict: Dict[str, np.ndarray]):\\n self._synchronize(self.dqn, new_state_dict)\",\n \"def change_state(self):\\n new_state = 0 if self.state.state == 1 else 1\\n answer = UsbHost.send_query(self.state.ser, \\\"SetState\\\", str(self.state.device_id), new_state)\\n if answer in wrong_answers:\\n error_message(\\\"Не удалось сменить состояние\\\")\\n self.statusbar.showMessage(answer_translate[answer])\\n else:\\n self.statusbar.clearMessage()\\n self.state.state = new_state\\n if new_state == 1:\\n self.set_auto_active()\\n if new_state == 0:\\n self.set_hand_active()\",\n \"def update_target_network(self) -> NoReturn:\\n self.target.load_state_dict(self.model.state_dict())\",\n \"def connect_to_master():\",\n \"def update(self) -> None:\\n self._gateway.update()\",\n \"def sync_target_network(self):\\n for t, e in zip(\\n self.target_network.trainable_variables, self.online_network.trainable_variables\\n ):\\n t.assign(e)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6180898","0.5961581","0.56596506","0.5563325","0.55320233","0.5489496","0.54843223","0.5393621","0.5317044","0.5298483","0.52749354","0.5256482","0.5226604","0.5218777","0.51281685","0.5090927","0.5090464","0.50808954","0.5073649","0.5064125","0.50575954","0.50570637","0.50452036","0.500757","0.50020987","0.49831516","0.49780908","0.49780908","0.49716738","0.49561352","0.4945451","0.4943557","0.49413657","0.49404845","0.49368498","0.49352416","0.49330553","0.4928883","0.49259177","0.49104935","0.49101922","0.49031663","0.4903081","0.48986343","0.4886376","0.48838907","0.4883253","0.4875042","0.48721102","0.48705554","0.48654082","0.48581094","0.48568454","0.48516914","0.48488852","0.4845425","0.48420876","0.48406675","0.48402536","0.48348626","0.4833364","0.48300287","0.48255444","0.482453","0.4820319","0.48149863","0.48141128","0.48133188","0.48054183","0.47988793","0.47900665","0.47884554","0.47880515","0.47880197","0.4787204","0.47822595","0.47812894","0.47777012","0.47747466","0.47729918","0.476505","0.47641495","0.47614056","0.4760146","0.47533926","0.47494796","0.47467223","0.47458062","0.47455695","0.47426674","0.4741894","0.47410896","0.4739647","0.4737633","0.47371024","0.47331387","0.4731941","0.47260267","0.47254455","0.47214"],"string":"[\n \"0.6180898\",\n \"0.5961581\",\n \"0.56596506\",\n \"0.5563325\",\n \"0.55320233\",\n \"0.5489496\",\n \"0.54843223\",\n \"0.5393621\",\n \"0.5317044\",\n \"0.5298483\",\n \"0.52749354\",\n \"0.5256482\",\n \"0.5226604\",\n \"0.5218777\",\n \"0.51281685\",\n \"0.5090927\",\n \"0.5090464\",\n \"0.50808954\",\n \"0.5073649\",\n \"0.5064125\",\n \"0.50575954\",\n \"0.50570637\",\n \"0.50452036\",\n \"0.500757\",\n \"0.50020987\",\n \"0.49831516\",\n \"0.49780908\",\n \"0.49780908\",\n \"0.49716738\",\n \"0.49561352\",\n \"0.4945451\",\n \"0.4943557\",\n \"0.49413657\",\n \"0.49404845\",\n \"0.49368498\",\n \"0.49352416\",\n \"0.49330553\",\n \"0.4928883\",\n \"0.49259177\",\n \"0.49104935\",\n \"0.49101922\",\n \"0.49031663\",\n \"0.4903081\",\n \"0.48986343\",\n \"0.4886376\",\n \"0.48838907\",\n \"0.4883253\",\n \"0.4875042\",\n \"0.48721102\",\n \"0.48705554\",\n \"0.48654082\",\n \"0.48581094\",\n \"0.48568454\",\n \"0.48516914\",\n \"0.48488852\",\n \"0.4845425\",\n \"0.48420876\",\n \"0.48406675\",\n \"0.48402536\",\n \"0.48348626\",\n \"0.4833364\",\n \"0.48300287\",\n \"0.48255444\",\n \"0.482453\",\n \"0.4820319\",\n \"0.48149863\",\n \"0.48141128\",\n \"0.48133188\",\n \"0.48054183\",\n \"0.47988793\",\n \"0.47900665\",\n \"0.47884554\",\n \"0.47880515\",\n \"0.47880197\",\n \"0.4787204\",\n \"0.47822595\",\n \"0.47812894\",\n \"0.47777012\",\n \"0.47747466\",\n \"0.47729918\",\n \"0.476505\",\n \"0.47641495\",\n \"0.47614056\",\n \"0.4760146\",\n \"0.47533926\",\n \"0.47494796\",\n \"0.47467223\",\n \"0.47458062\",\n \"0.47455695\",\n \"0.47426674\",\n \"0.4741894\",\n \"0.47410896\",\n \"0.4739647\",\n \"0.4737633\",\n \"0.47371024\",\n \"0.47331387\",\n \"0.4731941\",\n \"0.47260267\",\n \"0.47254455\",\n \"0.47214\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6583717"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79785,"cells":{"query":{"kind":"string","value":"Resync the dhcp state at the configured interval."},"document":{"kind":"string","value":"def _periodic_resync_helper(self):\n while True:\n eventlet.sleep(self.conf.resync_interval)\n if self.needs_resync:\n self.needs_resync = False\n self.sync_state()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n active_networks = set(self.plugin_rpc.get_active_networks())\n for deleted_id in known_networks - active_networks:\n self.disable_dhcp_helper(deleted_id)\n\n for network_id in active_networks:\n self.refresh_dhcp_helper(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Unable to sync network state.'))","def reconfigure(self):\n log.debug('Reconfiguring and restarting the DHCP daemon...')\n\n # Don't set the daemon running status here, but let the status\n # check take care of that.\n\n p = Properties(self.storage, CONFIG_SECTION)\n p.addCallback(self.changed).\\\n addCallback(lambda trigger: p.load()).\\\n addCallback(self.emit_config, p).\\\n addCallback(self.restart_daemon).\\\n addErrback(self.restart_error)","def renew_dhcp_lease(self):\n\t\tresponse = os.system(\"/sbin/dhclient -r;/sbin/dhclient\")\n\t\tif response != 0:\n\t\t\tprint \"Network restart failed. DHCP Lease failed.\"","def refresh_dhcp_helper(self, network_id):\n old_network = self.cache.get_network_by_id(network_id)\n if not old_network:\n # DHCP current not running for network.\n return self.enable_dhcp_helper(network_id)\n\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\n\n if new_cidrs and old_cidrs == new_cidrs:\n self.call_driver('reload_allocations', network)\n self.cache.put(network)\n elif new_cidrs:\n if self.call_driver('restart', network):\n self.cache.put(network)\n else:\n self.disable_dhcp_helper(network.id)","def pim_vrrp_updown(self):\n print \"Changing PIM DR and VRRP priority on {0}\".format(self.device_name)\n _pim_vrrp_config_template=[\"interface \",\"ip pim dr-priority \", \"vrrp \", \"priority \"]\n try:\n _vrrp_interfaces=self.show_command(\"show vrrp\")[\"TABLE_vrrp_group\"]\n except Exception as err:\n print err\n return\n if type(_vrrp_interfaces) == dict:\n # If there is only one VRRP interface, NXAPI returns dict instead of list of dict.\n _vrrp_interfaces=[_vrrp_interfaces]\n for _entry in _vrrp_interfaces:\n _pim_vrrp_config_command=[]\n time.sleep(5)\n if self.resume:\n if _entry[\"ROW_vrrp_group\"][\"sh_priority\"] == 10:\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[0]+_entry[\"ROW_vrrp_group\"][\"sh_if_index\"])\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[1]+str(250))\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[2]+str(_entry[\"ROW_vrrp_group\"][\"sh_group_id\"]))\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[3]+str(250))\n print \"Restoring VRRP and PIM DR priority on {0}\\n\".format(_entry[\"ROW_vrrp_group\"][\"sh_if_index\"])\n else:\n if _entry[\"ROW_vrrp_group\"][\"sh_group_state\"] == \"Master\":\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[0]+_entry[\"ROW_vrrp_group\"][\"sh_if_index\"])\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[1]+str(10))\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[2]+str(_entry[\"ROW_vrrp_group\"][\"sh_group_id\"]))\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[3]+str(10))\n print \"Lowering VRRP and PIM DR priority on {0}\\n\".format(_entry[\"ROW_vrrp_group\"][\"sh_if_index\"])\n try:\n self.conf_command(_pim_vrrp_config_command)\n except Exception as err:\n print err\n pass","def subnet_update_end(self, payload):\n network_id = payload['subnet']['network_id']\n self.refresh_dhcp_helper(network_id)","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def dhcp_renew(ifname):\n\n logging.debug('Renewing %s DHCP lease...', ifname)\n\n try:\n subprocess.call(['dhcpcd', '--rebind', ifname])\n except OSError, err:\n if err.errno != errno.ENOENT:\n raise err\n\n try:\n call_timeout(['dhclient', '-1', ifname], timeout=5)\n except OSError, err:\n if err.errno == errno.ENOENT:\n logging.critical('Neither dhcpcd nor dhclient were found.')\n else:\n raise err","def dhcp_utilization(self, dhcp_utilization):\n\n self._dhcp_utilization = dhcp_utilization","def reconfigure(self, behavior_params=None, async=True):\n self._op_api.run('api.service.reconfigure',\n func=self.do_reconfigure,\n func_kwds={'behavior_params': behavior_params},\n async=async,\n exclusive=True)","def refresh_config(self):\n\t\treturn Job(SDK.PrlVm_RefreshConfig(self.handle)[0])","def refresh():\n\n if _TRAFFICCTL:\n cmd = _traffic_ctl(\"config\", \"reload\")\n else:\n cmd = _traffic_line(\"-x\")\n\n return _subprocess(cmd)","def run(self):\n self.sync_state()\n self.periodic_resync()\n self.lease_relay.start()\n self.notifications.run_dispatch(self)","def autosync(self):\n if self.atbaudrate != 115200:\n self.setBaudrate(self.atbaudrate)\n print(\"Trying autosync\")\n self.send('\\r\\n')\n time.sleep(1.0)\n self.send('+++')\n self.expect('OK', timeout=1.1)\n for i in range(5):\n self.send('\\r\\nATI\\r\\n')\n if not self.expect('SiK .* on', timeout=0.5):\n print(\"Failed to get SiK banner\")\n continue\n self.send('\\r\\n')\n time.sleep(0.2)\n self.port.flushInput()\n self.send('AT&UPDATE\\r\\n')\n time.sleep(0.7)\n self.port.flushInput()\n if self.atbaudrate != 115200:\n self.setBaudrate(115200)\n print(\"Sent update command\")\n return True\n if self.atbaudrate != 115200:\n self.setBaudrate(115200)\n return False","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def update_cmts_isc_dhcp_config(self, board_config):\n self.setup_dhcp_config(board_config)\n self.setup_dhcp6_config(board_config)\n raise NotImplementedError","async def async_update(self):\n LOG.debug(f\"{self._name} async_update() called\")\n\n # delay reading allow any pending switch changes to be applied\n await asyncio.sleep(1.0)\n\n actual_speed = self._determine_current_relay_speed()\n LOG.debug(f\"{self._name} async_update() = {actual_speed}\")\n self._update_speed(actual_speed)","async def async_update(self) -> None:\n if (\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\n == BSH_POWER_ON\n ):\n self._state = True\n elif (\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\n == self.device.power_off_state\n ):\n self._state = False\n elif self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(\n ATTR_VALUE, None\n ) in [\n \"BSH.Common.EnumType.OperationState.Ready\",\n \"BSH.Common.EnumType.OperationState.DelayedStart\",\n \"BSH.Common.EnumType.OperationState.Run\",\n \"BSH.Common.EnumType.OperationState.Pause\",\n \"BSH.Common.EnumType.OperationState.ActionRequired\",\n \"BSH.Common.EnumType.OperationState.Aborting\",\n \"BSH.Common.EnumType.OperationState.Finished\",\n ]:\n self._state = True\n elif (\n self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(ATTR_VALUE)\n == \"BSH.Common.EnumType.OperationState.Inactive\"\n ):\n self._state = False\n else:\n self._state = None\n _LOGGER.debug(\"Updated, new state: %s\", self._state)","def agent_updated(self, context, payload):\n if payload['admin_state_up'] != self.admin_state_up:\n self.admin_state_up = payload['admin_state_up']\n if self.admin_state_up:\n self.needs_resync = True\n else:\n for pool_id in self.cache.get_pool_ids():\n self.destroy_device(pool_id)\n LOG.info(_(\"agent_updated by server side %s!\"), payload)","async def async_update(self) -> None:\n if not self.last_seen:\n return\n if self.location_name:\n self._state = self.location_name\n elif self.gps is not None and self.source_type == SourceType.GPS:\n zone_state = zone.async_active_zone(\n self.hass, self.gps[0], self.gps[1], self.gps_accuracy\n )\n if zone_state is None:\n self._state = STATE_NOT_HOME\n elif zone_state.entity_id == zone.ENTITY_ID_HOME:\n self._state = STATE_HOME\n else:\n self._state = zone_state.name\n elif self.stale():\n self.mark_stale()\n else:\n self._state = STATE_HOME\n self.last_update_home = True","def set_update_interval (self, interval):\n\t\tif self.__timeout:\n\t\t\tgobject.source_remove(self.__timeout)\n #print \"update interval : %s min\" % interval\n\t\tself.__timeout = gobject.timeout_add(interval*60*1000, self.update)","def periodic_resync(self):\n eventlet.spawn(self._periodic_resync_helper)","async def async_reconnect(self) -> None:\n await self.async_disconnect()\n await asyncio.sleep(1)\n await self.async_connect()","def change_dhcp_response_time(self, mac, time):\n self.execute_script('change_dhcp_response_time', mac, time)","def reload_config(self):\n pass","def sync():\n sync_ssda()","def syncrepl_refreshdone(self):\n pass","def refresh_configuration(self):\n pass","def snooze(self, interval: Optional[float] = 300.0):\n self._get_backend().snooze_alarm(interval=interval)","def _schedule_refresh(self, delay: int) -> asyncio.Task:\n\n async def _refresh_task(self: Instance, delay: int) -> InstanceMetadata:\n \"\"\"\n A coroutine that sleeps for the specified amount of time before\n running _perform_refresh.\n \"\"\"\n refresh_task: asyncio.Task\n try:\n logger.debug(f\"['{self._instance_connection_string}']: Entering sleep\")\n if delay > 0:\n await asyncio.sleep(delay)\n refresh_task = self._loop.create_task(self._perform_refresh())\n refresh_data = await refresh_task\n except asyncio.CancelledError:\n logger.debug(\n f\"['{self._instance_connection_string}']: Schedule refresh task cancelled.\"\n )\n raise\n # bad refresh attempt\n except Exception as e:\n logger.exception(\n f\"['{self._instance_connection_string}']: \"\n \"An error occurred while performing refresh. \"\n \"Scheduling another refresh attempt immediately\",\n exc_info=e,\n )\n # check if current metadata is invalid (expired),\n # don't want to replace valid metadata with invalid refresh\n if not await _is_valid(self._current):\n self._current = refresh_task\n # schedule new refresh attempt immediately\n self._next = self._schedule_refresh(0)\n raise\n # if valid refresh, replace current with valid metadata and schedule next refresh\n self._current = refresh_task\n # calculate refresh delay based on certificate expiration\n delay = _seconds_until_refresh(refresh_data.expiration)\n self._next = self._schedule_refresh(delay)\n\n return refresh_data\n\n # schedule refresh task and return it\n scheduled_task = self._loop.create_task(_refresh_task(self, delay))\n return scheduled_task","def async_update_stale(self, now: datetime) -> None:\n for device in self.devices.values():\n if (device.track and device.last_update_home) and device.stale(now):\n self.hass.async_create_task(device.async_update_ha_state(True))","def reload_when_connected(self, inst, poll_ms=500):\n if inst.isConnected():\n self.reload()\n else:\n self.after(poll_ms, utils.named_partial(self.reload_when_connected, inst, poll_ms))","def _delay_update(now):\n _LOGGER.debug(\n \"%s Called delayed (%ssec) update\", self._name, self._delay\n )\n self.schedule_update_ha_state()\n self._timer = None","def config_dhcpv6_options(ssh_conn_obj, ztp_params, config_params, options=dict(), cli_type=\"\"):\n cli_type = st.get_ui_type(config_params.dut, cli_type=cli_type)\n retry_count = config_params.retry_count if \"retry_count\" in config_params and config_params.retry_count else 0\n iteration = config_params.iteration if \"iteration\" in config_params and config_params.iteration else 300\n delay = config_params.delay if \"delay\" in config_params and config_params.delay else 3\n expect_reboot = True if \"expect_reboot\" in options and options [\"expect_reboot\"] else False\n st.log(config_params)\n if \"func_name\" in config_params:\n syslog_file_names = [\"syslog_1_{}\".format(config_params.func_name), \"syslog_{}\".format(config_params.func_name)]\n if \"json_content\" in config_params:\n file_path = basic_obj.write_to_json_file(config_params.json_content)\n st.log(file_path)\n if file_path:\n destination_path = \"{}{}/{}\".format(config_params.home_path, ztp_params.config_path, config_params.ztp_file)\n st.log(destination_path)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n config_params.option_59_url = \"http://[{}]{}/{}\".format(config_params.static_ip, ztp_params.config_path, config_params.ztp_file)\n config_params.search_pattern = r'\\s*option\\s+dhcp6.boot-file-url\\s+\"\\S+\";'\n write_option_59_to_dhcp_server(ssh_conn_obj, config_params)\n basic_obj.service_operations(ssh_conn_obj, config_params.dhcp6_service_name, \"restart\", \"server\")\n if not verify_dhcpd_service_status(ssh_conn_obj, config_params.dhcpd6_pid):\n st.log(\"{} service is running which is not expected\".format(config_params.dhcp6_service_name))\n st.report_fail(\"service_running_not_expected\", config_params.dhcp6_service_name)\n reboot_type = config_params.reboot_type if \"reboot_type\" in config_params and config_params.reboot_type else \"normal\"\n if \"ztp_operation\" in config_params:\n config_params.ztp_operation = \"reboot\" if cli_type == \"klish\" else config_params.ztp_operation\n if config_params.ztp_operation == \"reboot\":\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\n elif config_params.ztp_operation == \"run\":\n ztp_operations(config_params.dut, config_params.ztp_operation)\n else:\n st.log(\"ZTP operation is not mentioned hence rebooting the device ...\")\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\n if \"reboot_on_success\" in options and options[\"reboot_on_success\"]:\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=options[\"reboot_on_success\"], cli_type=cli_type)\n else:\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\n if not result:\n if \"logs_path\" in config_params and \"func_name\" in config_params:\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n if \"reboot_on_success\" in options and options[\"reboot_on_success\"]:\n reboot_obj.config_reload(config_params.dut)\n st.wait(5)\n if not ip_obj.ping(config_params.dut, config_params.static_ip, family=\"ipv6\"):\n st.log(\"Pinging to DHCP server failed from DUT, issue either with DUT or server\")\n # intf_obj.enable_dhcp_on_interface(config_params.dut, config_params.network_port, \"v6\")\n if not verify_ztp_status(config_params.dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in config_params and \"func_name\" in config_params:\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n verify_ztp_filename_logs(config_params.dut, config_params)\n if \"ztp_log_string\" in config_params and config_params.ztp_log_string:\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path, config_params.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(config_params.ztp_log_path, config_params.ztp_log_string))\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path_1, config_params.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(config_params.ztp_log_path_1, config_params.ztp_log_string))\n st.report_fail(\"ztp_log_verification_failed\", config_params.ztp_log_path_1, config_params.ztp_log_string)\n if \"result\" in config_params and config_params.result == \"pass\":\n st.report_pass(\"test_case_passed\")","def update(self):\n self._state = get_local_ip()","async def _refresh_task(self: Instance, delay: int) -> InstanceMetadata:\n refresh_task: asyncio.Task\n try:\n logger.debug(f\"['{self._instance_connection_string}']: Entering sleep\")\n if delay > 0:\n await asyncio.sleep(delay)\n refresh_task = self._loop.create_task(self._perform_refresh())\n refresh_data = await refresh_task\n except asyncio.CancelledError:\n logger.debug(\n f\"['{self._instance_connection_string}']: Schedule refresh task cancelled.\"\n )\n raise\n # bad refresh attempt\n except Exception as e:\n logger.exception(\n f\"['{self._instance_connection_string}']: \"\n \"An error occurred while performing refresh. \"\n \"Scheduling another refresh attempt immediately\",\n exc_info=e,\n )\n # check if current metadata is invalid (expired),\n # don't want to replace valid metadata with invalid refresh\n if not await _is_valid(self._current):\n self._current = refresh_task\n # schedule new refresh attempt immediately\n self._next = self._schedule_refresh(0)\n raise\n # if valid refresh, replace current with valid metadata and schedule next refresh\n self._current = refresh_task\n # calculate refresh delay based on certificate expiration\n delay = _seconds_until_refresh(refresh_data.expiration)\n self._next = self._schedule_refresh(delay)\n\n return refresh_data","async def async_turn_off(self):\n await self.coordinator.data.turn_off(self.zone_id)\n await self.coordinator.async_refresh()","async def async_update_config(self, config: ConfigType) -> None:\n self._config = config\n # just in case min/max values changed\n if self._current_value is None:\n return\n self._current_value = min(self._current_value, self._maximum)\n self._current_value = max(self._current_value, self._minimum)\n self.async_write_ha_state()","async def _async_update_data(self) -> dict[str, str]:\n try:\n state = await self.device.get_state()\n except JvcProjectorConnectError as err:\n raise UpdateFailed(f\"Unable to connect to {self.device.host}\") from err\n except JvcProjectorAuthError as err:\n raise ConfigEntryAuthFailed(\"Password authentication failed\") from err\n\n old_interval = self.update_interval\n\n if state[const.POWER] != const.STANDBY:\n self.update_interval = INTERVAL_FAST\n else:\n self.update_interval = INTERVAL_SLOW\n\n if self.update_interval != old_interval:\n _LOGGER.debug(\"Changed update interval to %s\", self.update_interval)\n\n return state","def _refresh_discovery(self):\n if self.terminate_flag:\n return\n\n self.devices = discover_drones(self.ip_range, self.skyserve_port)\n time.sleep(self.refresh_interval / 1000)\n self._refresh_discovery()","async def async_turn_off(self):\n path = \"/ip/firewall/nat\"\n param = \".id\"\n value = None\n for uid in self._ctrl.data[\"nat\"]:\n if (\n self._ctrl.data[\"nat\"][uid][\"name\"]\n == f\"{self._data['protocol']}:{self._data['dst-port']}\"\n ):\n value = self._ctrl.data[\"nat\"][uid][\".id\"]\n\n mod_param = \"disabled\"\n mod_value = True\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n await self._ctrl.async_update()","async def set_update_interval(call):\n _LOGGER.debug(\"call %s\", call)\n service_data = call.data\n email = service_data.get(CONF_EMAIL, \"\")\n\n if len(hass.config_entries.async_entries(DOMAIN)) > 1 and not email:\n raise ValueError(\"Email address missing\")\n controller: Controller = None\n for entry in hass.config_entries.async_entries(DOMAIN):\n if (\n len(hass.config_entries.async_entries(DOMAIN)) > 1\n and entry.title != email\n ):\n continue\n controller = hass.data[DOMAIN].get(entry.entry_id)[\"coordinator\"].controller\n if controller is None:\n raise ValueError(f\"No Tesla controllers found for email {email}\")\n\n vin = service_data.get(ATTR_VIN, \"\")\n update_interval = service_data.get(CONF_SCAN_INTERVAL, DEFAULT_SCAN_INTERVAL)\n _LOGGER.debug(\n \"Service %s called with email: %s vin %s interval %s\",\n SERVICE_SCAN_INTERVAL,\n email,\n vin,\n update_interval,\n )\n old_update_interval = controller.get_update_interval_vin(vin=vin)\n if old_update_interval != update_interval:\n _LOGGER.debug(\n \"Changing update_interval from %s to %s for %s\",\n old_update_interval,\n update_interval,\n vin,\n )\n controller.set_update_interval_vin(vin=vin, value=update_interval)\n return True","def configure_lldp_reinit(device, timer):\r\n try:\r\n device.configure(f'lldp reinit {timer}')\r\n except SubCommandFailure as e:\r\n raise SubCommandFailure(\r\n \"Could not configure LLDP reinit\"\r\n \"Error: {error}\".format(error=e)\r\n )","def stop_dhcp_response(self, mac):\n self.change_dhcp_response_time(mac, -1)","def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\"\"):\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\n cli_type = \"klish\" if cli_type in [\"rest-put\", \"rest-patch\"] else cli_type\n retry_count = data.retry_count if \"retry_count\" in data and data.retry_count else 0\n iteration = data.iteration if \"iteration\" in data and data.iteration else 300\n delay = data.delay if \"delay\" in data and data.delay else 3\n if \"func_name\" in data:\n syslog_file_names = [\"syslog_1_{}\".format(data.func_name), \"syslog_{}\".format(data.func_name)]\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\n if \"config_file_type\" in data and data.config_file_type == \"text\":\n file_path = \"/tmp/file_temp.json\"\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\"server\")\n elif \"config_file_type\" in data and data.config_file_type == \"EoL\":\n file_path = \"\"\n else:\n file_path = basic_obj.write_to_json_file(data.json_content)\n if file_path:\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"config_db_location\" in data and data.config_db_location == \"json\":\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"scenario\" in data and data.scenario == \"invalid-json\":\n st.log(\"Writing invalid content to make invalid json ...\")\n basic_obj.write_to_file_to_line(ssh_conn_obj, \",\", 5, destination_path, \"server\")\n if data.option_type == \"67\":\n st.log(\"Creating {} file on DHCP server ...\".format(data.config_file))\n data.search_pattern = r'\\s*option\\s+bootfile-name\\s*\\S*\\s*\"\\S+\";'\n data.option_string = \"option bootfile-name\"\n if data.type == \"http\":\n data.option_url = \"http://{}{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"tftp\":\n data.option_url = \"tftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"ftp\":\n data.option_url = \"ftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n write_option_to_dhcp_server(ssh_conn_obj, data)\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\n st.log(\"{} service not running\".format(data.dhcp_service_name))\n st.report_fail(\"service_not_running\", data.dhcp_service_name)\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\n data.device_action = \"reboot\" if cli_type == \"klish\" else data.device_action\n if data.device_action == \"reboot\":\n reboot_type = data.reboot_type if \"reboot_type\" in data and data.reboot_type else \"normal\"\n basic_obj.remove_file(dut, data.config_db_path)\n st.reboot(dut, reboot_type, skip_port_wait=True)\n st.wait_system_status(dut, 500)\n elif data.device_action == \"run\":\n ztp_operations(dut, data.device_action)\n if \"band_type\" in data and data.band_type==\"inband\":\n if not basic_obj.poll_for_system_status(dut):\n st.log(\"Sytem is not ready ..\")\n st.report_env_fail(\"system_not_ready\")\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\"up\"):\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \"down\")\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \"up\")\n if interface_status is not None:\n if not interface_status:\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\n if \"service\" in data and data.service == \"disable\":\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"stop\")\n if basic_obj.verify_service_status(dut, \"ztp\"):\n st.log(\"ZTP status is not stopped\")\n st.report_fail(\"service_not_stopped\", \"ztp\")\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"start\")\n if not poll_ztp_status(dut, [\"IN-PROGRESS\", \"Not Started\", \"SUCCESS\"], cli_type=cli_type):\n st.report_fail(\"ztp_max_polling_interval\")\n if \"check\" in data and data.check == \"not\":\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n else:\n st.log(\"Iteration count {}\".format(iteration))\n st.log(\"REBOOT ON SUCCESS - {}\".format(reboot_on_success))\n if reboot_on_success:\n if \"configdb-json\" in reboot_on_success:\n st.wait_system_reboot(dut)\n st.wait_system_status(dut, 300)\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\n else:\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\n if not result:\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n if reboot_on_success:\n output = show_ztp_status(dut, cli_type=cli_type)\n if output[\"status\"] != \"SUCCESS\":\n st.wait(300, \"Waiting for device to reboot after success...\")\n st.wait_system_status(dut, 300)\n # st.wait_system_reboot(dut)\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n st.banner(boot_up_obj.sonic_installer_list(dut))\n verify_ztp_filename_logs(dut, data)\n if \"ztp_log_string\" in data and data.ztp_log_string:\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path, data.ztp_log_string))\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path_1, data.ztp_log_string))\n st.report_fail(\"ztp_log_verification_failed\", data.ztp_log_path_1, data.ztp_log_string)\n if \"result\" in data and data.result == \"pass\":\n st.report_pass(\"test_case_passed\")","def sync() -> None:","def update_isolation(self, time: int):","def _update(self, host):\n pass","def task_update(self):\n try:\n self.task_stop()\n except:\n pass\n self.update()\n self.task_start()","def activate(self):\n super().activate()\n self._change_lease_time(self.runner.config.get(\"dhcp_lease_time\"))\n self._scan_finalize()","async def async_update(self):\n _LOGGER.debug(\"%s: updating statistics\", self.entity_id)\n if self._samples_max_age is not None:\n self._purge_old()\n\n self._update_characteristics()\n\n # If max_age is set, ensure to update again after the defined interval.\n next_to_purge_timestamp = self._next_to_purge_timestamp()\n if next_to_purge_timestamp:\n _LOGGER.debug(\n \"%s: scheduling update at %s\", self.entity_id, next_to_purge_timestamp\n )\n if self._update_listener:\n self._update_listener()\n self._update_listener = None\n\n @callback\n def _scheduled_update(now):\n \"\"\"Timer callback for sensor update.\"\"\"\n _LOGGER.debug(\"%s: executing scheduled update\", self.entity_id)\n self.async_schedule_update_ha_state(True)\n self._update_listener = None\n\n self._update_listener = async_track_point_in_utc_time(\n self.hass, _scheduled_update, next_to_purge_timestamp\n )","def Restart(self, udp=False):\n self.Stop()\n self.Start(udp)","def reload_ini(self):\n while True:\n\n if round(os.path.getmtime(self.config_file)) > self.config_last_modified:\n print('Config Changes Detected, Reloading .ini File')\n config = configparser.ConfigParser()\n config.read(self.config_file)\n self._set_ini_options(config)\n self.config_last_modified = round(os.path.getmtime(self.config_file))\n\n time.sleep(3)","def periodic_flavor_sync(ctx, driver, scg_id_list):\n sync_interval = CONF.powervc.flavor_sync_interval\n\n if sync_interval is None or sync_interval == 0:\n return\n\n def flavors_sync(driver, scg_id_list):\n FlavorSync(driver, scg_id_list).synchronize_flavors(ctx)\n LOG.debug('Flavors synchronization completed')\n\n sync_flavors = loopingcall.FixedIntervalLoopingCall(flavors_sync,\n driver,\n scg_id_list)\n sync_flavors.start(interval=sync_interval, initial_delay=sync_interval)","def run(self):\n try:\n rcvdConfig = self.conn.recv(self.size)\n if self.verbosity >= 1:\n print('%s: Received request from client %s:%s.' % (self.feederName, self.addr, self.port))\n except socket.error as e:\n raise Error(e.strerror)\n self.lockConfig.acquire()\n self.config.newConfig = json.loads(rcvdConfig)\n self.config.updateConfig()\n self.lockConfig.release()\n self.conn.close()","def _async_update_humidity(self, state):\n try:\n self.ccs811_client.set_humidity(float(state.state))\n except ValueError as ex:\n _LOGGER.error(\"Unable to update from sensor: %s\", ex)","async def test_aiodiscover_finds_new_hosts_after_interval(hass: HomeAssistant) -> None:\n with patch.object(hass.config_entries.flow, \"async_init\") as mock_init, patch(\n \"homeassistant.components.dhcp.DiscoverHosts.async_discover\",\n return_value=[],\n ):\n device_tracker_watcher = dhcp.NetworkWatcher(\n hass,\n {},\n [{\"domain\": \"mock-domain\", \"hostname\": \"connect\", \"macaddress\": \"B8B7F1*\"}],\n )\n await device_tracker_watcher.async_start()\n await hass.async_block_till_done()\n\n assert len(mock_init.mock_calls) == 0\n\n with patch.object(hass.config_entries.flow, \"async_init\") as mock_init, patch(\n \"homeassistant.components.dhcp.DiscoverHosts.async_discover\",\n return_value=[\n {\n dhcp.DISCOVERY_IP_ADDRESS: \"192.168.210.56\",\n dhcp.DISCOVERY_HOSTNAME: \"connect\",\n dhcp.DISCOVERY_MAC_ADDRESS: \"b8b7f16db533\",\n }\n ],\n ):\n async_fire_time_changed(hass, dt_util.utcnow() + datetime.timedelta(minutes=65))\n await hass.async_block_till_done()\n await device_tracker_watcher.async_stop()\n await hass.async_block_till_done()\n\n assert len(mock_init.mock_calls) == 1\n assert mock_init.mock_calls[0][1][0] == \"mock-domain\"\n assert mock_init.mock_calls[0][2][\"context\"] == {\n \"source\": config_entries.SOURCE_DHCP\n }\n assert mock_init.mock_calls[0][2][\"data\"] == dhcp.DhcpServiceInfo(\n ip=\"192.168.210.56\",\n hostname=\"connect\",\n macaddress=\"b8b7f16db533\",\n )","def test_unassign_configuration_after_patch(self):\n instance_info.dbaas.instances.update(instance_info.id,\n remove_configuration=True)\n assert_equal(202, instance_info.dbaas.last_http_code)\n instance = instance_info.dbaas.instances.get(instance_info.id)\n assert_equal('RESTART_REQUIRED', instance.status)\n # restart to be sure configuration has been unassigned\n instance_info.dbaas.instances.restart(instance_info.id)\n assert_equal(202, instance_info.dbaas.last_http_code)\n sleep(2)\n\n def result_is_active():\n instance = instance_info.dbaas.instances.get(\n instance_info.id)\n if instance.status in CONFIG.running_status:\n return True\n else:\n assert_equal(\"REBOOT\", instance.status)\n return False\n\n poll_until(result_is_active)\n result = instance_info.dbaas.configurations.get(configuration_info.id)\n assert_equal(result.instance_count, 0)","async def _async_complete_schedule_update(self, _):\n _LOGGER.debug(\"Processing scheduled update for %s\", self.name)\n self._scheduled_transition_update = None\n await self._async_force_refresh_state()","def sweep_relay():","async def async_turn_off(self, **kwargs: Any) -> None:\n await self.async_publish(\n self._config[CONF_COMMAND_TOPIC],\n self._config[CONF_PAYLOAD_OFF],\n self._config[CONF_QOS],\n self._config[CONF_RETAIN],\n self._config[CONF_ENCODING],\n )\n if self._optimistic:\n # Optimistically assume that switch has changed state.\n self._attr_is_on = False\n self.async_write_ha_state()","def _post_task_update_advertise_address():\n default_network_interface = None\n\n with open(KUBE_APISERVER_CONFIG) as f:\n lines = f.read()\n m = re.search(REGEXPR_ADVERTISE_ADDRESS, lines)\n if m:\n default_network_interface = m.group(1)\n LOG.debug(' default_network_interface = %s', default_network_interface)\n\n if advertise_address and default_network_interface \\\n and advertise_address != default_network_interface:\n cmd = [\"sed\", \"-i\", \"/oidc-issuer-url/! s/{}/{}/g\".format(default_network_interface, advertise_address),\n KUBE_APISERVER_CONFIG]\n _ = _exec_cmd(cmd)","def reset_time_out(self):\n self.reconnect()\n self.reconnect_params()","def _wakeup(self, wakeup_timeout=10, response_timeout=3):\n pass","def SetAutoReconnect(self, value):\n print 'setting automatically reconnect when connection drops'\n config = ConfigParser.ConfigParser()\n config.read(self.app_conf)\n config.set(\"Settings\", \"auto_reconnect\", misc.to_bool(value))\n config.write(open(self.app_conf, \"w\"))\n self.auto_reconnect = misc.to_bool(value)","def restore_running_config(device, path, file, timeout=60):\n try:\n device.execute(\n \"copy {path}{file} running-config replace\".format(path=path, file=file),\n timeout=timeout\n )\n except SubCommandFailure as e:\n raise SubCommandFailure(\n \"Could not replace saved configuration on \"\n \"device {device}\\nError: {e}\".format(device=device.name, e=str(e))\n )","def dhcp_agent_network_remove(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def dns_sync(self, args):\r\n dns = DNSManager(self.client)\r\n vsi = VSManager(self.client)\r\n\r\n vs_id = resolve_id(vsi.resolve_ids, args.get('<identifier>'), 'VS')\r\n instance = vsi.get_instance(vs_id)\r\n zone_id = resolve_id(dns.resolve_ids, instance['domain'], name='zone')\r\n\r\n def sync_a_record():\r\n \"\"\" Sync A record \"\"\"\r\n records = dns.get_records(\r\n zone_id,\r\n host=instance['hostname'],\r\n )\r\n\r\n if not records:\r\n # don't have a record, lets add one to the base zone\r\n dns.create_record(\r\n zone['id'],\r\n instance['hostname'],\r\n 'a',\r\n instance['primaryIpAddress'],\r\n ttl=args['--ttl'])\r\n else:\r\n recs = [x for x in records if x['type'].lower() == 'a']\r\n if len(recs) != 1:\r\n raise CLIAbort(\"Aborting A record sync, found %d \"\r\n \"A record exists!\" % len(recs))\r\n rec = recs[0]\r\n rec['data'] = instance['primaryIpAddress']\r\n rec['ttl'] = args['--ttl']\r\n dns.edit_record(rec)\r\n\r\n def sync_ptr_record():\r\n \"\"\" Sync PTR record \"\"\"\r\n host_rec = instance['primaryIpAddress'].split('.')[-1]\r\n ptr_domains = self.client['Virtual_Guest'].\\\r\n getReverseDomainRecords(id=instance['id'])[0]\r\n edit_ptr = None\r\n for ptr in ptr_domains['resourceRecords']:\r\n if ptr['host'] == host_rec:\r\n ptr['ttl'] = args['--ttl']\r\n edit_ptr = ptr\r\n break\r\n\r\n if edit_ptr:\r\n edit_ptr['data'] = instance['fullyQualifiedDomainName']\r\n dns.edit_record(edit_ptr)\r\n else:\r\n dns.create_record(\r\n ptr_domains['id'],\r\n host_rec,\r\n 'ptr',\r\n instance['fullyQualifiedDomainName'],\r\n ttl=args['--ttl'])\r\n\r\n if not instance['primaryIpAddress']:\r\n raise CLIAbort('No primary IP address associated with this VS')\r\n\r\n zone = dns.get_zone(zone_id)\r\n\r\n go_for_it = args['--really'] or confirm(\r\n \"Attempt to update DNS records for %s\"\r\n % instance['fullyQualifiedDomainName'])\r\n\r\n if not go_for_it:\r\n raise CLIAbort(\"Aborting DNS sync\")\r\n\r\n both = False\r\n if not args['--ptr'] and not args['-a']:\r\n both = True\r\n\r\n if both or args['-a']:\r\n sync_a_record()\r\n\r\n if both or args['--ptr']:\r\n sync_ptr_record()","def unconfigure_radius_server_deadtime(device, server_time):\n try:\n device.configure([\n f\"no radius-server deadtime {server_time}\"\n ])\n except SubCommandFailure:\n raise SubCommandFailure(\n 'Could not unconfigure radius server deadtime'\n )","def reactivate(self) -> None:\n self.display_until = None\n self.save()","def update(self,\n dhcp_server_config_id,\n dhcp_server_config,\n ):\n return self._invoke('update',\n {\n 'dhcp_server_config_id': dhcp_server_config_id,\n 'dhcp_server_config': dhcp_server_config,\n })","def _async_update_callback(self):\n self._async_update_zone_data()\n self.async_write_ha_state()","async def attempt_reconnect(self):\n await deploy.reconnect()","def syncloss(self):\n # expect a disassociation indication with a correct status\n assert(self.a.nxapi_disassociate_ind() == True)\n \n # generate a random frame\n msdu = self.host.tx_msdu(da=self.ap1.macaddr, length=1000, prio=1)\n \n # wait for data send confirmation (not in the air)\n self.a.host_send_data_cfm(msdu)","def force_update(self):\n self.update(self.poll())","def configureDHCP():\n dhcpStart = config.get(\"hotspot\", \"dhcpstart\")\n dhcpEnd = config.get(\"hotspot\", \"dhcpend\")\n dnsmasqConfig = f\"\"\"#PI Hotspot config\ndomain-needed\nbogus-priv\ndhcp-option=option:dns-server\ndhcp-authoritative\ndhcp-range={dhcpStart},{dhcpEnd},1h\n\"\"\"\n confFile = open(\"/etc/dnsmasq.conf\", \"w\")\n confFile.write(dnsmasqConfig)\n confFile.close()","def _mark_fresh(self):\n if self._is_stale:\n self._logger.debug(\"%s: transition to fresh\", self.ping_address)\n self.on_fresh()\n self._is_stale = False","def pcp_reload_config(self, command_scope):\n\n\t\tif self.PCPConnectionStatus() != ConnStateType.OK:\n\t\t\tself.pcp_internal_error('invalid PCP connection')\n\t\t\treturn None\n\n\t\tself._PCPWrite('Z'.encode(), 1)\n\t\twsize = self.int_to_bytes(4 + 1)\n\t\tself._PCPWrite(wsize, 4)\n\t\tself._PCPWrite(command_scope.encode(), 1)\n\t\tif self.PCPFlush() < 0:\n\t\t\treturn None\n\t\tif self.Pfdebug:\n\t\t\tself.Pfdebug.write(f'DEBUG: send: tos=\"Z\", length={self.bytes_to_int(wsize)}\\n')\n\n\t\treturn self._process_pcp_response('Z')","async def async_update(self):\n await self.coordinator.async_request_refresh()","def _update_callback(self) -> None:\n self.async_schedule_update_ha_state(force_refresh=True)","def _update_callback(self) -> None:\n self.async_schedule_update_ha_state(force_refresh=True)","async def update_listener(hass, config_entry):\n controller = hass.data[DOMAIN][config_entry.entry_id][\"coordinator\"].controller\n old_update_interval = controller.update_interval\n controller.update_interval = config_entry.options.get(CONF_SCAN_INTERVAL)\n if old_update_interval != controller.update_interval:\n _LOGGER.debug(\n \"Changing scan_interval from %s to %s\",\n old_update_interval,\n controller.update_interval,\n )","def reload_config():\n subprocess.run([SUPERVISOR_CMD, \"reload\"])","async def async_update(self):\n await self.wrapper.async_request_refresh()","def spin(self):\n rate = rospy.Rate(5) # hz\n while not rospy.is_shutdown():\n if self.parameters.charging:\n self.dynamic_reconfigure_server.update_configuration({\"charging_percentage\": min(100, self.battery.percentage + self.charging_increment)})\n else:\n self.dynamic_reconfigure_server.update_configuration({\"charging_percentage\": max(0, self.battery.percentage - self.charging_increment)})\n self.battery.header.stamp = rospy.get_rostime() # get_rostime() returns the time in rospy.Time structure\n self.battery_publisher.publish(self.battery)\n rate.sleep()","async def async_update(self):\n await self.hass.async_add_job(self.ccs811_client.update)\n if self.type == SENSOR_ECO2:\n eco2 = self.ccs811_client.eco2\n self._state = eco2\n elif self.type == SENSOR_TVOC:\n self._state = self.ccs811_client.tvoc","def update(self):\n try:\n response = requests.get('{}/digital/{}'.format(\n self._resource, self._pin), timeout=10)\n self.data = {'state': response.json()['return_value']}\n except requests.exceptions.ConnectionError:\n _LOGGER.error(\"No route to device '%s'. Is device offline?\",\n self._resource)","async def async_update(self):\n await self._coordinator.async_request_refresh()","async def async_update(self):\n await self._coordinator.async_request_refresh()","async def async_update(self):\n await self._coordinator.async_request_refresh()","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n\r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","def update(self):\n self.arest.update()","def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops\n # caused by a constantly changing state value at each run.\n #\n # Example: state.value += 1\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None)","def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops caused by a constantly changing state value at each run.\n # Example: state.value += 1\n\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None\n ):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","def set_check_interval (self, interval):\n\t\tif self.__timeout:\n\t\t\tgobject.source_remove(self.__timeout)\n\t\tself.__timeout = gobject.timeout_add(interval * 60000, \n\t\t\tself.run_mailcheck)","async def async_update(self):\n _LOGGER.debug(\n \"SynoDSMSurveillanceHomeModeToggle.async_update(%s)\",\n self._api.information.serial,\n )\n self._state = await self.hass.async_add_executor_job(\n self._api.surveillance_station.get_home_mode_status\n )","def sync(self) -> None: #\n self.__target.load_state_dict(self.__policy.state_dict())","def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n \r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)","def trigger(self):\n # Update current state.\n self.current_inst = self.future_inst\n self.current_timer = self.future_timer\n # Initialize future state.\n self.future_inst = self.current_inst\n self.future_timer = max(0, self.current_timer - 1)"],"string":"[\n \"def sync_state(self):\\n LOG.info(_('Synchronizing state'))\\n known_networks = set(self.cache.get_network_ids())\\n\\n try:\\n active_networks = set(self.plugin_rpc.get_active_networks())\\n for deleted_id in known_networks - active_networks:\\n self.disable_dhcp_helper(deleted_id)\\n\\n for network_id in active_networks:\\n self.refresh_dhcp_helper(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Unable to sync network state.'))\",\n \"def reconfigure(self):\\n log.debug('Reconfiguring and restarting the DHCP daemon...')\\n\\n # Don't set the daemon running status here, but let the status\\n # check take care of that.\\n\\n p = Properties(self.storage, CONFIG_SECTION)\\n p.addCallback(self.changed).\\\\\\n addCallback(lambda trigger: p.load()).\\\\\\n addCallback(self.emit_config, p).\\\\\\n addCallback(self.restart_daemon).\\\\\\n addErrback(self.restart_error)\",\n \"def renew_dhcp_lease(self):\\n\\t\\tresponse = os.system(\\\"/sbin/dhclient -r;/sbin/dhclient\\\")\\n\\t\\tif response != 0:\\n\\t\\t\\tprint \\\"Network restart failed. DHCP Lease failed.\\\"\",\n \"def refresh_dhcp_helper(self, network_id):\\n old_network = self.cache.get_network_by_id(network_id)\\n if not old_network:\\n # DHCP current not running for network.\\n return self.enable_dhcp_helper(network_id)\\n\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\\n\\n if new_cidrs and old_cidrs == new_cidrs:\\n self.call_driver('reload_allocations', network)\\n self.cache.put(network)\\n elif new_cidrs:\\n if self.call_driver('restart', network):\\n self.cache.put(network)\\n else:\\n self.disable_dhcp_helper(network.id)\",\n \"def pim_vrrp_updown(self):\\n print \\\"Changing PIM DR and VRRP priority on {0}\\\".format(self.device_name)\\n _pim_vrrp_config_template=[\\\"interface \\\",\\\"ip pim dr-priority \\\", \\\"vrrp \\\", \\\"priority \\\"]\\n try:\\n _vrrp_interfaces=self.show_command(\\\"show vrrp\\\")[\\\"TABLE_vrrp_group\\\"]\\n except Exception as err:\\n print err\\n return\\n if type(_vrrp_interfaces) == dict:\\n # If there is only one VRRP interface, NXAPI returns dict instead of list of dict.\\n _vrrp_interfaces=[_vrrp_interfaces]\\n for _entry in _vrrp_interfaces:\\n _pim_vrrp_config_command=[]\\n time.sleep(5)\\n if self.resume:\\n if _entry[\\\"ROW_vrrp_group\\\"][\\\"sh_priority\\\"] == 10:\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[0]+_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_if_index\\\"])\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[1]+str(250))\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[2]+str(_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_group_id\\\"]))\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[3]+str(250))\\n print \\\"Restoring VRRP and PIM DR priority on {0}\\\\n\\\".format(_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_if_index\\\"])\\n else:\\n if _entry[\\\"ROW_vrrp_group\\\"][\\\"sh_group_state\\\"] == \\\"Master\\\":\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[0]+_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_if_index\\\"])\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[1]+str(10))\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[2]+str(_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_group_id\\\"]))\\n _pim_vrrp_config_command.append(_pim_vrrp_config_template[3]+str(10))\\n print \\\"Lowering VRRP and PIM DR priority on {0}\\\\n\\\".format(_entry[\\\"ROW_vrrp_group\\\"][\\\"sh_if_index\\\"])\\n try:\\n self.conf_command(_pim_vrrp_config_command)\\n except Exception as err:\\n print err\\n pass\",\n \"def subnet_update_end(self, payload):\\n network_id = payload['subnet']['network_id']\\n self.refresh_dhcp_helper(network_id)\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def dhcp_renew(ifname):\\n\\n logging.debug('Renewing %s DHCP lease...', ifname)\\n\\n try:\\n subprocess.call(['dhcpcd', '--rebind', ifname])\\n except OSError, err:\\n if err.errno != errno.ENOENT:\\n raise err\\n\\n try:\\n call_timeout(['dhclient', '-1', ifname], timeout=5)\\n except OSError, err:\\n if err.errno == errno.ENOENT:\\n logging.critical('Neither dhcpcd nor dhclient were found.')\\n else:\\n raise err\",\n \"def dhcp_utilization(self, dhcp_utilization):\\n\\n self._dhcp_utilization = dhcp_utilization\",\n \"def reconfigure(self, behavior_params=None, async=True):\\n self._op_api.run('api.service.reconfigure',\\n func=self.do_reconfigure,\\n func_kwds={'behavior_params': behavior_params},\\n async=async,\\n exclusive=True)\",\n \"def refresh_config(self):\\n\\t\\treturn Job(SDK.PrlVm_RefreshConfig(self.handle)[0])\",\n \"def refresh():\\n\\n if _TRAFFICCTL:\\n cmd = _traffic_ctl(\\\"config\\\", \\\"reload\\\")\\n else:\\n cmd = _traffic_line(\\\"-x\\\")\\n\\n return _subprocess(cmd)\",\n \"def run(self):\\n self.sync_state()\\n self.periodic_resync()\\n self.lease_relay.start()\\n self.notifications.run_dispatch(self)\",\n \"def autosync(self):\\n if self.atbaudrate != 115200:\\n self.setBaudrate(self.atbaudrate)\\n print(\\\"Trying autosync\\\")\\n self.send('\\\\r\\\\n')\\n time.sleep(1.0)\\n self.send('+++')\\n self.expect('OK', timeout=1.1)\\n for i in range(5):\\n self.send('\\\\r\\\\nATI\\\\r\\\\n')\\n if not self.expect('SiK .* on', timeout=0.5):\\n print(\\\"Failed to get SiK banner\\\")\\n continue\\n self.send('\\\\r\\\\n')\\n time.sleep(0.2)\\n self.port.flushInput()\\n self.send('AT&UPDATE\\\\r\\\\n')\\n time.sleep(0.7)\\n self.port.flushInput()\\n if self.atbaudrate != 115200:\\n self.setBaudrate(115200)\\n print(\\\"Sent update command\\\")\\n return True\\n if self.atbaudrate != 115200:\\n self.setBaudrate(115200)\\n return False\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def update_cmts_isc_dhcp_config(self, board_config):\\n self.setup_dhcp_config(board_config)\\n self.setup_dhcp6_config(board_config)\\n raise NotImplementedError\",\n \"async def async_update(self):\\n LOG.debug(f\\\"{self._name} async_update() called\\\")\\n\\n # delay reading allow any pending switch changes to be applied\\n await asyncio.sleep(1.0)\\n\\n actual_speed = self._determine_current_relay_speed()\\n LOG.debug(f\\\"{self._name} async_update() = {actual_speed}\\\")\\n self._update_speed(actual_speed)\",\n \"async def async_update(self) -> None:\\n if (\\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\\n == BSH_POWER_ON\\n ):\\n self._state = True\\n elif (\\n self.device.appliance.status.get(BSH_POWER_STATE, {}).get(ATTR_VALUE)\\n == self.device.power_off_state\\n ):\\n self._state = False\\n elif self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(\\n ATTR_VALUE, None\\n ) in [\\n \\\"BSH.Common.EnumType.OperationState.Ready\\\",\\n \\\"BSH.Common.EnumType.OperationState.DelayedStart\\\",\\n \\\"BSH.Common.EnumType.OperationState.Run\\\",\\n \\\"BSH.Common.EnumType.OperationState.Pause\\\",\\n \\\"BSH.Common.EnumType.OperationState.ActionRequired\\\",\\n \\\"BSH.Common.EnumType.OperationState.Aborting\\\",\\n \\\"BSH.Common.EnumType.OperationState.Finished\\\",\\n ]:\\n self._state = True\\n elif (\\n self.device.appliance.status.get(BSH_OPERATION_STATE, {}).get(ATTR_VALUE)\\n == \\\"BSH.Common.EnumType.OperationState.Inactive\\\"\\n ):\\n self._state = False\\n else:\\n self._state = None\\n _LOGGER.debug(\\\"Updated, new state: %s\\\", self._state)\",\n \"def agent_updated(self, context, payload):\\n if payload['admin_state_up'] != self.admin_state_up:\\n self.admin_state_up = payload['admin_state_up']\\n if self.admin_state_up:\\n self.needs_resync = True\\n else:\\n for pool_id in self.cache.get_pool_ids():\\n self.destroy_device(pool_id)\\n LOG.info(_(\\\"agent_updated by server side %s!\\\"), payload)\",\n \"async def async_update(self) -> None:\\n if not self.last_seen:\\n return\\n if self.location_name:\\n self._state = self.location_name\\n elif self.gps is not None and self.source_type == SourceType.GPS:\\n zone_state = zone.async_active_zone(\\n self.hass, self.gps[0], self.gps[1], self.gps_accuracy\\n )\\n if zone_state is None:\\n self._state = STATE_NOT_HOME\\n elif zone_state.entity_id == zone.ENTITY_ID_HOME:\\n self._state = STATE_HOME\\n else:\\n self._state = zone_state.name\\n elif self.stale():\\n self.mark_stale()\\n else:\\n self._state = STATE_HOME\\n self.last_update_home = True\",\n \"def set_update_interval (self, interval):\\n\\t\\tif self.__timeout:\\n\\t\\t\\tgobject.source_remove(self.__timeout)\\n #print \\\"update interval : %s min\\\" % interval\\n\\t\\tself.__timeout = gobject.timeout_add(interval*60*1000, self.update)\",\n \"def periodic_resync(self):\\n eventlet.spawn(self._periodic_resync_helper)\",\n \"async def async_reconnect(self) -> None:\\n await self.async_disconnect()\\n await asyncio.sleep(1)\\n await self.async_connect()\",\n \"def change_dhcp_response_time(self, mac, time):\\n self.execute_script('change_dhcp_response_time', mac, time)\",\n \"def reload_config(self):\\n pass\",\n \"def sync():\\n sync_ssda()\",\n \"def syncrepl_refreshdone(self):\\n pass\",\n \"def refresh_configuration(self):\\n pass\",\n \"def snooze(self, interval: Optional[float] = 300.0):\\n self._get_backend().snooze_alarm(interval=interval)\",\n \"def _schedule_refresh(self, delay: int) -> asyncio.Task:\\n\\n async def _refresh_task(self: Instance, delay: int) -> InstanceMetadata:\\n \\\"\\\"\\\"\\n A coroutine that sleeps for the specified amount of time before\\n running _perform_refresh.\\n \\\"\\\"\\\"\\n refresh_task: asyncio.Task\\n try:\\n logger.debug(f\\\"['{self._instance_connection_string}']: Entering sleep\\\")\\n if delay > 0:\\n await asyncio.sleep(delay)\\n refresh_task = self._loop.create_task(self._perform_refresh())\\n refresh_data = await refresh_task\\n except asyncio.CancelledError:\\n logger.debug(\\n f\\\"['{self._instance_connection_string}']: Schedule refresh task cancelled.\\\"\\n )\\n raise\\n # bad refresh attempt\\n except Exception as e:\\n logger.exception(\\n f\\\"['{self._instance_connection_string}']: \\\"\\n \\\"An error occurred while performing refresh. \\\"\\n \\\"Scheduling another refresh attempt immediately\\\",\\n exc_info=e,\\n )\\n # check if current metadata is invalid (expired),\\n # don't want to replace valid metadata with invalid refresh\\n if not await _is_valid(self._current):\\n self._current = refresh_task\\n # schedule new refresh attempt immediately\\n self._next = self._schedule_refresh(0)\\n raise\\n # if valid refresh, replace current with valid metadata and schedule next refresh\\n self._current = refresh_task\\n # calculate refresh delay based on certificate expiration\\n delay = _seconds_until_refresh(refresh_data.expiration)\\n self._next = self._schedule_refresh(delay)\\n\\n return refresh_data\\n\\n # schedule refresh task and return it\\n scheduled_task = self._loop.create_task(_refresh_task(self, delay))\\n return scheduled_task\",\n \"def async_update_stale(self, now: datetime) -> None:\\n for device in self.devices.values():\\n if (device.track and device.last_update_home) and device.stale(now):\\n self.hass.async_create_task(device.async_update_ha_state(True))\",\n \"def reload_when_connected(self, inst, poll_ms=500):\\n if inst.isConnected():\\n self.reload()\\n else:\\n self.after(poll_ms, utils.named_partial(self.reload_when_connected, inst, poll_ms))\",\n \"def _delay_update(now):\\n _LOGGER.debug(\\n \\\"%s Called delayed (%ssec) update\\\", self._name, self._delay\\n )\\n self.schedule_update_ha_state()\\n self._timer = None\",\n \"def config_dhcpv6_options(ssh_conn_obj, ztp_params, config_params, options=dict(), cli_type=\\\"\\\"):\\n cli_type = st.get_ui_type(config_params.dut, cli_type=cli_type)\\n retry_count = config_params.retry_count if \\\"retry_count\\\" in config_params and config_params.retry_count else 0\\n iteration = config_params.iteration if \\\"iteration\\\" in config_params and config_params.iteration else 300\\n delay = config_params.delay if \\\"delay\\\" in config_params and config_params.delay else 3\\n expect_reboot = True if \\\"expect_reboot\\\" in options and options [\\\"expect_reboot\\\"] else False\\n st.log(config_params)\\n if \\\"func_name\\\" in config_params:\\n syslog_file_names = [\\\"syslog_1_{}\\\".format(config_params.func_name), \\\"syslog_{}\\\".format(config_params.func_name)]\\n if \\\"json_content\\\" in config_params:\\n file_path = basic_obj.write_to_json_file(config_params.json_content)\\n st.log(file_path)\\n if file_path:\\n destination_path = \\\"{}{}/{}\\\".format(config_params.home_path, ztp_params.config_path, config_params.ztp_file)\\n st.log(destination_path)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n config_params.option_59_url = \\\"http://[{}]{}/{}\\\".format(config_params.static_ip, ztp_params.config_path, config_params.ztp_file)\\n config_params.search_pattern = r'\\\\s*option\\\\s+dhcp6.boot-file-url\\\\s+\\\"\\\\S+\\\";'\\n write_option_59_to_dhcp_server(ssh_conn_obj, config_params)\\n basic_obj.service_operations(ssh_conn_obj, config_params.dhcp6_service_name, \\\"restart\\\", \\\"server\\\")\\n if not verify_dhcpd_service_status(ssh_conn_obj, config_params.dhcpd6_pid):\\n st.log(\\\"{} service is running which is not expected\\\".format(config_params.dhcp6_service_name))\\n st.report_fail(\\\"service_running_not_expected\\\", config_params.dhcp6_service_name)\\n reboot_type = config_params.reboot_type if \\\"reboot_type\\\" in config_params and config_params.reboot_type else \\\"normal\\\"\\n if \\\"ztp_operation\\\" in config_params:\\n config_params.ztp_operation = \\\"reboot\\\" if cli_type == \\\"klish\\\" else config_params.ztp_operation\\n if config_params.ztp_operation == \\\"reboot\\\":\\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\\n elif config_params.ztp_operation == \\\"run\\\":\\n ztp_operations(config_params.dut, config_params.ztp_operation)\\n else:\\n st.log(\\\"ZTP operation is not mentioned hence rebooting the device ...\\\")\\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\\n if \\\"reboot_on_success\\\" in options and options[\\\"reboot_on_success\\\"]:\\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=options[\\\"reboot_on_success\\\"], cli_type=cli_type)\\n else:\\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\\n if not result:\\n if \\\"logs_path\\\" in config_params and \\\"func_name\\\" in config_params:\\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n if \\\"reboot_on_success\\\" in options and options[\\\"reboot_on_success\\\"]:\\n reboot_obj.config_reload(config_params.dut)\\n st.wait(5)\\n if not ip_obj.ping(config_params.dut, config_params.static_ip, family=\\\"ipv6\\\"):\\n st.log(\\\"Pinging to DHCP server failed from DUT, issue either with DUT or server\\\")\\n # intf_obj.enable_dhcp_on_interface(config_params.dut, config_params.network_port, \\\"v6\\\")\\n if not verify_ztp_status(config_params.dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in config_params and \\\"func_name\\\" in config_params:\\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n verify_ztp_filename_logs(config_params.dut, config_params)\\n if \\\"ztp_log_string\\\" in config_params and config_params.ztp_log_string:\\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path, config_params.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(config_params.ztp_log_path, config_params.ztp_log_string))\\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path_1, config_params.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(config_params.ztp_log_path_1, config_params.ztp_log_string))\\n st.report_fail(\\\"ztp_log_verification_failed\\\", config_params.ztp_log_path_1, config_params.ztp_log_string)\\n if \\\"result\\\" in config_params and config_params.result == \\\"pass\\\":\\n st.report_pass(\\\"test_case_passed\\\")\",\n \"def update(self):\\n self._state = get_local_ip()\",\n \"async def _refresh_task(self: Instance, delay: int) -> InstanceMetadata:\\n refresh_task: asyncio.Task\\n try:\\n logger.debug(f\\\"['{self._instance_connection_string}']: Entering sleep\\\")\\n if delay > 0:\\n await asyncio.sleep(delay)\\n refresh_task = self._loop.create_task(self._perform_refresh())\\n refresh_data = await refresh_task\\n except asyncio.CancelledError:\\n logger.debug(\\n f\\\"['{self._instance_connection_string}']: Schedule refresh task cancelled.\\\"\\n )\\n raise\\n # bad refresh attempt\\n except Exception as e:\\n logger.exception(\\n f\\\"['{self._instance_connection_string}']: \\\"\\n \\\"An error occurred while performing refresh. \\\"\\n \\\"Scheduling another refresh attempt immediately\\\",\\n exc_info=e,\\n )\\n # check if current metadata is invalid (expired),\\n # don't want to replace valid metadata with invalid refresh\\n if not await _is_valid(self._current):\\n self._current = refresh_task\\n # schedule new refresh attempt immediately\\n self._next = self._schedule_refresh(0)\\n raise\\n # if valid refresh, replace current with valid metadata and schedule next refresh\\n self._current = refresh_task\\n # calculate refresh delay based on certificate expiration\\n delay = _seconds_until_refresh(refresh_data.expiration)\\n self._next = self._schedule_refresh(delay)\\n\\n return refresh_data\",\n \"async def async_turn_off(self):\\n await self.coordinator.data.turn_off(self.zone_id)\\n await self.coordinator.async_refresh()\",\n \"async def async_update_config(self, config: ConfigType) -> None:\\n self._config = config\\n # just in case min/max values changed\\n if self._current_value is None:\\n return\\n self._current_value = min(self._current_value, self._maximum)\\n self._current_value = max(self._current_value, self._minimum)\\n self.async_write_ha_state()\",\n \"async def _async_update_data(self) -> dict[str, str]:\\n try:\\n state = await self.device.get_state()\\n except JvcProjectorConnectError as err:\\n raise UpdateFailed(f\\\"Unable to connect to {self.device.host}\\\") from err\\n except JvcProjectorAuthError as err:\\n raise ConfigEntryAuthFailed(\\\"Password authentication failed\\\") from err\\n\\n old_interval = self.update_interval\\n\\n if state[const.POWER] != const.STANDBY:\\n self.update_interval = INTERVAL_FAST\\n else:\\n self.update_interval = INTERVAL_SLOW\\n\\n if self.update_interval != old_interval:\\n _LOGGER.debug(\\\"Changed update interval to %s\\\", self.update_interval)\\n\\n return state\",\n \"def _refresh_discovery(self):\\n if self.terminate_flag:\\n return\\n\\n self.devices = discover_drones(self.ip_range, self.skyserve_port)\\n time.sleep(self.refresh_interval / 1000)\\n self._refresh_discovery()\",\n \"async def async_turn_off(self):\\n path = \\\"/ip/firewall/nat\\\"\\n param = \\\".id\\\"\\n value = None\\n for uid in self._ctrl.data[\\\"nat\\\"]:\\n if (\\n self._ctrl.data[\\\"nat\\\"][uid][\\\"name\\\"]\\n == f\\\"{self._data['protocol']}:{self._data['dst-port']}\\\"\\n ):\\n value = self._ctrl.data[\\\"nat\\\"][uid][\\\".id\\\"]\\n\\n mod_param = \\\"disabled\\\"\\n mod_value = True\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n await self._ctrl.async_update()\",\n \"async def set_update_interval(call):\\n _LOGGER.debug(\\\"call %s\\\", call)\\n service_data = call.data\\n email = service_data.get(CONF_EMAIL, \\\"\\\")\\n\\n if len(hass.config_entries.async_entries(DOMAIN)) > 1 and not email:\\n raise ValueError(\\\"Email address missing\\\")\\n controller: Controller = None\\n for entry in hass.config_entries.async_entries(DOMAIN):\\n if (\\n len(hass.config_entries.async_entries(DOMAIN)) > 1\\n and entry.title != email\\n ):\\n continue\\n controller = hass.data[DOMAIN].get(entry.entry_id)[\\\"coordinator\\\"].controller\\n if controller is None:\\n raise ValueError(f\\\"No Tesla controllers found for email {email}\\\")\\n\\n vin = service_data.get(ATTR_VIN, \\\"\\\")\\n update_interval = service_data.get(CONF_SCAN_INTERVAL, DEFAULT_SCAN_INTERVAL)\\n _LOGGER.debug(\\n \\\"Service %s called with email: %s vin %s interval %s\\\",\\n SERVICE_SCAN_INTERVAL,\\n email,\\n vin,\\n update_interval,\\n )\\n old_update_interval = controller.get_update_interval_vin(vin=vin)\\n if old_update_interval != update_interval:\\n _LOGGER.debug(\\n \\\"Changing update_interval from %s to %s for %s\\\",\\n old_update_interval,\\n update_interval,\\n vin,\\n )\\n controller.set_update_interval_vin(vin=vin, value=update_interval)\\n return True\",\n \"def configure_lldp_reinit(device, timer):\\r\\n try:\\r\\n device.configure(f'lldp reinit {timer}')\\r\\n except SubCommandFailure as e:\\r\\n raise SubCommandFailure(\\r\\n \\\"Could not configure LLDP reinit\\\"\\r\\n \\\"Error: {error}\\\".format(error=e)\\r\\n )\",\n \"def stop_dhcp_response(self, mac):\\n self.change_dhcp_response_time(mac, -1)\",\n \"def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\\\"\\\"):\\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\\n cli_type = \\\"klish\\\" if cli_type in [\\\"rest-put\\\", \\\"rest-patch\\\"] else cli_type\\n retry_count = data.retry_count if \\\"retry_count\\\" in data and data.retry_count else 0\\n iteration = data.iteration if \\\"iteration\\\" in data and data.iteration else 300\\n delay = data.delay if \\\"delay\\\" in data and data.delay else 3\\n if \\\"func_name\\\" in data:\\n syslog_file_names = [\\\"syslog_1_{}\\\".format(data.func_name), \\\"syslog_{}\\\".format(data.func_name)]\\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\\n if \\\"config_file_type\\\" in data and data.config_file_type == \\\"text\\\":\\n file_path = \\\"/tmp/file_temp.json\\\"\\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\\\"server\\\")\\n elif \\\"config_file_type\\\" in data and data.config_file_type == \\\"EoL\\\":\\n file_path = \\\"\\\"\\n else:\\n file_path = basic_obj.write_to_json_file(data.json_content)\\n if file_path:\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"config_db_location\\\" in data and data.config_db_location == \\\"json\\\":\\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"scenario\\\" in data and data.scenario == \\\"invalid-json\\\":\\n st.log(\\\"Writing invalid content to make invalid json ...\\\")\\n basic_obj.write_to_file_to_line(ssh_conn_obj, \\\",\\\", 5, destination_path, \\\"server\\\")\\n if data.option_type == \\\"67\\\":\\n st.log(\\\"Creating {} file on DHCP server ...\\\".format(data.config_file))\\n data.search_pattern = r'\\\\s*option\\\\s+bootfile-name\\\\s*\\\\S*\\\\s*\\\"\\\\S+\\\";'\\n data.option_string = \\\"option bootfile-name\\\"\\n if data.type == \\\"http\\\":\\n data.option_url = \\\"http://{}{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"tftp\\\":\\n data.option_url = \\\"tftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"ftp\\\":\\n data.option_url = \\\"ftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n write_option_to_dhcp_server(ssh_conn_obj, data)\\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\\n st.log(\\\"{} service not running\\\".format(data.dhcp_service_name))\\n st.report_fail(\\\"service_not_running\\\", data.dhcp_service_name)\\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\\n data.device_action = \\\"reboot\\\" if cli_type == \\\"klish\\\" else data.device_action\\n if data.device_action == \\\"reboot\\\":\\n reboot_type = data.reboot_type if \\\"reboot_type\\\" in data and data.reboot_type else \\\"normal\\\"\\n basic_obj.remove_file(dut, data.config_db_path)\\n st.reboot(dut, reboot_type, skip_port_wait=True)\\n st.wait_system_status(dut, 500)\\n elif data.device_action == \\\"run\\\":\\n ztp_operations(dut, data.device_action)\\n if \\\"band_type\\\" in data and data.band_type==\\\"inband\\\":\\n if not basic_obj.poll_for_system_status(dut):\\n st.log(\\\"Sytem is not ready ..\\\")\\n st.report_env_fail(\\\"system_not_ready\\\")\\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\\\"up\\\"):\\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \\\"down\\\")\\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \\\"up\\\")\\n if interface_status is not None:\\n if not interface_status:\\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\\n if \\\"service\\\" in data and data.service == \\\"disable\\\":\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"stop\\\")\\n if basic_obj.verify_service_status(dut, \\\"ztp\\\"):\\n st.log(\\\"ZTP status is not stopped\\\")\\n st.report_fail(\\\"service_not_stopped\\\", \\\"ztp\\\")\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"start\\\")\\n if not poll_ztp_status(dut, [\\\"IN-PROGRESS\\\", \\\"Not Started\\\", \\\"SUCCESS\\\"], cli_type=cli_type):\\n st.report_fail(\\\"ztp_max_polling_interval\\\")\\n if \\\"check\\\" in data and data.check == \\\"not\\\":\\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n else:\\n st.log(\\\"Iteration count {}\\\".format(iteration))\\n st.log(\\\"REBOOT ON SUCCESS - {}\\\".format(reboot_on_success))\\n if reboot_on_success:\\n if \\\"configdb-json\\\" in reboot_on_success:\\n st.wait_system_reboot(dut)\\n st.wait_system_status(dut, 300)\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\\n else:\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\\n if not result:\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n if reboot_on_success:\\n output = show_ztp_status(dut, cli_type=cli_type)\\n if output[\\\"status\\\"] != \\\"SUCCESS\\\":\\n st.wait(300, \\\"Waiting for device to reboot after success...\\\")\\n st.wait_system_status(dut, 300)\\n # st.wait_system_reboot(dut)\\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n st.banner(boot_up_obj.sonic_installer_list(dut))\\n verify_ztp_filename_logs(dut, data)\\n if \\\"ztp_log_string\\\" in data and data.ztp_log_string:\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path, data.ztp_log_string))\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path_1, data.ztp_log_string))\\n st.report_fail(\\\"ztp_log_verification_failed\\\", data.ztp_log_path_1, data.ztp_log_string)\\n if \\\"result\\\" in data and data.result == \\\"pass\\\":\\n st.report_pass(\\\"test_case_passed\\\")\",\n \"def sync() -> None:\",\n \"def update_isolation(self, time: int):\",\n \"def _update(self, host):\\n pass\",\n \"def task_update(self):\\n try:\\n self.task_stop()\\n except:\\n pass\\n self.update()\\n self.task_start()\",\n \"def activate(self):\\n super().activate()\\n self._change_lease_time(self.runner.config.get(\\\"dhcp_lease_time\\\"))\\n self._scan_finalize()\",\n \"async def async_update(self):\\n _LOGGER.debug(\\\"%s: updating statistics\\\", self.entity_id)\\n if self._samples_max_age is not None:\\n self._purge_old()\\n\\n self._update_characteristics()\\n\\n # If max_age is set, ensure to update again after the defined interval.\\n next_to_purge_timestamp = self._next_to_purge_timestamp()\\n if next_to_purge_timestamp:\\n _LOGGER.debug(\\n \\\"%s: scheduling update at %s\\\", self.entity_id, next_to_purge_timestamp\\n )\\n if self._update_listener:\\n self._update_listener()\\n self._update_listener = None\\n\\n @callback\\n def _scheduled_update(now):\\n \\\"\\\"\\\"Timer callback for sensor update.\\\"\\\"\\\"\\n _LOGGER.debug(\\\"%s: executing scheduled update\\\", self.entity_id)\\n self.async_schedule_update_ha_state(True)\\n self._update_listener = None\\n\\n self._update_listener = async_track_point_in_utc_time(\\n self.hass, _scheduled_update, next_to_purge_timestamp\\n )\",\n \"def Restart(self, udp=False):\\n self.Stop()\\n self.Start(udp)\",\n \"def reload_ini(self):\\n while True:\\n\\n if round(os.path.getmtime(self.config_file)) > self.config_last_modified:\\n print('Config Changes Detected, Reloading .ini File')\\n config = configparser.ConfigParser()\\n config.read(self.config_file)\\n self._set_ini_options(config)\\n self.config_last_modified = round(os.path.getmtime(self.config_file))\\n\\n time.sleep(3)\",\n \"def periodic_flavor_sync(ctx, driver, scg_id_list):\\n sync_interval = CONF.powervc.flavor_sync_interval\\n\\n if sync_interval is None or sync_interval == 0:\\n return\\n\\n def flavors_sync(driver, scg_id_list):\\n FlavorSync(driver, scg_id_list).synchronize_flavors(ctx)\\n LOG.debug('Flavors synchronization completed')\\n\\n sync_flavors = loopingcall.FixedIntervalLoopingCall(flavors_sync,\\n driver,\\n scg_id_list)\\n sync_flavors.start(interval=sync_interval, initial_delay=sync_interval)\",\n \"def run(self):\\n try:\\n rcvdConfig = self.conn.recv(self.size)\\n if self.verbosity >= 1:\\n print('%s: Received request from client %s:%s.' % (self.feederName, self.addr, self.port))\\n except socket.error as e:\\n raise Error(e.strerror)\\n self.lockConfig.acquire()\\n self.config.newConfig = json.loads(rcvdConfig)\\n self.config.updateConfig()\\n self.lockConfig.release()\\n self.conn.close()\",\n \"def _async_update_humidity(self, state):\\n try:\\n self.ccs811_client.set_humidity(float(state.state))\\n except ValueError as ex:\\n _LOGGER.error(\\\"Unable to update from sensor: %s\\\", ex)\",\n \"async def test_aiodiscover_finds_new_hosts_after_interval(hass: HomeAssistant) -> None:\\n with patch.object(hass.config_entries.flow, \\\"async_init\\\") as mock_init, patch(\\n \\\"homeassistant.components.dhcp.DiscoverHosts.async_discover\\\",\\n return_value=[],\\n ):\\n device_tracker_watcher = dhcp.NetworkWatcher(\\n hass,\\n {},\\n [{\\\"domain\\\": \\\"mock-domain\\\", \\\"hostname\\\": \\\"connect\\\", \\\"macaddress\\\": \\\"B8B7F1*\\\"}],\\n )\\n await device_tracker_watcher.async_start()\\n await hass.async_block_till_done()\\n\\n assert len(mock_init.mock_calls) == 0\\n\\n with patch.object(hass.config_entries.flow, \\\"async_init\\\") as mock_init, patch(\\n \\\"homeassistant.components.dhcp.DiscoverHosts.async_discover\\\",\\n return_value=[\\n {\\n dhcp.DISCOVERY_IP_ADDRESS: \\\"192.168.210.56\\\",\\n dhcp.DISCOVERY_HOSTNAME: \\\"connect\\\",\\n dhcp.DISCOVERY_MAC_ADDRESS: \\\"b8b7f16db533\\\",\\n }\\n ],\\n ):\\n async_fire_time_changed(hass, dt_util.utcnow() + datetime.timedelta(minutes=65))\\n await hass.async_block_till_done()\\n await device_tracker_watcher.async_stop()\\n await hass.async_block_till_done()\\n\\n assert len(mock_init.mock_calls) == 1\\n assert mock_init.mock_calls[0][1][0] == \\\"mock-domain\\\"\\n assert mock_init.mock_calls[0][2][\\\"context\\\"] == {\\n \\\"source\\\": config_entries.SOURCE_DHCP\\n }\\n assert mock_init.mock_calls[0][2][\\\"data\\\"] == dhcp.DhcpServiceInfo(\\n ip=\\\"192.168.210.56\\\",\\n hostname=\\\"connect\\\",\\n macaddress=\\\"b8b7f16db533\\\",\\n )\",\n \"def test_unassign_configuration_after_patch(self):\\n instance_info.dbaas.instances.update(instance_info.id,\\n remove_configuration=True)\\n assert_equal(202, instance_info.dbaas.last_http_code)\\n instance = instance_info.dbaas.instances.get(instance_info.id)\\n assert_equal('RESTART_REQUIRED', instance.status)\\n # restart to be sure configuration has been unassigned\\n instance_info.dbaas.instances.restart(instance_info.id)\\n assert_equal(202, instance_info.dbaas.last_http_code)\\n sleep(2)\\n\\n def result_is_active():\\n instance = instance_info.dbaas.instances.get(\\n instance_info.id)\\n if instance.status in CONFIG.running_status:\\n return True\\n else:\\n assert_equal(\\\"REBOOT\\\", instance.status)\\n return False\\n\\n poll_until(result_is_active)\\n result = instance_info.dbaas.configurations.get(configuration_info.id)\\n assert_equal(result.instance_count, 0)\",\n \"async def _async_complete_schedule_update(self, _):\\n _LOGGER.debug(\\\"Processing scheduled update for %s\\\", self.name)\\n self._scheduled_transition_update = None\\n await self._async_force_refresh_state()\",\n \"def sweep_relay():\",\n \"async def async_turn_off(self, **kwargs: Any) -> None:\\n await self.async_publish(\\n self._config[CONF_COMMAND_TOPIC],\\n self._config[CONF_PAYLOAD_OFF],\\n self._config[CONF_QOS],\\n self._config[CONF_RETAIN],\\n self._config[CONF_ENCODING],\\n )\\n if self._optimistic:\\n # Optimistically assume that switch has changed state.\\n self._attr_is_on = False\\n self.async_write_ha_state()\",\n \"def _post_task_update_advertise_address():\\n default_network_interface = None\\n\\n with open(KUBE_APISERVER_CONFIG) as f:\\n lines = f.read()\\n m = re.search(REGEXPR_ADVERTISE_ADDRESS, lines)\\n if m:\\n default_network_interface = m.group(1)\\n LOG.debug(' default_network_interface = %s', default_network_interface)\\n\\n if advertise_address and default_network_interface \\\\\\n and advertise_address != default_network_interface:\\n cmd = [\\\"sed\\\", \\\"-i\\\", \\\"/oidc-issuer-url/! s/{}/{}/g\\\".format(default_network_interface, advertise_address),\\n KUBE_APISERVER_CONFIG]\\n _ = _exec_cmd(cmd)\",\n \"def reset_time_out(self):\\n self.reconnect()\\n self.reconnect_params()\",\n \"def _wakeup(self, wakeup_timeout=10, response_timeout=3):\\n pass\",\n \"def SetAutoReconnect(self, value):\\n print 'setting automatically reconnect when connection drops'\\n config = ConfigParser.ConfigParser()\\n config.read(self.app_conf)\\n config.set(\\\"Settings\\\", \\\"auto_reconnect\\\", misc.to_bool(value))\\n config.write(open(self.app_conf, \\\"w\\\"))\\n self.auto_reconnect = misc.to_bool(value)\",\n \"def restore_running_config(device, path, file, timeout=60):\\n try:\\n device.execute(\\n \\\"copy {path}{file} running-config replace\\\".format(path=path, file=file),\\n timeout=timeout\\n )\\n except SubCommandFailure as e:\\n raise SubCommandFailure(\\n \\\"Could not replace saved configuration on \\\"\\n \\\"device {device}\\\\nError: {e}\\\".format(device=device.name, e=str(e))\\n )\",\n \"def dhcp_agent_network_remove(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def dns_sync(self, args):\\r\\n dns = DNSManager(self.client)\\r\\n vsi = VSManager(self.client)\\r\\n\\r\\n vs_id = resolve_id(vsi.resolve_ids, args.get('<identifier>'), 'VS')\\r\\n instance = vsi.get_instance(vs_id)\\r\\n zone_id = resolve_id(dns.resolve_ids, instance['domain'], name='zone')\\r\\n\\r\\n def sync_a_record():\\r\\n \\\"\\\"\\\" Sync A record \\\"\\\"\\\"\\r\\n records = dns.get_records(\\r\\n zone_id,\\r\\n host=instance['hostname'],\\r\\n )\\r\\n\\r\\n if not records:\\r\\n # don't have a record, lets add one to the base zone\\r\\n dns.create_record(\\r\\n zone['id'],\\r\\n instance['hostname'],\\r\\n 'a',\\r\\n instance['primaryIpAddress'],\\r\\n ttl=args['--ttl'])\\r\\n else:\\r\\n recs = [x for x in records if x['type'].lower() == 'a']\\r\\n if len(recs) != 1:\\r\\n raise CLIAbort(\\\"Aborting A record sync, found %d \\\"\\r\\n \\\"A record exists!\\\" % len(recs))\\r\\n rec = recs[0]\\r\\n rec['data'] = instance['primaryIpAddress']\\r\\n rec['ttl'] = args['--ttl']\\r\\n dns.edit_record(rec)\\r\\n\\r\\n def sync_ptr_record():\\r\\n \\\"\\\"\\\" Sync PTR record \\\"\\\"\\\"\\r\\n host_rec = instance['primaryIpAddress'].split('.')[-1]\\r\\n ptr_domains = self.client['Virtual_Guest'].\\\\\\r\\n getReverseDomainRecords(id=instance['id'])[0]\\r\\n edit_ptr = None\\r\\n for ptr in ptr_domains['resourceRecords']:\\r\\n if ptr['host'] == host_rec:\\r\\n ptr['ttl'] = args['--ttl']\\r\\n edit_ptr = ptr\\r\\n break\\r\\n\\r\\n if edit_ptr:\\r\\n edit_ptr['data'] = instance['fullyQualifiedDomainName']\\r\\n dns.edit_record(edit_ptr)\\r\\n else:\\r\\n dns.create_record(\\r\\n ptr_domains['id'],\\r\\n host_rec,\\r\\n 'ptr',\\r\\n instance['fullyQualifiedDomainName'],\\r\\n ttl=args['--ttl'])\\r\\n\\r\\n if not instance['primaryIpAddress']:\\r\\n raise CLIAbort('No primary IP address associated with this VS')\\r\\n\\r\\n zone = dns.get_zone(zone_id)\\r\\n\\r\\n go_for_it = args['--really'] or confirm(\\r\\n \\\"Attempt to update DNS records for %s\\\"\\r\\n % instance['fullyQualifiedDomainName'])\\r\\n\\r\\n if not go_for_it:\\r\\n raise CLIAbort(\\\"Aborting DNS sync\\\")\\r\\n\\r\\n both = False\\r\\n if not args['--ptr'] and not args['-a']:\\r\\n both = True\\r\\n\\r\\n if both or args['-a']:\\r\\n sync_a_record()\\r\\n\\r\\n if both or args['--ptr']:\\r\\n sync_ptr_record()\",\n \"def unconfigure_radius_server_deadtime(device, server_time):\\n try:\\n device.configure([\\n f\\\"no radius-server deadtime {server_time}\\\"\\n ])\\n except SubCommandFailure:\\n raise SubCommandFailure(\\n 'Could not unconfigure radius server deadtime'\\n )\",\n \"def reactivate(self) -> None:\\n self.display_until = None\\n self.save()\",\n \"def update(self,\\n dhcp_server_config_id,\\n dhcp_server_config,\\n ):\\n return self._invoke('update',\\n {\\n 'dhcp_server_config_id': dhcp_server_config_id,\\n 'dhcp_server_config': dhcp_server_config,\\n })\",\n \"def _async_update_callback(self):\\n self._async_update_zone_data()\\n self.async_write_ha_state()\",\n \"async def attempt_reconnect(self):\\n await deploy.reconnect()\",\n \"def syncloss(self):\\n # expect a disassociation indication with a correct status\\n assert(self.a.nxapi_disassociate_ind() == True)\\n \\n # generate a random frame\\n msdu = self.host.tx_msdu(da=self.ap1.macaddr, length=1000, prio=1)\\n \\n # wait for data send confirmation (not in the air)\\n self.a.host_send_data_cfm(msdu)\",\n \"def force_update(self):\\n self.update(self.poll())\",\n \"def configureDHCP():\\n dhcpStart = config.get(\\\"hotspot\\\", \\\"dhcpstart\\\")\\n dhcpEnd = config.get(\\\"hotspot\\\", \\\"dhcpend\\\")\\n dnsmasqConfig = f\\\"\\\"\\\"#PI Hotspot config\\ndomain-needed\\nbogus-priv\\ndhcp-option=option:dns-server\\ndhcp-authoritative\\ndhcp-range={dhcpStart},{dhcpEnd},1h\\n\\\"\\\"\\\"\\n confFile = open(\\\"/etc/dnsmasq.conf\\\", \\\"w\\\")\\n confFile.write(dnsmasqConfig)\\n confFile.close()\",\n \"def _mark_fresh(self):\\n if self._is_stale:\\n self._logger.debug(\\\"%s: transition to fresh\\\", self.ping_address)\\n self.on_fresh()\\n self._is_stale = False\",\n \"def pcp_reload_config(self, command_scope):\\n\\n\\t\\tif self.PCPConnectionStatus() != ConnStateType.OK:\\n\\t\\t\\tself.pcp_internal_error('invalid PCP connection')\\n\\t\\t\\treturn None\\n\\n\\t\\tself._PCPWrite('Z'.encode(), 1)\\n\\t\\twsize = self.int_to_bytes(4 + 1)\\n\\t\\tself._PCPWrite(wsize, 4)\\n\\t\\tself._PCPWrite(command_scope.encode(), 1)\\n\\t\\tif self.PCPFlush() < 0:\\n\\t\\t\\treturn None\\n\\t\\tif self.Pfdebug:\\n\\t\\t\\tself.Pfdebug.write(f'DEBUG: send: tos=\\\"Z\\\", length={self.bytes_to_int(wsize)}\\\\n')\\n\\n\\t\\treturn self._process_pcp_response('Z')\",\n \"async def async_update(self):\\n await self.coordinator.async_request_refresh()\",\n \"def _update_callback(self) -> None:\\n self.async_schedule_update_ha_state(force_refresh=True)\",\n \"def _update_callback(self) -> None:\\n self.async_schedule_update_ha_state(force_refresh=True)\",\n \"async def update_listener(hass, config_entry):\\n controller = hass.data[DOMAIN][config_entry.entry_id][\\\"coordinator\\\"].controller\\n old_update_interval = controller.update_interval\\n controller.update_interval = config_entry.options.get(CONF_SCAN_INTERVAL)\\n if old_update_interval != controller.update_interval:\\n _LOGGER.debug(\\n \\\"Changing scan_interval from %s to %s\\\",\\n old_update_interval,\\n controller.update_interval,\\n )\",\n \"def reload_config():\\n subprocess.run([SUPERVISOR_CMD, \\\"reload\\\"])\",\n \"async def async_update(self):\\n await self.wrapper.async_request_refresh()\",\n \"def spin(self):\\n rate = rospy.Rate(5) # hz\\n while not rospy.is_shutdown():\\n if self.parameters.charging:\\n self.dynamic_reconfigure_server.update_configuration({\\\"charging_percentage\\\": min(100, self.battery.percentage + self.charging_increment)})\\n else:\\n self.dynamic_reconfigure_server.update_configuration({\\\"charging_percentage\\\": max(0, self.battery.percentage - self.charging_increment)})\\n self.battery.header.stamp = rospy.get_rostime() # get_rostime() returns the time in rospy.Time structure\\n self.battery_publisher.publish(self.battery)\\n rate.sleep()\",\n \"async def async_update(self):\\n await self.hass.async_add_job(self.ccs811_client.update)\\n if self.type == SENSOR_ECO2:\\n eco2 = self.ccs811_client.eco2\\n self._state = eco2\\n elif self.type == SENSOR_TVOC:\\n self._state = self.ccs811_client.tvoc\",\n \"def update(self):\\n try:\\n response = requests.get('{}/digital/{}'.format(\\n self._resource, self._pin), timeout=10)\\n self.data = {'state': response.json()['return_value']}\\n except requests.exceptions.ConnectionError:\\n _LOGGER.error(\\\"No route to device '%s'. Is device offline?\\\",\\n self._resource)\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"async def async_update(self):\\n await self._coordinator.async_request_refresh()\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def sync(self):\\r\\n\\r\\n # Ensure to rerun only once to avoid infinite loops\\r\\n # caused by a constantly changing state value at each run.\\r\\n #\\r\\n # Example: state.value += 1\\r\\n if self._state[\\\"is_rerun\\\"]:\\r\\n self._state[\\\"is_rerun\\\"] = False\\r\\n\\r\\n elif self._state[\\\"hash\\\"] is not None:\\r\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\r\\n self._state[\\\"is_rerun\\\"] = True\\r\\n self._state[\\\"session\\\"].request_rerun()\\r\\n\\r\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"def update(self):\\n self.arest.update()\",\n \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops\\n # caused by a constantly changing state value at each run.\\n #\\n # Example: state.value += 1\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None)\",\n \"def sync(self):\\n\\n # Ensure to rerun only once to avoid infinite loops caused by a constantly changing state value at each run.\\n # Example: state.value += 1\\n\\n if self._state[\\\"is_rerun\\\"]:\\n self._state[\\\"is_rerun\\\"] = False\\n\\n elif self._state[\\\"hash\\\"] is not None:\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(\\n self._state[\\\"data\\\"], None\\n ):\\n self._state[\\\"is_rerun\\\"] = True\\n self._state[\\\"session\\\"].request_rerun()\\n\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"def set_check_interval (self, interval):\\n\\t\\tif self.__timeout:\\n\\t\\t\\tgobject.source_remove(self.__timeout)\\n\\t\\tself.__timeout = gobject.timeout_add(interval * 60000, \\n\\t\\t\\tself.run_mailcheck)\",\n \"async def async_update(self):\\n _LOGGER.debug(\\n \\\"SynoDSMSurveillanceHomeModeToggle.async_update(%s)\\\",\\n self._api.information.serial,\\n )\\n self._state = await self.hass.async_add_executor_job(\\n self._api.surveillance_station.get_home_mode_status\\n )\",\n \"def sync(self) -> None: #\\n self.__target.load_state_dict(self.__policy.state_dict())\",\n \"def sync(self):\\r\\n\\r\\n # Ensure to rerun only once to avoid infinite loops\\r\\n # caused by a constantly changing state value at each run.\\r\\n #\\r\\n # Example: state.value += 1\\r\\n if self._state[\\\"is_rerun\\\"]:\\r\\n self._state[\\\"is_rerun\\\"] = False\\r\\n \\r\\n elif self._state[\\\"hash\\\"] is not None:\\r\\n if self._state[\\\"hash\\\"] != self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None):\\r\\n self._state[\\\"is_rerun\\\"] = True\\r\\n self._state[\\\"session\\\"].request_rerun()\\r\\n\\r\\n self._state[\\\"hash\\\"] = self._state[\\\"hasher\\\"].to_bytes(self._state[\\\"data\\\"], None)\",\n \"def trigger(self):\\n # Update current state.\\n self.current_inst = self.future_inst\\n self.current_timer = self.future_timer\\n # Initialize future state.\\n self.future_inst = self.current_inst\\n self.future_timer = max(0, self.current_timer - 1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62378156","0.60955244","0.58129066","0.5434197","0.53986806","0.5379062","0.53275496","0.52896744","0.52710843","0.51772535","0.51282424","0.50909096","0.5082574","0.5062422","0.50369143","0.49950752","0.49852315","0.49442717","0.49381378","0.49372518","0.49296987","0.49037886","0.4880136","0.48729885","0.48588607","0.48480013","0.47872546","0.4776957","0.47482607","0.4746089","0.47434878","0.4736188","0.4714701","0.47048485","0.46971446","0.46913868","0.4691085","0.4689929","0.46870393","0.4657999","0.46515942","0.4650315","0.46378002","0.46357808","0.46330506","0.46327242","0.46257678","0.46251118","0.4619428","0.46114802","0.45972732","0.45795044","0.45758983","0.45730612","0.45729482","0.4572135","0.45698088","0.45649487","0.4560081","0.45537522","0.45522898","0.45497116","0.45484033","0.45476174","0.45398253","0.45393","0.45389014","0.4536722","0.4534701","0.45329803","0.45287943","0.4528605","0.4521889","0.45209873","0.4520623","0.45199296","0.4516268","0.4512883","0.45111242","0.4508836","0.4508836","0.45075205","0.45063117","0.450575","0.4505262","0.45036682","0.44977662","0.44914684","0.44914684","0.44914684","0.4491421","0.44789422","0.4475014","0.4472541","0.4470824","0.44693923","0.4464166","0.4463753","0.44630563","0.4459419"],"string":"[\n \"0.62378156\",\n \"0.60955244\",\n \"0.58129066\",\n \"0.5434197\",\n \"0.53986806\",\n \"0.5379062\",\n \"0.53275496\",\n \"0.52896744\",\n \"0.52710843\",\n \"0.51772535\",\n \"0.51282424\",\n \"0.50909096\",\n \"0.5082574\",\n \"0.5062422\",\n \"0.50369143\",\n \"0.49950752\",\n \"0.49852315\",\n \"0.49442717\",\n \"0.49381378\",\n \"0.49372518\",\n \"0.49296987\",\n \"0.49037886\",\n \"0.4880136\",\n \"0.48729885\",\n \"0.48588607\",\n \"0.48480013\",\n \"0.47872546\",\n \"0.4776957\",\n \"0.47482607\",\n \"0.4746089\",\n \"0.47434878\",\n \"0.4736188\",\n \"0.4714701\",\n \"0.47048485\",\n \"0.46971446\",\n \"0.46913868\",\n \"0.4691085\",\n \"0.4689929\",\n \"0.46870393\",\n \"0.4657999\",\n \"0.46515942\",\n \"0.4650315\",\n \"0.46378002\",\n \"0.46357808\",\n \"0.46330506\",\n \"0.46327242\",\n \"0.46257678\",\n \"0.46251118\",\n \"0.4619428\",\n \"0.46114802\",\n \"0.45972732\",\n \"0.45795044\",\n \"0.45758983\",\n \"0.45730612\",\n \"0.45729482\",\n \"0.4572135\",\n \"0.45698088\",\n \"0.45649487\",\n \"0.4560081\",\n \"0.45537522\",\n \"0.45522898\",\n \"0.45497116\",\n \"0.45484033\",\n \"0.45476174\",\n \"0.45398253\",\n \"0.45393\",\n \"0.45389014\",\n \"0.4536722\",\n \"0.4534701\",\n \"0.45329803\",\n \"0.45287943\",\n \"0.4528605\",\n \"0.4521889\",\n \"0.45209873\",\n \"0.4520623\",\n \"0.45199296\",\n \"0.4516268\",\n \"0.4512883\",\n \"0.45111242\",\n \"0.4508836\",\n \"0.4508836\",\n \"0.45075205\",\n \"0.45063117\",\n \"0.450575\",\n \"0.4505262\",\n \"0.45036682\",\n \"0.44977662\",\n \"0.44914684\",\n \"0.44914684\",\n \"0.44914684\",\n \"0.4491421\",\n \"0.44789422\",\n \"0.4475014\",\n \"0.4472541\",\n \"0.4470824\",\n \"0.44693923\",\n \"0.4464166\",\n \"0.4463753\",\n \"0.44630563\",\n \"0.4459419\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55289453"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":79786,"cells":{"query":{"kind":"string","value":"Spawn a thread to periodically resync the dhcp state."},"document":{"kind":"string","value":"def periodic_resync(self):\n eventlet.spawn(self._periodic_resync_helper)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run(self):\n self.sync_state()\n self.periodic_resync()\n self.lease_relay.start()\n self.notifications.run_dispatch(self)","def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n active_networks = set(self.plugin_rpc.get_active_networks())\n for deleted_id in known_networks - active_networks:\n self.disable_dhcp_helper(deleted_id)\n\n for network_id in active_networks:\n self.refresh_dhcp_helper(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Unable to sync network state.'))","def run(self):\n self.cncLock.acquire()\n self.running = True\n\n # Initialize\n try:\n self.cnc = serial.Serial(self.deviceFile,BAUD_RATE)\n\n self.updaterThread = threading.Thread(target=self.periodic_timer)\n self.updaterThread.start()\n\n # Wake up grbl\n log.info(\"Initializing Grbl...\")\n cmd = \"\\r\\n\\r\\n\"\n self.cnc.write(cmd.encode())\n\n # Wait for grbl to initialize and flush startup text in serial input\n time.sleep(2)\n self.cnc.flushInput()\n self.cncLock.release()\n\n while self.running :\n cmd = self.commandQueue.get().strip() + EOLStr\n if self.running == False:\n break\n self.cncLock.acquire()\n self.cnc.write(cmd.encode())\n\n out = str(self.cnc.readline().strip()) # Wait for grbl response\n if out.find('ok') >= 0 :\n log.debug(f'MSG: {out}') # Debug response\n elif out.find('error') >= 0 :\n log.error(f'ERROR: {out}')\n else:\n log.info(out)\n self.cncLock.release()\n except:\n raise\n finally:\n log.debug(\"CNC main loop left\")\n self.cnc.close()","def reconfigure(self):\n log.debug('Reconfiguring and restarting the DHCP daemon...')\n\n # Don't set the daemon running status here, but let the status\n # check take care of that.\n\n p = Properties(self.storage, CONFIG_SECTION)\n p.addCallback(self.changed).\\\n addCallback(lambda trigger: p.load()).\\\n addCallback(self.emit_config, p).\\\n addCallback(self.restart_daemon).\\\n addErrback(self.restart_error)","def _periodic_resync_helper(self):\n while True:\n eventlet.sleep(self.conf.resync_interval)\n if self.needs_resync:\n self.needs_resync = False\n self.sync_state()","def setup():\r\n thread = threading.Thread(target = backup)\r\n thread.start()","def renew_dhcp_lease(self):\n\t\tresponse = os.system(\"/sbin/dhclient -r;/sbin/dhclient\")\n\t\tif response != 0:\n\t\t\tprint \"Network restart failed. DHCP Lease failed.\"","def init_thread(self):\n logger.info('broadcast thread start with interval %ds (dpid=%s)', self.tbl.advertise_interval, dpid_to_str(self.dp.id))\n broadcast_thread = Thread(target=self.broadcast_thread)\n broadcast_thread.setDaemon(True)\n broadcast_thread.start()","def run():\n adapter = pygatt.GATTToolBackend()\n ADDRESS_TYPE = pygatt.BLEAddressType.random\n\n # TODO if a thread is killed then this will never reestablish a new one since connections never has elements removed\n while True:\n try:\n for device in adapter.scan():\n if device[\"name\"] == \"DYP Hydrometer\":\n print(\"NEW HYDROMETER FOUND\")\n t = threading.Thread(target=handle_connection, args=(device[\"address\"],))\n t.start()\n except:\n pass\n ptime.sleep(5)","def main(self):\n # initialize last update to startup interval if present, otherwise update interval\n options = self.options\n startup_interval = options.get('startup_interval', None)\n if startup_interval is not None:\n startup_interval = int(startup_interval)\n self.lastupdate = startup_interval\n polling_interval = int(options.get(\"polling_interval\", 0))\n self.state = options.get('state', None)\n # Use a timeout value of polling_interval (in secs) + 10 secs to wait for all threads to end.\n thread_timeout = (polling_interval * 60) + 10\n\n # Wait for threads to stop within thread timeout interval.\n stop_time = time.time() + thread_timeout\n while any(t.isAlive for t in self.threads) and (time.time() < stop_time):\n time.sleep(0.1)\n\n # Get rid of stopped threads from list.\n self.threads = [t for t in self.threads if t.is_alive()]\n\n if self.threads:\n # Polling threads still running raise runtime error.\n LOG.error(\"There were %d polling threads which did not stop within timeout period on restart\",\n len(self.threads))\n raise RuntimeError(\"There were {} polling threads which did not stop within timeout period on restart.\"\n .format(len(self.threads)))\n\n # Turn off 'stop_thread' flag.\n self.stop_thread = False\n\n if polling_interval > 0:\n # Create and start polling thread\n thread = Thread(target=self.polling_thread)\n self.threads.append(thread)\n thread.daemon = True\n thread.start()\n LOG.info(\"Polling for incidents in Proofpoint TRAP every %d minutes\", polling_interval)\n else:\n LOG.info(\"Polling for incidents in Proofpoint TRAP not enabled\")","def __init__(self, ifname):\n\n self._dbus_loop = gobject.MainLoop()\n self._bus = dbus.SystemBus()\n wait_bus_owner_timeout = 5 # Wait for 5s to have an owner for the bus name we are expecting\n logger.debug('Going to wait for an owner on bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n while not self._bus.name_has_owner(RemoteDhcpClientControl.DBUS_NAME):\n time.sleep(0.2)\n wait_bus_owner_timeout -= 0.2\n if wait_bus_owner_timeout <= 0: # We timeout without having an owner for the expected bus name\n raise Exception('No owner found for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n \n logger.debug('Got an owner for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n gobject.threads_init() # Allow the mainloop to run as an independent thread\n dbus.mainloop.glib.threads_init()\n \n dbus_object_name = RemoteDhcpClientControl.DBUS_OBJECT_ROOT + '/' + str(ifname)\n logger.debug('Going to communicate with object ' + dbus_object_name)\n self._dhcp_client_proxy = self._bus.get_object(RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE, dbus_object_name)\n self._dbus_iface = dbus.Interface(self._dhcp_client_proxy, RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE)\n \n logger.debug(\"Connected to D-Bus\")\n self._dhcp_client_proxy.connect_to_signal(\"IpConfigApplied\",\n self._handleIpConfigApplied,\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\n \n self._dhcp_client_proxy.connect_to_signal(\"LeaseLost\",\n self._handleLeaseLost,\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\n \n #Lionel: the following line is used for D-Bus debugging only\n #self._bus.add_signal_receiver(catchall_signal_handler, interface_keyword='dbus_interface', member_keyword='member')\n self._dbus_loop_thread = threading.Thread(target = self._loopHandleDbus) # Start handling D-Bus messages in a background thread\n self._dbus_loop_thread.setDaemon(True) # D-Bus loop should be forced to terminate when main program exits\n self._dbus_loop_thread.start()\n \n self._bus.watch_name_owner(RemoteDhcpClientControl.DBUS_NAME, self._handleBusOwnerChanged) # Install a callback to run when the bus owner changes\n \n self._callback_new_lease_mutex = threading.Lock() # This mutex protects writes to the _callback_new_lease attribute\n self._callback_new_lease = None\n \n self._exit_unlock_event = threading.Event() # Create a new threading event that will allow the exit() method to wait for the child to terminate properly\n self._getversion_unlock_event = threading.Event() # Create a new threading event that will allow the GetVersion() D-Bus call below to execute within a timed limit \n\n self.status = DhcpLeaseStatus.DhcpLeaseStatus()\n\n self._getversion_unlock_event.clear()\n self._remote_version = ''\n self._dbus_iface.GetVersion(reply_handler = self._getVersionUnlock, error_handler = self._getVersionError)\n if not self._getversion_unlock_event.wait(10): # We give 10s for slave to answer the GetVersion() request\n logfile = tempfile.NamedTemporaryFile(prefix='TimeoutOnGetVersion-', suffix='.log', delete=False)\n if logfile:\n print('Saving TimeoutOnGetVersion environment dump to file \"' + logfile.name + '\"', file=sys.stderr)\n print('TimeoutOnGetVersion', file=logfile)\n subprocess.call('ps -ef', stdout=logfile, shell=True)\n subprocess.call('perl ./dbus-introspect.pl --system com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1', stdout=logfile, shell=True)\n subprocess.call('dbus-send --system --type=method_call --print-reply --dest=com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1 com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary.GetVersion', stdout=logfile, shell=True)\n logfile.close()\n raise Exception('TimeoutOnGetVersion')\n else:\n logger.debug('Slave version: ' + self._remote_version)","def activate(self):\n super().activate()\n self._change_lease_time(self.runner.config.get(\"dhcp_lease_time\"))\n self._scan_finalize()","def interface_thread(file_name):\n # this thread will first wait a few seconds\n # and then run so that the file is read is\n # for initial data by the read_config_file func\n # the initial file is stored as string by read_config_file\n global INITIAL_CONFIG_FILE\n global READ_CONFIG_COMP\n\n if READ_CONFIG_COMP:\n start = current_time()\n while True:\n if (current_time() - start) < 30:\n time.sleep(5)\n continue\n else:\n file1 = open(file_name, \"r\")\n temp = file1.read()\n file1.close()\n if INITIAL_CONFIG_FILE == temp:\n start = current_time()\n continue\n else:\n list_1 = INITIAL_CONFIG_FILE.split(\"\\n\")\n list_2 = temp.split(\"\\n\")\n # first line that's no. of lines is never changed\n diff = [\n (ind, x[1])\n for ind, x in enumerate(zip(list_1, list_2))\n if x[0] != x[1]\n ]\n for ind, new_str in diff:\n # we use ind - 1 because list is unordered\n # and line no. is not included here hence\n # index is 1 less\n DATA[\"neighbor\"].pop(ind - 1)\n new_en = [x for x in new_str.split(\" \")]\n new_en[1] = float(new_en[1])\n new_en[2] = int(new_en[2])\n DATA[\"neighbor\"].insert(ind - 1, new_en)\n # inserting new base router data to n_d_vec\n DATA[\"n_d_vec\"][DATA[\"router_id\"]] = DATA[\"neighbor\"]\n INITIAL_CONFIG_FILE = temp\n start = current_time()","def run_forever(self, *args, **kwargs):\n try:\n self.logger.debug('Begin account update')\n\n # get account-updater server ownership\n self.get_ownership_obj = threading.Thread(target = self.msg.get_my_ownership)\n self.get_ownership_obj.setDaemon(True)\n self.get_ownership_obj.start()\n\n self.walker_obj = Walker(self.walker_map, self.__param, self.logger)\n self.walker_obj.setDaemon(True)\n self.walker_obj.start()\n self.logger.info(\"Walker Started\")\n self.reader_obj = Reader(self.walker_map, self.reader_map, \\\n self.__param, self.logger)\n self.reader_obj.setDaemon(True)\n self.reader_obj.start() \n self.logger.info(\"Reader Started\")\n self.account_sweeper = AccountSweep(self.__param, self.logger)\n self.account_sweeper.setDaemon(True)\n self.account_sweeper.start()\n self.logger.info(\"Account Sweeper Started\") \n self.updater_obj = Updater(self.walker_map, self.reader_map, \\\n self.__param, self.logger)\n self.updater_obj.setDaemon(True)\n self.updater_obj.start() \n self.logger.info(\"Updater Started\") \n self.container_sweeper = ContainerSweeper(self.walker_map, \\\n self.reader_map, self.__param, self.logger)\n self.container_sweeper.setDaemon(True)\n self.container_sweeper.start()\n self.logger.info(\"Container Sweeper Started\") \n\n account_updater_server = ThreadedAccountUpdaterServer(\\\n (self.__get_node_ip(gethostname()), \\\n self.__account_updater_port), HttpListener)\n account_updater_server.serve_forever()\n except Exception as ex:\n self.logger.error(\"Exception occured: %s\" % ex)","def run(self):\n self.running = True\n dynamic_reconfigure.client.Client(\"dynamic_reconfigure_server\", timeout=5, config_callback=self.dynamic_reconfigure_callback)\n rate = rospy.Rate(20)\n while not rospy.is_shutdown() and self.running:\n self.pub.publish(self.get_cmd_vel())\n rate.sleep()","def start(self):\n def f():\n if (self.started): return\n self.started = True\n with client.ServerProxy(self.host) as proxy:\n while (not self.req_shutdown):\n self.update_speed(proxy)\n time.sleep(self.com_freq)\n self.started = False\n self.req_shutdwon = False\n\n Thread(target=f).start()","def _StatusUpdateThreadMain(self):\n while self._status_update_active:\n self._UpdateStatus()\n time.sleep(self._status_update_interval)","def run(self):\n logger.debug(\"Starting a daemon thread to sync experiment states\")\n while self.thread_running.is_set():\n try:\n self.sync_experiment_state_with_ddb()\n except Exception as e:\n logger.warn(\"Exception occurred in Experiment Sync Thread: \" + str(e))\n logger.error(e)\n logger.warn(\"Resuming Sync in 10 seconds...\")\n time.sleep(10)\n time.sleep(0.5)","def heartbeat_thread_func(self):\n while True:\n heartbeat = json.dumps({\"type\": \"HEARTBEAT\",\n \"issued\": time.time() * 1000})\n log.info(\"Sending ACL heartbeat %s\" % heartbeat)\n self.pub_lock.acquire()\n self.pub_socket.send_multipart([\"aclheartbeat\",\n heartbeat.encode(\"utf-8\")])\n self.pub_lock.release()\n log.debug(\"Sent ACL heartbeat\")\n\n # In a perfect world this should subtract the time spent waiting\n # for the lock and sending the packet. For the moment, in normal\n # operation this will suffice.\n time.sleep(30)","def run(self):\n try:\n rcvdConfig = self.conn.recv(self.size)\n if self.verbosity >= 1:\n print('%s: Received request from client %s:%s.' % (self.feederName, self.addr, self.port))\n except socket.error as e:\n raise Error(e.strerror)\n self.lockConfig.acquire()\n self.config.newConfig = json.loads(rcvdConfig)\n self.config.updateConfig()\n self.lockConfig.release()\n self.conn.close()","def updateRunningConf(self, responsible):\n stop_command, start_command = map(\n self.gen_actionCommand, (\"stop\", \"start\")\n )\n\n #runCommandAsRootAndCheck raises in case of an error (perfect!)\n yield deferToThread(self.runCommandAsRootAndCheck, stop_command)\n\n ntpservers = self.CONFIG['ntpservers'].split(' ')\n responsible.feedback(\n tr(\"Reference servers: %(NTPSERVERS)s\"),\n NTPSERVERS=', '.join(ntpservers)\n )\n\n ha_status = None\n context = Context.fromComponent(self)\n if EDENWALL and self.core.hasComponent(context, 'ha'):\n try:\n ha_status = yield self.core.callService(context, 'ha', 'getHAMode')\n except Exception, err:\n self.error(exceptionAsUnicode(err))\n peers = _ntppeers(responsible, ha_status)\n\n template_variables = {\n 'ntpservers' : self.CONFIG['ntpservers'].split(' '),\n 'peers': peers,\n }\n self.generate_configfile(template_variables)\n\n yield deferToThread(self.runCommandAsRootAndCheck, start_command)","def send_state():\n while True:\n if I_AM_CRUSHED is False:\n sleep_time = random.randint(send_state_sec[0], send_state_sec[1])\n sock_check = socket.socket(type=socket.SOCK_DGRAM)\n sock_check.sendto(\"I'am healthy\", ('dispatcher', port_for_check))\n sock_check.close()\n time.sleep(sleep_time)","def sleep_for_cloudinit():\n run(\"sleep 15\")","def broadcast_thread(self):\n while True:\n try:\n logger.info('broadcast routing table (dpid=%s)', dpid_to_str(self.dp.id))\n for port_no, port in self.ports.items():\n if port.neighbor_switch_dpid:\n self.switches[port.neighbor_switch_dpid].add_to_queue((port, self.tbl))\n self.switches[port.neighbor_switch_dpid].trigger_update()\n time.sleep(self.tbl.advertise_interval)\n except:\n logger.info('broadcast thread of dpid=%s is killed', dpid_to_str(self.dp.id))\n break","def start_pull_thread(self):\r\n threading.Thread(target=self._pull_thread).start()","def idle_handler(self):\n if self.state == _STATE.INIT:\n self._activate()\n elif self.state == _STATE.ACTIVE:\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\n self.record_result('dhcp', state='run')\n self.dhcp_monitor.start()\n elif self.state == _STATE.BASE:\n self._base_start()","def dhcp_renew(ifname):\n\n logging.debug('Renewing %s DHCP lease...', ifname)\n\n try:\n subprocess.call(['dhcpcd', '--rebind', ifname])\n except OSError, err:\n if err.errno != errno.ENOENT:\n raise err\n\n try:\n call_timeout(['dhclient', '-1', ifname], timeout=5)\n except OSError, err:\n if err.errno == errno.ENOENT:\n logging.critical('Neither dhcpcd nor dhclient were found.')\n else:\n raise err","def _StartStatusUpdateThread(self):\n self._status_update_active = True\n self._status_update_thread = threading.Thread(\n name='Status update', target=self._StatusUpdateThreadMain)\n self._status_update_thread.start()","def refresh_dhcp_helper(self, network_id):\n old_network = self.cache.get_network_by_id(network_id)\n if not old_network:\n # DHCP current not running for network.\n return self.enable_dhcp_helper(network_id)\n\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\n\n if new_cidrs and old_cidrs == new_cidrs:\n self.call_driver('reload_allocations', network)\n self.cache.put(network)\n elif new_cidrs:\n if self.call_driver('restart', network):\n self.cache.put(network)\n else:\n self.disable_dhcp_helper(network.id)","def run(self):\n while self.time <= self.deadline:\n self.setup_period()\n self.execute_process(self.select_process())\n\n self.time += 1","def __init__(self, dt, ADDR=TIME_IP, PORT=TIME_PORT, interval=1, retry_connect=30):\n self.dt = dt\n self.ADDR = ADDR\n self.PORT = PORT\n self.retry_connect = retry_connect\n self.interval = interval\n self.tcpclient = TCPClient(self.dt, ADDR, PORT, interval, retry_connect=self.retry_connect)\n thread = threading.Thread(target=self.run, args=())\n thread.daemon = True\n thread.start()","async def async_setup_entry( # noqa: C901\n hass: HomeAssistant, entry: ConfigEntry\n) -> bool:\n websession = aiohttp_client.async_get_clientsession(hass)\n client = Client(session=websession)\n ip_address = entry.data[CONF_IP_ADDRESS]\n\n try:\n await client.load_local(\n ip_address,\n entry.data[CONF_PASSWORD],\n port=entry.data[CONF_PORT],\n use_ssl=entry.data.get(CONF_SSL, DEFAULT_SSL),\n )\n except RainMachineError as err:\n raise ConfigEntryNotReady from err\n\n # regenmaschine can load multiple controllers at once, but we only grab the one\n # we loaded above:\n controller = get_client_controller(client)\n\n entry_updates: dict[str, Any] = {}\n if not entry.unique_id or is_ip_address(entry.unique_id):\n # If the config entry doesn't already have a unique ID, set one:\n entry_updates[\"unique_id\"] = controller.mac\n\n if CONF_DEFAULT_ZONE_RUN_TIME in entry.data:\n # If a zone run time exists in the config entry's data, pop it and move it to\n # options:\n data = {**entry.data}\n entry_updates[\"data\"] = data\n entry_updates[\"options\"] = {\n **entry.options,\n CONF_DEFAULT_ZONE_RUN_TIME: data.pop(CONF_DEFAULT_ZONE_RUN_TIME),\n }\n if CONF_USE_APP_RUN_TIMES not in entry.options:\n entry_updates[\"options\"] = {**entry.options, CONF_USE_APP_RUN_TIMES: False}\n if entry_updates:\n hass.config_entries.async_update_entry(entry, **entry_updates)\n\n if entry.unique_id and controller.mac != entry.unique_id:\n # If the mac address of the device does not match the unique_id\n # of the config entry, it likely means the DHCP lease has expired\n # and the device has been assigned a new IP address. We need to\n # wait for the next discovery to find the device at its new address\n # and update the config entry so we do not mix up devices.\n raise ConfigEntryNotReady(\n f\"Unexpected device found at {ip_address}; expected {entry.unique_id}, \"\n f\"found {controller.mac}\"\n )\n\n async def async_update(api_category: str) -> dict:\n \"\"\"Update the appropriate API data based on a category.\"\"\"\n data: dict = {}\n\n try:\n if api_category == DATA_API_VERSIONS:\n data = await controller.api.versions()\n elif api_category == DATA_MACHINE_FIRMWARE_UPDATE_STATUS:\n data = await controller.machine.get_firmware_update_status()\n elif api_category == DATA_PROGRAMS:\n data = await controller.programs.all(include_inactive=True)\n elif api_category == DATA_PROVISION_SETTINGS:\n data = await controller.provisioning.settings()\n elif api_category == DATA_RESTRICTIONS_CURRENT:\n data = await controller.restrictions.current()\n elif api_category == DATA_RESTRICTIONS_UNIVERSAL:\n data = await controller.restrictions.universal()\n else:\n data = await controller.zones.all(details=True, include_inactive=True)\n except UnknownAPICallError:\n LOGGER.info(\n \"Skipping unsupported API call for controller %s: %s\",\n controller.name,\n api_category,\n )\n except RainMachineError as err:\n raise UpdateFailed(err) from err\n\n return data\n\n async def async_init_coordinator(\n coordinator: RainMachineDataUpdateCoordinator,\n ) -> None:\n \"\"\"Initialize a RainMachineDataUpdateCoordinator.\"\"\"\n await coordinator.async_initialize()\n await coordinator.async_config_entry_first_refresh()\n\n controller_init_tasks = []\n coordinators = {}\n for api_category, update_interval in COORDINATOR_UPDATE_INTERVAL_MAP.items():\n coordinator = coordinators[api_category] = RainMachineDataUpdateCoordinator(\n hass,\n entry=entry,\n name=f'{controller.name} (\"{api_category}\")',\n api_category=api_category,\n update_interval=update_interval,\n update_method=partial(async_update, api_category),\n )\n controller_init_tasks.append(async_init_coordinator(coordinator))\n\n await asyncio.gather(*controller_init_tasks)\n\n hass.data.setdefault(DOMAIN, {})\n hass.data[DOMAIN][entry.entry_id] = RainMachineData(\n controller=controller, coordinators=coordinators\n )\n\n await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)\n\n entry.async_on_unload(entry.add_update_listener(async_reload_entry))\n\n def call_with_controller(update_programs_and_zones: bool = True) -> Callable:\n \"\"\"Hydrate a service call with the appropriate controller.\"\"\"\n\n def decorator(func: Callable) -> Callable[..., Awaitable]:\n \"\"\"Define the decorator.\"\"\"\n\n @wraps(func)\n async def wrapper(call: ServiceCall) -> None:\n \"\"\"Wrap the service function.\"\"\"\n entry = async_get_entry_for_service_call(hass, call)\n data: RainMachineData = hass.data[DOMAIN][entry.entry_id]\n\n try:\n await func(call, data.controller)\n except RainMachineError as err:\n raise HomeAssistantError(\n f\"Error while executing {func.__name__}: {err}\"\n ) from err\n\n if update_programs_and_zones:\n await async_update_programs_and_zones(hass, entry)\n\n return wrapper\n\n return decorator\n\n @call_with_controller()\n async def async_pause_watering(call: ServiceCall, controller: Controller) -> None:\n \"\"\"Pause watering for a set number of seconds.\"\"\"\n await controller.watering.pause_all(call.data[CONF_SECONDS])\n\n @call_with_controller(update_programs_and_zones=False)\n async def async_push_flow_meter_data(\n call: ServiceCall, controller: Controller\n ) -> None:\n \"\"\"Push flow meter data to the device.\"\"\"\n value = call.data[CONF_VALUE]\n if units := call.data.get(CONF_UNIT_OF_MEASUREMENT):\n await controller.watering.post_flowmeter(value=value, units=units)\n else:\n await controller.watering.post_flowmeter(value=value)\n\n @call_with_controller(update_programs_and_zones=False)\n async def async_push_weather_data(\n call: ServiceCall, controller: Controller\n ) -> None:\n \"\"\"Push weather data to the device.\"\"\"\n await controller.parsers.post_data(\n {\n CONF_WEATHER: [\n {\n key: value\n for key, value in call.data.items()\n if key != CONF_DEVICE_ID\n }\n ]\n }\n )\n\n @call_with_controller()\n async def async_restrict_watering(\n call: ServiceCall, controller: Controller\n ) -> None:\n \"\"\"Restrict watering for a time period.\"\"\"\n duration = call.data[CONF_DURATION]\n await controller.restrictions.set_universal(\n {\n \"rainDelayStartTime\": round(as_timestamp(utcnow())),\n \"rainDelayDuration\": duration.total_seconds(),\n },\n )\n\n @call_with_controller()\n async def async_stop_all(call: ServiceCall, controller: Controller) -> None:\n \"\"\"Stop all watering.\"\"\"\n await controller.watering.stop_all()\n\n @call_with_controller()\n async def async_unpause_watering(call: ServiceCall, controller: Controller) -> None:\n \"\"\"Unpause watering.\"\"\"\n await controller.watering.unpause_all()\n\n @call_with_controller()\n async def async_unrestrict_watering(\n call: ServiceCall, controller: Controller\n ) -> None:\n \"\"\"Unrestrict watering.\"\"\"\n await controller.restrictions.set_universal(\n {\n \"rainDelayStartTime\": round(as_timestamp(utcnow())),\n \"rainDelayDuration\": 0,\n },\n )\n\n for service_name, schema, method in (\n (\n SERVICE_NAME_PAUSE_WATERING,\n SERVICE_PAUSE_WATERING_SCHEMA,\n async_pause_watering,\n ),\n (\n SERVICE_NAME_PUSH_FLOW_METER_DATA,\n SERVICE_PUSH_FLOW_METER_DATA_SCHEMA,\n async_push_flow_meter_data,\n ),\n (\n SERVICE_NAME_PUSH_WEATHER_DATA,\n SERVICE_PUSH_WEATHER_DATA_SCHEMA,\n async_push_weather_data,\n ),\n (\n SERVICE_NAME_RESTRICT_WATERING,\n SERVICE_RESTRICT_WATERING_SCHEMA,\n async_restrict_watering,\n ),\n (SERVICE_NAME_STOP_ALL, SERVICE_SCHEMA, async_stop_all),\n (SERVICE_NAME_UNPAUSE_WATERING, SERVICE_SCHEMA, async_unpause_watering),\n (\n SERVICE_NAME_UNRESTRICT_WATERING,\n SERVICE_SCHEMA,\n async_unrestrict_watering,\n ),\n ):\n if hass.services.has_service(DOMAIN, service_name):\n continue\n hass.services.async_register(DOMAIN, service_name, method, schema=schema)\n\n return True","def _delay_update(now):\n _LOGGER.debug(\n \"%s Called delayed (%ssec) update\", self._name, self._delay\n )\n self.schedule_update_ha_state()\n self._timer = None","def _RunDHCPCD(self, **kwargs):\n del kwargs\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\n interface=self.interface)\n # -K: Don't receive link messages for carrier status. You should\n # only have to use this with buggy device drivers or running\n # dhcpcd through a network manager.\n # -c: Location to the hooks file. If the default location happens to be\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\n timeout=self._dhcp_timeout,\n interface=self.interface)\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\n timeout=self._dhcp_timeout,\n cmd=dhcp_command)\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\n\n logging.info('Killing any existing dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n logging.info('Clearing any existing ifconfig networks...')\n self._device.Call(clear_ifconfig_command)\n\n logging.info('Starting dhcpcd...')\n self._device.CheckCall(dhcp_timeout_command)\n\n logging.info('Verifying IP address...')\n ip = self._LeasedIP()\n if not ip:\n self._device.Call(force_kill_command)\n raise WiFiError('DHCP bind failed')\n logging.info('Success: bound to IP %s', ip)\n\n yield ip # We have bound an IP; yield back to the caller.\n\n logging.info('Killing any remaining dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n yield # We have released the IP.","def check_daemon(self):\n log.debug(\"Checking for the DHCP daemon status.\")\n\n d = defer.Deferred()\n args = ( self.initscript, 'status' )\n env = dict(os.environ)\n\n process = reactor.spawnProcess(OutputGrabber(d), self.initscript,\n args=args, env=env)\n\n def try_restart(ign, status):\n self.script_running = False\n\n if not status:\n log.debug(\"Scheduling for immediate restart.\")\n self.schedule('restart', 0)\n else:\n log.debug(\"Scheduling the next status check.\")\n self.schedule('check', self.daemon_check_interval)\n\n def complete(output, timer):\n try:\n timer.cancel()\n except:\n pass\n\n # Use heuristics to determine whether the script succeeded\n # or not.\n running = output.find('is running') != -1\n\n return defer.succeed(running).\\\n addCallback(self.set_daemon_running).\\\n addCallback(try_restart, running)\n\n def abort(process, d):\n log.error(\"Script timeout while checking DHCP daemon status. \" + \n \"Killing the script.\")\n try:\n process.signalProcess(SIGKILL)\n except:\n log.exception(\"Unable to kill the script process.\")\n\n # Don't call errback here, but let the process die and\n # complete function above handle the error.\n\n #d.errback(Failure(RuntimeError('Init script timeout (check)')))\n\n def error(failure):\n log.exception(failure)\n\n return defer.succeed(False).\\\n addCallback(self.set_daemon_running).\\\n addCallback(try_restart, False)\n\n timer = reactor.callLater(self.timeout, abort, process, d)\n\n return d.addCallback(complete, timer).\\\n addErrback(error)","def _maintenance_thread():\n while True:\n _purge_expired_items()\n time.sleep(maintenance_sleep)\n\n return","def start_srv(start, process):\n if not \"conf_option\" in world.cfg:\n world.cfg[\"conf_option\"] = \"\"\n\n world.cfg['log_file'] = build_log_path()\n fabric_sudo_command('cat /dev/null >' + world.cfg['log_file'])\n world.cfg[\"dhcp_log_file\"] = world.cfg['log_file']\n\n log = \"local7\"\n if world.f_cfg.isc_dhcp_log_facility != \"\":\n log = world.f_cfg.isc_dhcp_log_facility\n\n world.cfg['log_facility'] = '''\\nlog-facility {log};\\n'''.format(**locals())\n\n add_defaults()\n cfg_write()\n log.debug(\"Start ISC-DHCP with generated config:\")\n convert_cfg_file(world.cfg[\"cfg_file\"])\n fabric_send_file(world.cfg[\"cfg_file\"] + '_processed', world.cfg[\"cfg_file\"] + '_processed')\n copy_configuration_file(world.cfg[\"cfg_file\"] + '_processed')\n remove_local_file(world.cfg[\"cfg_file\"])\n #set_ethernet_interface()\n stop_srv()\n\n world.cfg['leases'] = build_leases_path()\n\n #fabric_sudo_command('echo y |rm ' + world.cfg['leases'])\n fabric_sudo_command('touch ' + world.cfg['leases'])\n\n result = fabric_sudo_command('(' + os.path.join(world.f_cfg.software_install_path, 'sbin/dhcpd') + ' -cf server.cfg_processed'\n + ' -lf ' + world.cfg['leases']\n + '&); sleep ' + str(world.f_cfg.sleep_time_1) + ';')\n\n check_process_result(start, result, process)\n\n # clear configs in case we would like make couple configs in one test\n world.cfg[\"conf_time\"] = \"\"\n world.cfg[\"log_facility\"] = \"\"\n world.cfg[\"custom_lines\"] = \"\"\n world.cfg[\"conf_option\"] = \"\"\n world.cfg[\"conf_vendor\"] = \"\"","def run(self):\n\n self._daemon_thread.start()\n\n while True:\n time.sleep(5)","def work(self):\n self.config_file = self.args.config\n self.init_config()\n self.init_db()\n\n self.kickoff()","def _refresh_discovery(self):\n if self.terminate_flag:\n return\n\n self.devices = discover_drones(self.ip_range, self.skyserve_port)\n time.sleep(self.refresh_interval / 1000)\n self._refresh_discovery()","def run():\r\n event = threading.Event()\r\n while (event.is_set() == False):\r\n # perform database backup\r\n backup()\r\n\r\n # sleep for the predefined amount interval\r\n event.wait(BACKUP_INTERVAL)","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def ping_thread(state):\n while state.running:\n time.sleep(PING_FREQ)\n # for nick in users:\n # users[nick].queue.append(('SERVER', 'PING'))","def thread_function(client):\n threading.Timer(30.0, thread_function).start()\n client.publish(\"serverCommand/keepalive\", \"0\")\n print(\"Message Sent. (keepalive)\")","def periodic_flavor_sync(ctx, driver, scg_id_list):\n sync_interval = CONF.powervc.flavor_sync_interval\n\n if sync_interval is None or sync_interval == 0:\n return\n\n def flavors_sync(driver, scg_id_list):\n FlavorSync(driver, scg_id_list).synchronize_flavors(ctx)\n LOG.debug('Flavors synchronization completed')\n\n sync_flavors = loopingcall.FixedIntervalLoopingCall(flavors_sync,\n driver,\n scg_id_list)\n sync_flavors.start(interval=sync_interval, initial_delay=sync_interval)","def start(self):\n self.build_client_snapshot()\n self.load_local_dir_state()\n\n # Operations necessary to start the daemon\n self.create_observer()\n self.observer.start()\n self.sync_with_server()\n\n self.listener_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.listener_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.listener_socket.bind((self.cfg['cmd_address'], self.cfg['cmd_port']))\n self.listener_socket.listen(self.cfg['backlog_listener_sock'])\n r_list = [self.listener_socket]\n self.daemon_state = 'started'\n self.running = 1\n polling_counter = 0\n try:\n while self.running:\n r_ready, w_ready, e_ready = select.select(r_list, [], [], self.cfg['timeout_listener_sock'])\n\n for s in r_ready:\n\n if s == self.listener_socket:\n # handle the server socket\n client_socket, client_address = self.listener_socket.accept()\n r_list.append(client_socket)\n else:\n # handle all other sockets\n length = s.recv(Daemon.INT_SIZE)\n if length:\n # i need to do [0] and cast int because the struct.unpack return a tupla like (23234234,)\n # with the length as a string\n length = int(struct.unpack('!i', length)[0])\n message = json.loads(s.recv(length))\n for cmd, data in message.items():\n if cmd == 'shutdown':\n raise KeyboardInterrupt\n self.conn_mng.dispatch_request(cmd, data)\n else:\n s.close()\n r_list.remove(s)\n\n # synchronization polling\n # makes the polling every 3 seconds, so it waits six cycle (0.5 * 6 = 3 seconds)\n # maybe optimizable but now functional\n polling_counter += 1\n if polling_counter == 6:\n self.sync_with_server()\n polling_counter = 0\n\n except KeyboardInterrupt:\n self.stop(0)\n self.observer.stop()\n self.observer.join()\n self.listener_socket.close()","def create_daemon(self, handle, refresh_delay=5):\n self.handle = handle\n self.refresh_delay = refresh_delay\n self.thread = threading.Thread(name=\"Reddit Daemon\", target=self._keep_getting_new_messages)\n self.thread.daemon = True\n self.thread.start()","def start(self):\n\n if self.__bus_controller == None:\n print(\"can't start please pass me the needed dictionaries\")\n\n self.__global_messages = {\"kick reason\": \"\", \"free text\": \"\"}\n self.__lock_data = False\n self.__socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.stop = False\n __main_loop = threading.Thread(target=self.__main_loop, args=(), name=\"bus updater\")\n __main_loop.start()","def run(self):\n while True:\n if self.timeout - time() <= 0:\n if self.state in [State.CANDIDATE, State.FOLLOWER]:\n self.start_election()\n\n elif self.state is State.LEADER:\n self.send_appends()\n self.commit_entries()\n\n else:\n self.unknown_state()\n\n self.handle_message()","def configureDHCP():\n dhcpStart = config.get(\"hotspot\", \"dhcpstart\")\n dhcpEnd = config.get(\"hotspot\", \"dhcpend\")\n dnsmasqConfig = f\"\"\"#PI Hotspot config\ndomain-needed\nbogus-priv\ndhcp-option=option:dns-server\ndhcp-authoritative\ndhcp-range={dhcpStart},{dhcpEnd},1h\n\"\"\"\n confFile = open(\"/etc/dnsmasq.conf\", \"w\")\n confFile.write(dnsmasqConfig)\n confFile.close()","def run(self):\n while self.running:\n self.__update_battery()\n self.__update_signal()\n time.sleep(5)","def sync_dns(self,):\n\n for server_name, server_ip in self.get_instances():\n self.dnsmanager.ensure_a_record(server_name, server_ip)","def refresh():\n\n if _TRAFFICCTL:\n cmd = _traffic_ctl(\"config\", \"reload\")\n else:\n cmd = _traffic_line(\"-x\")\n\n return _subprocess(cmd)","def run(self):\n last_time = time.time()\n while self.running:\n now_time = time.time()\n interval = now_time - last_time\n last_time = now_time\n self.update(interval)\n time.sleep(Options['update interval'])","def main():\n\tlogger.warn(\"leprechaun rsync started.\")\n\n\ttime.sleep(1)\n\tts_rsync()\n\tsqlite_rsync()\n\n\twhile True:\n\t\ttry:\n\t\t\t# schedule crash guard:\n\t\t\timportlib.reload(schedule)\n\t\t\tschedule.every(1301).seconds.do(ts_rsync)\n\t\t\tschedule.every(300).seconds.do(sqlite_rsync)\n\t\t\twhile True:\n\t\t\t\ttry:\n\t\t\t\t\ttime.sleep(2)\n\t\t\t\t\tschedule.run_pending()\n\t\t\t\texcept Exception as ex:\n\t\t\t\t\tlogger.exception(ex)\n\t\t\t\t\ttime.sleep(2)\n\t\texcept Exception as ex:\n\t\t\tlogger.exception(ex)\n\t\t\ttime.sleep(2)","def run(self):\r\n while not self.shutdown_flag:\r\n try:\r\n # wait for connection\r\n if len(self.conns) == 0:\r\n self._wait()\r\n\r\n # receive message\r\n conn, (command, args) = self.recv()\r\n\r\n # process according to request\r\n if command == 'update_policy_network':\r\n self.agent.update_policy_network()\r\n elif command == 'get_action':\r\n action = self.agent.get_action(**args)\r\n self.send(conn, action)\r\n elif command == 'memorize':\r\n self.agent.memorize(**args)\r\n elif command == 'save_model':\r\n self.agent.save_model(**args)\r\n elif command == 'load_model':\r\n self.agent.load_model(**args)\r\n elif command == 'save_memory':\r\n self.agent.save_memory(**args)\r\n elif command == 'load_memory':\r\n self.agent.load_memory(**args)\r\n elif command == 'update_target_network':\r\n self.agent.update_target_network()\r\n elif command == 'detach':\r\n self.disconnect(conn)\r\n elif command == 'shutdown':\r\n self.shutdown_flag = True\r\n else:\r\n print('unknown command: {}'.format(command))\r\n except KeyboardInterrupt:\r\n print('interrupted')\r\n self.shutdown_flag = True\r\n break","def start(self):\n self.open()\n #t = Thread(target=self._cache_update, args=())\n #t.daemon = True\n #t.start()","def _inst_run(self):\r\n self._inst_get_img_info_from_db()\r\n print(\"run method: \", time.ctime())\r\n th = threading.Timer(\r\n 10,\r\n self._inst_run\r\n )\r\n th.start()","def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\"\"):\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\n cli_type = \"klish\" if cli_type in [\"rest-put\", \"rest-patch\"] else cli_type\n retry_count = data.retry_count if \"retry_count\" in data and data.retry_count else 0\n iteration = data.iteration if \"iteration\" in data and data.iteration else 300\n delay = data.delay if \"delay\" in data and data.delay else 3\n if \"func_name\" in data:\n syslog_file_names = [\"syslog_1_{}\".format(data.func_name), \"syslog_{}\".format(data.func_name)]\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\n if \"config_file_type\" in data and data.config_file_type == \"text\":\n file_path = \"/tmp/file_temp.json\"\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\"server\")\n elif \"config_file_type\" in data and data.config_file_type == \"EoL\":\n file_path = \"\"\n else:\n file_path = basic_obj.write_to_json_file(data.json_content)\n if file_path:\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"config_db_location\" in data and data.config_db_location == \"json\":\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\n destination_path = \"{}{}/{}\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n if \"scenario\" in data and data.scenario == \"invalid-json\":\n st.log(\"Writing invalid content to make invalid json ...\")\n basic_obj.write_to_file_to_line(ssh_conn_obj, \",\", 5, destination_path, \"server\")\n if data.option_type == \"67\":\n st.log(\"Creating {} file on DHCP server ...\".format(data.config_file))\n data.search_pattern = r'\\s*option\\s+bootfile-name\\s*\\S*\\s*\"\\S+\";'\n data.option_string = \"option bootfile-name\"\n if data.type == \"http\":\n data.option_url = \"http://{}{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"tftp\":\n data.option_url = \"tftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n elif data.type == \"ftp\":\n data.option_url = \"ftp://{}/{}/{}\".format(data.static_ip, data.config_path, data.config_file)\n write_option_to_dhcp_server(ssh_conn_obj, data)\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\n st.log(\"{} service not running\".format(data.dhcp_service_name))\n st.report_fail(\"service_not_running\", data.dhcp_service_name)\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\n data.device_action = \"reboot\" if cli_type == \"klish\" else data.device_action\n if data.device_action == \"reboot\":\n reboot_type = data.reboot_type if \"reboot_type\" in data and data.reboot_type else \"normal\"\n basic_obj.remove_file(dut, data.config_db_path)\n st.reboot(dut, reboot_type, skip_port_wait=True)\n st.wait_system_status(dut, 500)\n elif data.device_action == \"run\":\n ztp_operations(dut, data.device_action)\n if \"band_type\" in data and data.band_type==\"inband\":\n if not basic_obj.poll_for_system_status(dut):\n st.log(\"Sytem is not ready ..\")\n st.report_env_fail(\"system_not_ready\")\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\"up\"):\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \"down\")\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \"up\")\n if interface_status is not None:\n if not interface_status:\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\n if \"service\" in data and data.service == \"disable\":\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"stop\")\n if basic_obj.verify_service_status(dut, \"ztp\"):\n st.log(\"ZTP status is not stopped\")\n st.report_fail(\"service_not_stopped\", \"ztp\")\n basic_obj.service_operations_by_systemctl(dut, \"ztp\", \"start\")\n if not poll_ztp_status(dut, [\"IN-PROGRESS\", \"Not Started\", \"SUCCESS\"], cli_type=cli_type):\n st.report_fail(\"ztp_max_polling_interval\")\n if \"check\" in data and data.check == \"not\":\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n else:\n st.log(\"Iteration count {}\".format(iteration))\n st.log(\"REBOOT ON SUCCESS - {}\".format(reboot_on_success))\n if reboot_on_success:\n if \"configdb-json\" in reboot_on_success:\n st.wait_system_reboot(dut)\n st.wait_system_status(dut, 300)\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\n else:\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\n if not result:\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n if reboot_on_success:\n output = show_ztp_status(dut, cli_type=cli_type)\n if output[\"status\"] != \"SUCCESS\":\n st.wait(300, \"Waiting for device to reboot after success...\")\n st.wait_system_status(dut, 300)\n # st.wait_system_reboot(dut)\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in data and \"func_name\" in data:\n capture_syslogs(dut, data.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n st.banner(boot_up_obj.sonic_installer_list(dut))\n verify_ztp_filename_logs(dut, data)\n if \"ztp_log_string\" in data and data.ztp_log_string:\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path, data.ztp_log_string))\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(data.ztp_log_path_1, data.ztp_log_string))\n st.report_fail(\"ztp_log_verification_failed\", data.ztp_log_path_1, data.ztp_log_string)\n if \"result\" in data and data.result == \"pass\":\n st.report_pass(\"test_case_passed\")","def update_cmts_isc_dhcp_config(self, board_config):\n self.setup_dhcp_config(board_config)\n self.setup_dhcp6_config(board_config)\n raise NotImplementedError","def main():\n parser = ArgumentParser()\n parser.add_argument(\"router_id\", help=\"id of the router\")\n parser.add_argument(\"port_no\", help=\"port no. at which the\\\n router is listening\", type=int)\n parser.add_argument(\"router_config_file\", help=\"configuration\\\n file for the router\")\n args = parser.parse_args()\n DATA[\"port\"] = args.port_no\n DATA[\"router_id\"] = args.router_id\n read_config_file(args.router_config_file)\n\n # after the read_config_file func completes we set a variable\n # saying that neighbor data is ready\n global READ_CONFIG_COMP\n READ_CONFIG_COMP = True\n\n initial_dvec_and_forw_insert()\n\n SOCKET1.bind((\"\", DATA[\"port\"])) # converts the port to a listening state\n # print(DATA['neighbor'])\n\n # read thread is listening for incoming messages\n recv_th = Thread(target=recving)\n recv_th.start()\n\n # sending thread sends its distance vector to its direct neighbors\n send_th = Thread(target=sending)\n send_th.start()\n\n # link cost change interface thread\n intf_th = Thread(target=interface_thread, args=(\n args.router_config_file, ), daemon=True)\n intf_th.start()\n\n # find thread is checking if every router is available/alive\n find_th = Thread(target=check_if_alive, daemon=True)\n find_th.start()\n\n # we don't need thread.join because all of our threads\n # are non daemon threads\n\n # SOCKET1.close()\n return","def _dht_updater(self):\n while True:\n self.temp_previous = self.temp\n self.humi_previous = self.humi\n self.timelast = self.measurement_time\n\n # Download page sources\n sockt = urllib.urlopen('http://' + self.ip_address + '/temp')\n srct = sockt.read()\n sockt.close()\n # Set new measurement time\n self.measurement_time = dt.datetime.now()\n sockh = urllib.urlopen('http://' + self.ip_address + '/humidity')\n srch = sockh.read()\n sockh.close()\n\n # Split, strip strings and set values\n tempf = srct.split(' ', 1)[1].strip(' F')\n self.humi = int(srch.split(' ', 1)[1].strip('%'))\n self.temp = (float(tempf) - 32) / 1.8 # Convert temperature to celcius\n\n # Calculate change values\n self.temp_change = self.temp - self.temp_previous\n self.humi_change = self.humi - self.humi_previous\n\n # Flag change\n if (self.temp_change or self.humi_change) != 0:\n self.has_changed = True\n else:\n self.has_changed = False\n\n time.sleep(2)","def __init__(self, conf, logger=None):\n self.logger = logger or SimpleLogger(conf).get_logger_object()\n Daemon.__init__(self, conf, self.logger)\n\tlibraryUtils.OSDLoggerImpl(\"account-updater-monitoring\").initialize_logger()\n create_recovery_file('account-updater-server')\n self.conf = conf\n self.__interval = int(conf.get('interval', 1800))\n self.__ll_port = int(conf.get('llport', 61014))\n self.__account_updater_port = int(\\\n conf.get('account_updater_port', 61009))\n self.__service_id = gethostname() + \"_\" + str(self.__ll_port) + \\\n \"_account-updater-server\"\n self.__param = self.__get_param(conf)\n self.msg = GlobalVariables(self.logger)\n self.msg.set_service_id(self.__service_id)\n self.walker_map = WalkerMap()\n self.reader_map = ReaderMap()\n\n # Start sending health to local leader\n self.logger.info(\"Loading health monitoring library\")\n self.health_instance = healthMonitoring(self.__get_node_ip\\\n (gethostname()), self.__account_updater_port, \\\n self.__ll_port, self.__service_id)\n self.logger.info(\"Loaded health monitoring library\") \n remove_recovery_file('account-updater-server')\n\n\t# load global map\n if not self.msg.load_gl_map():\n sys.exit(130)\n self.logger.info(\"Account updater started\")","def update_worker():\n from test import get_remote_runner\n runner = get_remote_runner()\n runner.run(\"python2.7 /vagrant/bootstrap_lxc_manager.py --update_only=True\")","def run(self):\n self.stopped = False\n # receives incoming 'host up' requests\n serverSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)\n serverSocket.bind(('255.255.255.255', Globals.BROADCAST_PORT))\n \n # wait for UDP broadcast, send TCP ACK\n while 1:\n \n # open a socket and listen for a message\n value,address = serverSocket.recvfrom(256)\n host,port = address\n \n # this actually prevents a seg fault ;( for some reason\n if self.stopped:\n return\n \n if value == 'host up':\n \n sendSocket = socket.socket(socket.AF_INET, \n socket.SOCK_STREAM, 0)\n sendSocket.connect((host, Globals.ACK_PORT))\n sendSocket.send('host up ack')\n sendSocket.close()\n sendSocket = None\n self._addHost(host)\n \n elif value.find('host down') == 0:\n self._removeHost(host)\n \n elif value.find('add group') == 0:\n self._postEvent(value)\n \n elif value.find('remove group') == 0:\n self._postEvent(value)\n \n elif value.find('group beat') == 0:\n self._postEvent(value)\n \n serverSocket.close()","def start(interval: int, devices_list: list):\n t = threading.Thread(target=start_monitoring, args=(interval, devices_list))\n t.start()\n with lock:\n global running\n running = True","def dhcp_utilization(self, dhcp_utilization):\n\n self._dhcp_utilization = dhcp_utilization","def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\n self._ifname = ifname\n self._slave_dhcp_process = None\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\n self._new_lease_event = threading.Event() # At initialisation, event is cleared","def do_heartbeat(self):\n try:\n self.api.heartbeat(\n uuid=self.agent.get_node_uuid(),\n advertise_address=self.agent.advertise_address\n )\n self.error_delay = self.initial_delay\n self.log.info('heartbeat successful')\n except Exception:\n self.log.exception('error sending heartbeat')\n self.error_delay = min(self.error_delay * self.backoff_factor,\n self.max_delay)","def ScheduleUpdate(self):\r\n self.__fCachedFiles = None\r\n if self._timeout_id is not None:\r\n return\r\n app = wingapi.gApplication\r\n self._timeout_id = app.InstallTimeout(1000, self.__CB_DoUpdate)","def execute(timeSync):\r\n while True:\r\n runSched(timeSync)\r\n # Syncdb()\r","def dns_sync(self, args):\r\n dns = DNSManager(self.client)\r\n vsi = VSManager(self.client)\r\n\r\n vs_id = resolve_id(vsi.resolve_ids, args.get('<identifier>'), 'VS')\r\n instance = vsi.get_instance(vs_id)\r\n zone_id = resolve_id(dns.resolve_ids, instance['domain'], name='zone')\r\n\r\n def sync_a_record():\r\n \"\"\" Sync A record \"\"\"\r\n records = dns.get_records(\r\n zone_id,\r\n host=instance['hostname'],\r\n )\r\n\r\n if not records:\r\n # don't have a record, lets add one to the base zone\r\n dns.create_record(\r\n zone['id'],\r\n instance['hostname'],\r\n 'a',\r\n instance['primaryIpAddress'],\r\n ttl=args['--ttl'])\r\n else:\r\n recs = [x for x in records if x['type'].lower() == 'a']\r\n if len(recs) != 1:\r\n raise CLIAbort(\"Aborting A record sync, found %d \"\r\n \"A record exists!\" % len(recs))\r\n rec = recs[0]\r\n rec['data'] = instance['primaryIpAddress']\r\n rec['ttl'] = args['--ttl']\r\n dns.edit_record(rec)\r\n\r\n def sync_ptr_record():\r\n \"\"\" Sync PTR record \"\"\"\r\n host_rec = instance['primaryIpAddress'].split('.')[-1]\r\n ptr_domains = self.client['Virtual_Guest'].\\\r\n getReverseDomainRecords(id=instance['id'])[0]\r\n edit_ptr = None\r\n for ptr in ptr_domains['resourceRecords']:\r\n if ptr['host'] == host_rec:\r\n ptr['ttl'] = args['--ttl']\r\n edit_ptr = ptr\r\n break\r\n\r\n if edit_ptr:\r\n edit_ptr['data'] = instance['fullyQualifiedDomainName']\r\n dns.edit_record(edit_ptr)\r\n else:\r\n dns.create_record(\r\n ptr_domains['id'],\r\n host_rec,\r\n 'ptr',\r\n instance['fullyQualifiedDomainName'],\r\n ttl=args['--ttl'])\r\n\r\n if not instance['primaryIpAddress']:\r\n raise CLIAbort('No primary IP address associated with this VS')\r\n\r\n zone = dns.get_zone(zone_id)\r\n\r\n go_for_it = args['--really'] or confirm(\r\n \"Attempt to update DNS records for %s\"\r\n % instance['fullyQualifiedDomainName'])\r\n\r\n if not go_for_it:\r\n raise CLIAbort(\"Aborting DNS sync\")\r\n\r\n both = False\r\n if not args['--ptr'] and not args['-a']:\r\n both = True\r\n\r\n if both or args['-a']:\r\n sync_a_record()\r\n\r\n if both or args['--ptr']:\r\n sync_ptr_record()","def activate(self):\n super(Pfsense, self).activate()\n default_identifier = self.config.get('DEFAULT_IDENTIFIER_STR', '')\n if not default_identifier:\n self.log.warn('No default identifier set')\n\n self.default_identifier = self.build_identifier(default_identifier)\n self.thread = threading.Thread(target=log_thread, args=(self,))\n\n self.dns_cache = DNSCache()\n self.dns_cache.start()\n self.running = False","def exchange_timestamps_thread(other_uuid: str, other_ip: str, other_tcp_port: int):\n print_yellow(f\"ATTEMPTING TO CONNECT TO {other_uuid}\")\n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n client.connect((other_ip, other_tcp_port))\n data = client.recv(1024)\n except ConnectionError:\n print_red(\"connection refused\") \n exit()\n timestamp = struct.unpack(\"!d\", data)\n # calculate delay\n my_timestamp = datetime.datetime.utcnow().timestamp()\n delay = my_timestamp - timestamp[0]\n\n if other_uuid in neighbor_information:\n neighbor_information[other_uuid].delay = delay\n # neighbor_information[other_uuid].last_timestamp = timestamp[0]\n\n else:\n neighbour_node = NeighborInfo(delay, other_ip, other_tcp_port)\n neighbor_information.update({other_uuid: neighbour_node})\n\n client.close()","def run(self):\n \n # Loop through all checkers to do an initial state check\n for checker in self.checkers:\n checker.update_last_state()\n\n # Send initial heartbeat\n self._send_heartbeat()\n \n # Main loop\n while True: \n html = \"\"\n for checker in self.checkers:\n if checker.just_changed_state():\n log.warn(\"Checker {} has changed state.\"\n .format(checker.name))\n html += \"<li>\" + checker.html() + \"</li>\\n\"\n \n if isinstance(checker, Process) and checker.state() == FAIL:\n log.warn(\"Process {} is not running.\"\n .format(checker.name))\n html += (\"<li>Attempting to restart \" + \n escape(checker.name) + \"...</li>\\n\")\n try:\n checker.restart()\n except MaxRetriesError, e:\n self.shutdown_reason = str(e)\n return\n time.sleep(5)\n html += (\"<li>State after restart: \" + \n checker.html() + \"</li>\\n\")\n\n if html:\n html = \"<h2>STATE CHANGED:</h2>\\n<ul>\\n\" + html + \"</ul>\\n\" \n html += self.html()\n html += run_commands(self.state_change_cmds)\n self.send_email_with_time(html=html,\n subject=\"Babysitter detected\"\n \" state change.\")\n\n if self._need_to_send_heartbeat():\n self._send_heartbeat()\n\n # Check if a new data subdir has been created\n if self.base_data_dir and self.sub_data_dir:\n if self._find_last_numeric_subdir() != self.sub_data_dir:\n self._send_heartbeat(\"<p>New subdir found so about to restart \"\n \"babysitter. Below are the last stats \"\n \"for the old data subdirectory.</p>\\n\")\n raise NewDataDirError()\n \n time.sleep(UPDATE_PERIOD)","def monitor(self):\n if self.startup():\n time.sleep(0.250)\n self.run()","def run():\n logger.verbose(\"bwmon: Thread started\")\n while True:\n lock.wait()\n logger.verbose(\"bwmon: Event received. Running.\")\n database.db_lock.acquire()\n nmdbcopy = copy.deepcopy(database.db)\n database.db_lock.release()\n try:\n if getDefaults(nmdbcopy) and len(bwlimit.tc(\"class show dev %s\" % dev_default)) > 0:\n # class show to check if net:InitNodeLimit:bwlimit.init has run.\n sync(nmdbcopy)\n else: logger.log(\"bwmon: BW limits DISABLED.\")\n except: logger.log_exc(\"bwmon failed\")\n lock.clear()","def _async_update_callback(self):\n self._async_update_zone_data()\n self.async_write_ha_state()","def _run(self):\n #print(\"try to connect run\")\n while True:\n self._connect()\n while not self.connected and self.auto_retry is not None:\n gevent.sleep(self.auto_retry)\n self._connect()\n if self.connected:\n self.run()\n if self.auto_retry is None:\n break","def initial_sync_thread(self, engine_id):\n\n while True:\n # Catch exceptions so the thread does not die.\n try:\n eventlet.greenthread.sleep(SYNC_INTERVAL)\n self._initial_sync_subclouds(engine_id)\n except eventlet.greenlet.GreenletExit:\n # We have been told to exit\n return\n except Exception as e:\n LOG.exception(e)","def postStartup(self):\n daemon = component.getUtility(interfaces.ICollector)\n task = NmapPingTask(\n \"NmapPingTask\", \"NmapPingTask\", taskConfig=daemon._prefs\n )\n # introduce a small delay to can have a chance to load some config\n task.startDelay = 5\n daemon._scheduler.addTask(task)\n if not daemon.options.disableCorrelator:\n correlationBackend = daemon.options.correlationBackend\n task._correlate = component.getUtility(\n IPingTaskCorrelator, correlationBackend\n )\n task.disable_correlator = daemon.options.disableCorrelator","async def test_aiodiscover_finds_new_hosts_after_interval(hass: HomeAssistant) -> None:\n with patch.object(hass.config_entries.flow, \"async_init\") as mock_init, patch(\n \"homeassistant.components.dhcp.DiscoverHosts.async_discover\",\n return_value=[],\n ):\n device_tracker_watcher = dhcp.NetworkWatcher(\n hass,\n {},\n [{\"domain\": \"mock-domain\", \"hostname\": \"connect\", \"macaddress\": \"B8B7F1*\"}],\n )\n await device_tracker_watcher.async_start()\n await hass.async_block_till_done()\n\n assert len(mock_init.mock_calls) == 0\n\n with patch.object(hass.config_entries.flow, \"async_init\") as mock_init, patch(\n \"homeassistant.components.dhcp.DiscoverHosts.async_discover\",\n return_value=[\n {\n dhcp.DISCOVERY_IP_ADDRESS: \"192.168.210.56\",\n dhcp.DISCOVERY_HOSTNAME: \"connect\",\n dhcp.DISCOVERY_MAC_ADDRESS: \"b8b7f16db533\",\n }\n ],\n ):\n async_fire_time_changed(hass, dt_util.utcnow() + datetime.timedelta(minutes=65))\n await hass.async_block_till_done()\n await device_tracker_watcher.async_stop()\n await hass.async_block_till_done()\n\n assert len(mock_init.mock_calls) == 1\n assert mock_init.mock_calls[0][1][0] == \"mock-domain\"\n assert mock_init.mock_calls[0][2][\"context\"] == {\n \"source\": config_entries.SOURCE_DHCP\n }\n assert mock_init.mock_calls[0][2][\"data\"] == dhcp.DhcpServiceInfo(\n ip=\"192.168.210.56\",\n hostname=\"connect\",\n macaddress=\"b8b7f16db533\",\n )","def handleClient(self, connection, address):\r\n # time.sleep(5) #server Action\r\n while True:\r\n try:\r\n data = connection.recv(1024).decode(\"utf-8\")\r\n except:\r\n print('client disconnect: ', address, 'at', self.now())\r\n data = \"\"\r\n\r\n if not data: break\r\n\r\n data = self.change_host(data, address)\r\n result = self.manag_bd.dispatcher(data)\r\n\r\n mutex = thread.allocate_lock()\r\n\r\n\r\n if type(result)==type(list()):\r\n mutex.acquire() #Lock interrupt\r\n l = len(result)\r\n reply = str(l)\r\n connection.send(reply.encode(\"utf-8\"))\r\n for line in result:\r\n time.sleep(0.0025)\r\n reply = line\r\n connection.send(reply.encode(\"utf-8\"))\r\n mutex.release()# permission to interrupt\r\n else:\r\n reply = str(self.now())\r\n connection.send(reply.encode(\"utf-8\"))\r\n\r\n\r\n\r\n connection.close()","def sync():\n sync_ssda()","def on_enter(self):\n\n Clock.schedule_once(partial(check_connection.is_alive,\n self.ids[\"ico_connection\"]\n )\n )\n self.check_connection = Clock.schedule_interval(partial(check_connection.is_alive,\n self.ids[\"ico_connection\"]\n ),\n 5.0\n )","def start_daemon(interval=600):\n thread = threading.Thread(target=daemon_job, args=(interval, ))\n thread.daemon = True\n thread.start()","def __init__( self,\\\n targetip,\\\n gatewayip,\\\n name='arpoison'\n ):\n self._stopevent = threading.Event( )\n self._sleepperiod = 1.0\n\n threading.Thread.__init__(self, name=name)\n\n self.targetip = targetip\n self.gatewayip = gatewayip","def sync_loop(self):\n\n while True:\n print(\"Sync loop\")\n self.sync()\n time.sleep(SYNC_TIME_OUT)","def __init__(self, _log: logging.Logger, _config: dict = None, _devices_callback=None):\n Thread.__init__(self)\n self.command_lock = Lock()\n self.log = _log\n self.config = _config\n self.available_devices_callback = _devices_callback\n self.s_socket = None\n self.on_config_change = None\n self.is_running = False\n self.config_hash = dict()\n self.config_hash['version'] = self.__get_hashstr(self.config)\n self.config_hash['al_working'] = 'none'\n # self.is_socket_usable = True\n self.log.debug(\"instantiate RadioCommandServer...\")","def __work__(self):\n while not self.is_done:\n self.refreshSignal.emit()\n time.sleep(0.05)","async def _main(self):\n while True:\n time.sleep(1)","def ping_daemon(self):\n s = self.ping_interval\n while True:\n p = domintell.messages.Ping()\n self.send(p)\n time.sleep(s)","def run(self):\n while True:\n # Do something\n print('Doing something imporant in the background')\n\n self.loadData()\n time.sleep(self.interval)","def receive_broadcast_thread():\n global neighbor_information\n client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\n client.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n client.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n client.bind(('', get_broadcast_port()))\n\n while True:\n data, (ip, port) = client.recvfrom(4096)\n data_v2 = data.decode(\"utf-8\")\n parsed = data_v2.split(\" \")\n print(parsed)\n newnodeflag=checkIfsameNode(parsed)\n\n if(newnodeflag == 1):\n tcpClient=daemon_thread_builder(exchange_timestamps_thread,args=(parsed[0],\"127.0.0.1\",parsed[2],))\n tcpClient.start() \n tcpClient.join()\n elif(newnodeflag == 2):\n x = neighbor_information.get(parsed[0])\n neighbor_information.update({parsed[0]:NeighborInfo(x.delay,x.last_timestamp,x.broadcast_count+1)})\n if (x.broadcast_count == 9): \n neighbor_information.pop(parsed[0])\n\n print_blue(f\"RECV: {data} FROM: {ip}:{port}\")","def main():\n populate_satellites_array()\n latitude = float(os.environ['LATITUDE'])\n longitude = float(os.environ['LONGITUDE'])\n radius = int(os.environ['RADIUS'])\n timeout = 1\n previous_satellites = []\n while True:\n if (last_updated[0] + 86400) < int(time.time()):\n print('Expired data, updating from spacetrack')\n cron_refresh_spacetrack_cache()\n populate_satellites_array()\n print('Checking {}, {}'.format(latitude, longitude))\n currently_overhead = get_overhead_satellites_dicts(latitude, longitude, radius)\n for sat in currently_overhead:\n if not sat['name'] in previous_satellites:\n announce_satellite(sat)\n previous_satellites = [x['name'] for x in currently_overhead]\n time.sleep(timeout)","def start(self):\n gevent.spawn_later(self._period, self._run)","def start(self):\r\n t = Thread(target=self.update, args=())\r\n t.daemon = True\r\n t.start()\r\n return self","def start(self):\r\n t = Thread(target=self.update, args=())\r\n t.daemon = True\r\n t.start()\r\n return self","async def _autoconfigure_run(self):\n self.logger.info(\"cellular auto configuration\")\n try:\n # configure the SkyCtrl to use this apc token for the cellular connection.\n await self._aconfigure()\n except (HTTPError, TimeoutError, RuntimeError) as e:\n # raises an autoconfigure failure Event\n event = CellularAutoconfigureFailureEvent(exception=e)\n self.logger.info(str(event))\n self._controller.scheduler.process_event(event)","def backgroundStart(): #Background checks thread\n global currentStatus\n while True:\n currentStatus = checkClassChanges()\n time.sleep(10)"],"string":"[\n \"def run(self):\\n self.sync_state()\\n self.periodic_resync()\\n self.lease_relay.start()\\n self.notifications.run_dispatch(self)\",\n \"def sync_state(self):\\n LOG.info(_('Synchronizing state'))\\n known_networks = set(self.cache.get_network_ids())\\n\\n try:\\n active_networks = set(self.plugin_rpc.get_active_networks())\\n for deleted_id in known_networks - active_networks:\\n self.disable_dhcp_helper(deleted_id)\\n\\n for network_id in active_networks:\\n self.refresh_dhcp_helper(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Unable to sync network state.'))\",\n \"def run(self):\\n self.cncLock.acquire()\\n self.running = True\\n\\n # Initialize\\n try:\\n self.cnc = serial.Serial(self.deviceFile,BAUD_RATE)\\n\\n self.updaterThread = threading.Thread(target=self.periodic_timer)\\n self.updaterThread.start()\\n\\n # Wake up grbl\\n log.info(\\\"Initializing Grbl...\\\")\\n cmd = \\\"\\\\r\\\\n\\\\r\\\\n\\\"\\n self.cnc.write(cmd.encode())\\n\\n # Wait for grbl to initialize and flush startup text in serial input\\n time.sleep(2)\\n self.cnc.flushInput()\\n self.cncLock.release()\\n\\n while self.running :\\n cmd = self.commandQueue.get().strip() + EOLStr\\n if self.running == False:\\n break\\n self.cncLock.acquire()\\n self.cnc.write(cmd.encode())\\n\\n out = str(self.cnc.readline().strip()) # Wait for grbl response\\n if out.find('ok') >= 0 :\\n log.debug(f'MSG: {out}') # Debug response\\n elif out.find('error') >= 0 :\\n log.error(f'ERROR: {out}')\\n else:\\n log.info(out)\\n self.cncLock.release()\\n except:\\n raise\\n finally:\\n log.debug(\\\"CNC main loop left\\\")\\n self.cnc.close()\",\n \"def reconfigure(self):\\n log.debug('Reconfiguring and restarting the DHCP daemon...')\\n\\n # Don't set the daemon running status here, but let the status\\n # check take care of that.\\n\\n p = Properties(self.storage, CONFIG_SECTION)\\n p.addCallback(self.changed).\\\\\\n addCallback(lambda trigger: p.load()).\\\\\\n addCallback(self.emit_config, p).\\\\\\n addCallback(self.restart_daemon).\\\\\\n addErrback(self.restart_error)\",\n \"def _periodic_resync_helper(self):\\n while True:\\n eventlet.sleep(self.conf.resync_interval)\\n if self.needs_resync:\\n self.needs_resync = False\\n self.sync_state()\",\n \"def setup():\\r\\n thread = threading.Thread(target = backup)\\r\\n thread.start()\",\n \"def renew_dhcp_lease(self):\\n\\t\\tresponse = os.system(\\\"/sbin/dhclient -r;/sbin/dhclient\\\")\\n\\t\\tif response != 0:\\n\\t\\t\\tprint \\\"Network restart failed. DHCP Lease failed.\\\"\",\n \"def init_thread(self):\\n logger.info('broadcast thread start with interval %ds (dpid=%s)', self.tbl.advertise_interval, dpid_to_str(self.dp.id))\\n broadcast_thread = Thread(target=self.broadcast_thread)\\n broadcast_thread.setDaemon(True)\\n broadcast_thread.start()\",\n \"def run():\\n adapter = pygatt.GATTToolBackend()\\n ADDRESS_TYPE = pygatt.BLEAddressType.random\\n\\n # TODO if a thread is killed then this will never reestablish a new one since connections never has elements removed\\n while True:\\n try:\\n for device in adapter.scan():\\n if device[\\\"name\\\"] == \\\"DYP Hydrometer\\\":\\n print(\\\"NEW HYDROMETER FOUND\\\")\\n t = threading.Thread(target=handle_connection, args=(device[\\\"address\\\"],))\\n t.start()\\n except:\\n pass\\n ptime.sleep(5)\",\n \"def main(self):\\n # initialize last update to startup interval if present, otherwise update interval\\n options = self.options\\n startup_interval = options.get('startup_interval', None)\\n if startup_interval is not None:\\n startup_interval = int(startup_interval)\\n self.lastupdate = startup_interval\\n polling_interval = int(options.get(\\\"polling_interval\\\", 0))\\n self.state = options.get('state', None)\\n # Use a timeout value of polling_interval (in secs) + 10 secs to wait for all threads to end.\\n thread_timeout = (polling_interval * 60) + 10\\n\\n # Wait for threads to stop within thread timeout interval.\\n stop_time = time.time() + thread_timeout\\n while any(t.isAlive for t in self.threads) and (time.time() < stop_time):\\n time.sleep(0.1)\\n\\n # Get rid of stopped threads from list.\\n self.threads = [t for t in self.threads if t.is_alive()]\\n\\n if self.threads:\\n # Polling threads still running raise runtime error.\\n LOG.error(\\\"There were %d polling threads which did not stop within timeout period on restart\\\",\\n len(self.threads))\\n raise RuntimeError(\\\"There were {} polling threads which did not stop within timeout period on restart.\\\"\\n .format(len(self.threads)))\\n\\n # Turn off 'stop_thread' flag.\\n self.stop_thread = False\\n\\n if polling_interval > 0:\\n # Create and start polling thread\\n thread = Thread(target=self.polling_thread)\\n self.threads.append(thread)\\n thread.daemon = True\\n thread.start()\\n LOG.info(\\\"Polling for incidents in Proofpoint TRAP every %d minutes\\\", polling_interval)\\n else:\\n LOG.info(\\\"Polling for incidents in Proofpoint TRAP not enabled\\\")\",\n \"def __init__(self, ifname):\\n\\n self._dbus_loop = gobject.MainLoop()\\n self._bus = dbus.SystemBus()\\n wait_bus_owner_timeout = 5 # Wait for 5s to have an owner for the bus name we are expecting\\n logger.debug('Going to wait for an owner on bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n while not self._bus.name_has_owner(RemoteDhcpClientControl.DBUS_NAME):\\n time.sleep(0.2)\\n wait_bus_owner_timeout -= 0.2\\n if wait_bus_owner_timeout <= 0: # We timeout without having an owner for the expected bus name\\n raise Exception('No owner found for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n \\n logger.debug('Got an owner for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n gobject.threads_init() # Allow the mainloop to run as an independent thread\\n dbus.mainloop.glib.threads_init()\\n \\n dbus_object_name = RemoteDhcpClientControl.DBUS_OBJECT_ROOT + '/' + str(ifname)\\n logger.debug('Going to communicate with object ' + dbus_object_name)\\n self._dhcp_client_proxy = self._bus.get_object(RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE, dbus_object_name)\\n self._dbus_iface = dbus.Interface(self._dhcp_client_proxy, RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE)\\n \\n logger.debug(\\\"Connected to D-Bus\\\")\\n self._dhcp_client_proxy.connect_to_signal(\\\"IpConfigApplied\\\",\\n self._handleIpConfigApplied,\\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\\n \\n self._dhcp_client_proxy.connect_to_signal(\\\"LeaseLost\\\",\\n self._handleLeaseLost,\\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\\n \\n #Lionel: the following line is used for D-Bus debugging only\\n #self._bus.add_signal_receiver(catchall_signal_handler, interface_keyword='dbus_interface', member_keyword='member')\\n self._dbus_loop_thread = threading.Thread(target = self._loopHandleDbus) # Start handling D-Bus messages in a background thread\\n self._dbus_loop_thread.setDaemon(True) # D-Bus loop should be forced to terminate when main program exits\\n self._dbus_loop_thread.start()\\n \\n self._bus.watch_name_owner(RemoteDhcpClientControl.DBUS_NAME, self._handleBusOwnerChanged) # Install a callback to run when the bus owner changes\\n \\n self._callback_new_lease_mutex = threading.Lock() # This mutex protects writes to the _callback_new_lease attribute\\n self._callback_new_lease = None\\n \\n self._exit_unlock_event = threading.Event() # Create a new threading event that will allow the exit() method to wait for the child to terminate properly\\n self._getversion_unlock_event = threading.Event() # Create a new threading event that will allow the GetVersion() D-Bus call below to execute within a timed limit \\n\\n self.status = DhcpLeaseStatus.DhcpLeaseStatus()\\n\\n self._getversion_unlock_event.clear()\\n self._remote_version = ''\\n self._dbus_iface.GetVersion(reply_handler = self._getVersionUnlock, error_handler = self._getVersionError)\\n if not self._getversion_unlock_event.wait(10): # We give 10s for slave to answer the GetVersion() request\\n logfile = tempfile.NamedTemporaryFile(prefix='TimeoutOnGetVersion-', suffix='.log', delete=False)\\n if logfile:\\n print('Saving TimeoutOnGetVersion environment dump to file \\\"' + logfile.name + '\\\"', file=sys.stderr)\\n print('TimeoutOnGetVersion', file=logfile)\\n subprocess.call('ps -ef', stdout=logfile, shell=True)\\n subprocess.call('perl ./dbus-introspect.pl --system com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1', stdout=logfile, shell=True)\\n subprocess.call('dbus-send --system --type=method_call --print-reply --dest=com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1 com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary.GetVersion', stdout=logfile, shell=True)\\n logfile.close()\\n raise Exception('TimeoutOnGetVersion')\\n else:\\n logger.debug('Slave version: ' + self._remote_version)\",\n \"def activate(self):\\n super().activate()\\n self._change_lease_time(self.runner.config.get(\\\"dhcp_lease_time\\\"))\\n self._scan_finalize()\",\n \"def interface_thread(file_name):\\n # this thread will first wait a few seconds\\n # and then run so that the file is read is\\n # for initial data by the read_config_file func\\n # the initial file is stored as string by read_config_file\\n global INITIAL_CONFIG_FILE\\n global READ_CONFIG_COMP\\n\\n if READ_CONFIG_COMP:\\n start = current_time()\\n while True:\\n if (current_time() - start) < 30:\\n time.sleep(5)\\n continue\\n else:\\n file1 = open(file_name, \\\"r\\\")\\n temp = file1.read()\\n file1.close()\\n if INITIAL_CONFIG_FILE == temp:\\n start = current_time()\\n continue\\n else:\\n list_1 = INITIAL_CONFIG_FILE.split(\\\"\\\\n\\\")\\n list_2 = temp.split(\\\"\\\\n\\\")\\n # first line that's no. of lines is never changed\\n diff = [\\n (ind, x[1])\\n for ind, x in enumerate(zip(list_1, list_2))\\n if x[0] != x[1]\\n ]\\n for ind, new_str in diff:\\n # we use ind - 1 because list is unordered\\n # and line no. is not included here hence\\n # index is 1 less\\n DATA[\\\"neighbor\\\"].pop(ind - 1)\\n new_en = [x for x in new_str.split(\\\" \\\")]\\n new_en[1] = float(new_en[1])\\n new_en[2] = int(new_en[2])\\n DATA[\\\"neighbor\\\"].insert(ind - 1, new_en)\\n # inserting new base router data to n_d_vec\\n DATA[\\\"n_d_vec\\\"][DATA[\\\"router_id\\\"]] = DATA[\\\"neighbor\\\"]\\n INITIAL_CONFIG_FILE = temp\\n start = current_time()\",\n \"def run_forever(self, *args, **kwargs):\\n try:\\n self.logger.debug('Begin account update')\\n\\n # get account-updater server ownership\\n self.get_ownership_obj = threading.Thread(target = self.msg.get_my_ownership)\\n self.get_ownership_obj.setDaemon(True)\\n self.get_ownership_obj.start()\\n\\n self.walker_obj = Walker(self.walker_map, self.__param, self.logger)\\n self.walker_obj.setDaemon(True)\\n self.walker_obj.start()\\n self.logger.info(\\\"Walker Started\\\")\\n self.reader_obj = Reader(self.walker_map, self.reader_map, \\\\\\n self.__param, self.logger)\\n self.reader_obj.setDaemon(True)\\n self.reader_obj.start() \\n self.logger.info(\\\"Reader Started\\\")\\n self.account_sweeper = AccountSweep(self.__param, self.logger)\\n self.account_sweeper.setDaemon(True)\\n self.account_sweeper.start()\\n self.logger.info(\\\"Account Sweeper Started\\\") \\n self.updater_obj = Updater(self.walker_map, self.reader_map, \\\\\\n self.__param, self.logger)\\n self.updater_obj.setDaemon(True)\\n self.updater_obj.start() \\n self.logger.info(\\\"Updater Started\\\") \\n self.container_sweeper = ContainerSweeper(self.walker_map, \\\\\\n self.reader_map, self.__param, self.logger)\\n self.container_sweeper.setDaemon(True)\\n self.container_sweeper.start()\\n self.logger.info(\\\"Container Sweeper Started\\\") \\n\\n account_updater_server = ThreadedAccountUpdaterServer(\\\\\\n (self.__get_node_ip(gethostname()), \\\\\\n self.__account_updater_port), HttpListener)\\n account_updater_server.serve_forever()\\n except Exception as ex:\\n self.logger.error(\\\"Exception occured: %s\\\" % ex)\",\n \"def run(self):\\n self.running = True\\n dynamic_reconfigure.client.Client(\\\"dynamic_reconfigure_server\\\", timeout=5, config_callback=self.dynamic_reconfigure_callback)\\n rate = rospy.Rate(20)\\n while not rospy.is_shutdown() and self.running:\\n self.pub.publish(self.get_cmd_vel())\\n rate.sleep()\",\n \"def start(self):\\n def f():\\n if (self.started): return\\n self.started = True\\n with client.ServerProxy(self.host) as proxy:\\n while (not self.req_shutdown):\\n self.update_speed(proxy)\\n time.sleep(self.com_freq)\\n self.started = False\\n self.req_shutdwon = False\\n\\n Thread(target=f).start()\",\n \"def _StatusUpdateThreadMain(self):\\n while self._status_update_active:\\n self._UpdateStatus()\\n time.sleep(self._status_update_interval)\",\n \"def run(self):\\n logger.debug(\\\"Starting a daemon thread to sync experiment states\\\")\\n while self.thread_running.is_set():\\n try:\\n self.sync_experiment_state_with_ddb()\\n except Exception as e:\\n logger.warn(\\\"Exception occurred in Experiment Sync Thread: \\\" + str(e))\\n logger.error(e)\\n logger.warn(\\\"Resuming Sync in 10 seconds...\\\")\\n time.sleep(10)\\n time.sleep(0.5)\",\n \"def heartbeat_thread_func(self):\\n while True:\\n heartbeat = json.dumps({\\\"type\\\": \\\"HEARTBEAT\\\",\\n \\\"issued\\\": time.time() * 1000})\\n log.info(\\\"Sending ACL heartbeat %s\\\" % heartbeat)\\n self.pub_lock.acquire()\\n self.pub_socket.send_multipart([\\\"aclheartbeat\\\",\\n heartbeat.encode(\\\"utf-8\\\")])\\n self.pub_lock.release()\\n log.debug(\\\"Sent ACL heartbeat\\\")\\n\\n # In a perfect world this should subtract the time spent waiting\\n # for the lock and sending the packet. For the moment, in normal\\n # operation this will suffice.\\n time.sleep(30)\",\n \"def run(self):\\n try:\\n rcvdConfig = self.conn.recv(self.size)\\n if self.verbosity >= 1:\\n print('%s: Received request from client %s:%s.' % (self.feederName, self.addr, self.port))\\n except socket.error as e:\\n raise Error(e.strerror)\\n self.lockConfig.acquire()\\n self.config.newConfig = json.loads(rcvdConfig)\\n self.config.updateConfig()\\n self.lockConfig.release()\\n self.conn.close()\",\n \"def updateRunningConf(self, responsible):\\n stop_command, start_command = map(\\n self.gen_actionCommand, (\\\"stop\\\", \\\"start\\\")\\n )\\n\\n #runCommandAsRootAndCheck raises in case of an error (perfect!)\\n yield deferToThread(self.runCommandAsRootAndCheck, stop_command)\\n\\n ntpservers = self.CONFIG['ntpservers'].split(' ')\\n responsible.feedback(\\n tr(\\\"Reference servers: %(NTPSERVERS)s\\\"),\\n NTPSERVERS=', '.join(ntpservers)\\n )\\n\\n ha_status = None\\n context = Context.fromComponent(self)\\n if EDENWALL and self.core.hasComponent(context, 'ha'):\\n try:\\n ha_status = yield self.core.callService(context, 'ha', 'getHAMode')\\n except Exception, err:\\n self.error(exceptionAsUnicode(err))\\n peers = _ntppeers(responsible, ha_status)\\n\\n template_variables = {\\n 'ntpservers' : self.CONFIG['ntpservers'].split(' '),\\n 'peers': peers,\\n }\\n self.generate_configfile(template_variables)\\n\\n yield deferToThread(self.runCommandAsRootAndCheck, start_command)\",\n \"def send_state():\\n while True:\\n if I_AM_CRUSHED is False:\\n sleep_time = random.randint(send_state_sec[0], send_state_sec[1])\\n sock_check = socket.socket(type=socket.SOCK_DGRAM)\\n sock_check.sendto(\\\"I'am healthy\\\", ('dispatcher', port_for_check))\\n sock_check.close()\\n time.sleep(sleep_time)\",\n \"def sleep_for_cloudinit():\\n run(\\\"sleep 15\\\")\",\n \"def broadcast_thread(self):\\n while True:\\n try:\\n logger.info('broadcast routing table (dpid=%s)', dpid_to_str(self.dp.id))\\n for port_no, port in self.ports.items():\\n if port.neighbor_switch_dpid:\\n self.switches[port.neighbor_switch_dpid].add_to_queue((port, self.tbl))\\n self.switches[port.neighbor_switch_dpid].trigger_update()\\n time.sleep(self.tbl.advertise_interval)\\n except:\\n logger.info('broadcast thread of dpid=%s is killed', dpid_to_str(self.dp.id))\\n break\",\n \"def start_pull_thread(self):\\r\\n threading.Thread(target=self._pull_thread).start()\",\n \"def idle_handler(self):\\n if self.state == _STATE.INIT:\\n self._activate()\\n elif self.state == _STATE.ACTIVE:\\n self._state_transition(_STATE.DHCP, _STATE.ACTIVE)\\n self.record_result('dhcp', state='run')\\n self.dhcp_monitor.start()\\n elif self.state == _STATE.BASE:\\n self._base_start()\",\n \"def dhcp_renew(ifname):\\n\\n logging.debug('Renewing %s DHCP lease...', ifname)\\n\\n try:\\n subprocess.call(['dhcpcd', '--rebind', ifname])\\n except OSError, err:\\n if err.errno != errno.ENOENT:\\n raise err\\n\\n try:\\n call_timeout(['dhclient', '-1', ifname], timeout=5)\\n except OSError, err:\\n if err.errno == errno.ENOENT:\\n logging.critical('Neither dhcpcd nor dhclient were found.')\\n else:\\n raise err\",\n \"def _StartStatusUpdateThread(self):\\n self._status_update_active = True\\n self._status_update_thread = threading.Thread(\\n name='Status update', target=self._StatusUpdateThreadMain)\\n self._status_update_thread.start()\",\n \"def refresh_dhcp_helper(self, network_id):\\n old_network = self.cache.get_network_by_id(network_id)\\n if not old_network:\\n # DHCP current not running for network.\\n return self.enable_dhcp_helper(network_id)\\n\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\\n\\n if new_cidrs and old_cidrs == new_cidrs:\\n self.call_driver('reload_allocations', network)\\n self.cache.put(network)\\n elif new_cidrs:\\n if self.call_driver('restart', network):\\n self.cache.put(network)\\n else:\\n self.disable_dhcp_helper(network.id)\",\n \"def run(self):\\n while self.time <= self.deadline:\\n self.setup_period()\\n self.execute_process(self.select_process())\\n\\n self.time += 1\",\n \"def __init__(self, dt, ADDR=TIME_IP, PORT=TIME_PORT, interval=1, retry_connect=30):\\n self.dt = dt\\n self.ADDR = ADDR\\n self.PORT = PORT\\n self.retry_connect = retry_connect\\n self.interval = interval\\n self.tcpclient = TCPClient(self.dt, ADDR, PORT, interval, retry_connect=self.retry_connect)\\n thread = threading.Thread(target=self.run, args=())\\n thread.daemon = True\\n thread.start()\",\n \"async def async_setup_entry( # noqa: C901\\n hass: HomeAssistant, entry: ConfigEntry\\n) -> bool:\\n websession = aiohttp_client.async_get_clientsession(hass)\\n client = Client(session=websession)\\n ip_address = entry.data[CONF_IP_ADDRESS]\\n\\n try:\\n await client.load_local(\\n ip_address,\\n entry.data[CONF_PASSWORD],\\n port=entry.data[CONF_PORT],\\n use_ssl=entry.data.get(CONF_SSL, DEFAULT_SSL),\\n )\\n except RainMachineError as err:\\n raise ConfigEntryNotReady from err\\n\\n # regenmaschine can load multiple controllers at once, but we only grab the one\\n # we loaded above:\\n controller = get_client_controller(client)\\n\\n entry_updates: dict[str, Any] = {}\\n if not entry.unique_id or is_ip_address(entry.unique_id):\\n # If the config entry doesn't already have a unique ID, set one:\\n entry_updates[\\\"unique_id\\\"] = controller.mac\\n\\n if CONF_DEFAULT_ZONE_RUN_TIME in entry.data:\\n # If a zone run time exists in the config entry's data, pop it and move it to\\n # options:\\n data = {**entry.data}\\n entry_updates[\\\"data\\\"] = data\\n entry_updates[\\\"options\\\"] = {\\n **entry.options,\\n CONF_DEFAULT_ZONE_RUN_TIME: data.pop(CONF_DEFAULT_ZONE_RUN_TIME),\\n }\\n if CONF_USE_APP_RUN_TIMES not in entry.options:\\n entry_updates[\\\"options\\\"] = {**entry.options, CONF_USE_APP_RUN_TIMES: False}\\n if entry_updates:\\n hass.config_entries.async_update_entry(entry, **entry_updates)\\n\\n if entry.unique_id and controller.mac != entry.unique_id:\\n # If the mac address of the device does not match the unique_id\\n # of the config entry, it likely means the DHCP lease has expired\\n # and the device has been assigned a new IP address. We need to\\n # wait for the next discovery to find the device at its new address\\n # and update the config entry so we do not mix up devices.\\n raise ConfigEntryNotReady(\\n f\\\"Unexpected device found at {ip_address}; expected {entry.unique_id}, \\\"\\n f\\\"found {controller.mac}\\\"\\n )\\n\\n async def async_update(api_category: str) -> dict:\\n \\\"\\\"\\\"Update the appropriate API data based on a category.\\\"\\\"\\\"\\n data: dict = {}\\n\\n try:\\n if api_category == DATA_API_VERSIONS:\\n data = await controller.api.versions()\\n elif api_category == DATA_MACHINE_FIRMWARE_UPDATE_STATUS:\\n data = await controller.machine.get_firmware_update_status()\\n elif api_category == DATA_PROGRAMS:\\n data = await controller.programs.all(include_inactive=True)\\n elif api_category == DATA_PROVISION_SETTINGS:\\n data = await controller.provisioning.settings()\\n elif api_category == DATA_RESTRICTIONS_CURRENT:\\n data = await controller.restrictions.current()\\n elif api_category == DATA_RESTRICTIONS_UNIVERSAL:\\n data = await controller.restrictions.universal()\\n else:\\n data = await controller.zones.all(details=True, include_inactive=True)\\n except UnknownAPICallError:\\n LOGGER.info(\\n \\\"Skipping unsupported API call for controller %s: %s\\\",\\n controller.name,\\n api_category,\\n )\\n except RainMachineError as err:\\n raise UpdateFailed(err) from err\\n\\n return data\\n\\n async def async_init_coordinator(\\n coordinator: RainMachineDataUpdateCoordinator,\\n ) -> None:\\n \\\"\\\"\\\"Initialize a RainMachineDataUpdateCoordinator.\\\"\\\"\\\"\\n await coordinator.async_initialize()\\n await coordinator.async_config_entry_first_refresh()\\n\\n controller_init_tasks = []\\n coordinators = {}\\n for api_category, update_interval in COORDINATOR_UPDATE_INTERVAL_MAP.items():\\n coordinator = coordinators[api_category] = RainMachineDataUpdateCoordinator(\\n hass,\\n entry=entry,\\n name=f'{controller.name} (\\\"{api_category}\\\")',\\n api_category=api_category,\\n update_interval=update_interval,\\n update_method=partial(async_update, api_category),\\n )\\n controller_init_tasks.append(async_init_coordinator(coordinator))\\n\\n await asyncio.gather(*controller_init_tasks)\\n\\n hass.data.setdefault(DOMAIN, {})\\n hass.data[DOMAIN][entry.entry_id] = RainMachineData(\\n controller=controller, coordinators=coordinators\\n )\\n\\n await hass.config_entries.async_forward_entry_setups(entry, PLATFORMS)\\n\\n entry.async_on_unload(entry.add_update_listener(async_reload_entry))\\n\\n def call_with_controller(update_programs_and_zones: bool = True) -> Callable:\\n \\\"\\\"\\\"Hydrate a service call with the appropriate controller.\\\"\\\"\\\"\\n\\n def decorator(func: Callable) -> Callable[..., Awaitable]:\\n \\\"\\\"\\\"Define the decorator.\\\"\\\"\\\"\\n\\n @wraps(func)\\n async def wrapper(call: ServiceCall) -> None:\\n \\\"\\\"\\\"Wrap the service function.\\\"\\\"\\\"\\n entry = async_get_entry_for_service_call(hass, call)\\n data: RainMachineData = hass.data[DOMAIN][entry.entry_id]\\n\\n try:\\n await func(call, data.controller)\\n except RainMachineError as err:\\n raise HomeAssistantError(\\n f\\\"Error while executing {func.__name__}: {err}\\\"\\n ) from err\\n\\n if update_programs_and_zones:\\n await async_update_programs_and_zones(hass, entry)\\n\\n return wrapper\\n\\n return decorator\\n\\n @call_with_controller()\\n async def async_pause_watering(call: ServiceCall, controller: Controller) -> None:\\n \\\"\\\"\\\"Pause watering for a set number of seconds.\\\"\\\"\\\"\\n await controller.watering.pause_all(call.data[CONF_SECONDS])\\n\\n @call_with_controller(update_programs_and_zones=False)\\n async def async_push_flow_meter_data(\\n call: ServiceCall, controller: Controller\\n ) -> None:\\n \\\"\\\"\\\"Push flow meter data to the device.\\\"\\\"\\\"\\n value = call.data[CONF_VALUE]\\n if units := call.data.get(CONF_UNIT_OF_MEASUREMENT):\\n await controller.watering.post_flowmeter(value=value, units=units)\\n else:\\n await controller.watering.post_flowmeter(value=value)\\n\\n @call_with_controller(update_programs_and_zones=False)\\n async def async_push_weather_data(\\n call: ServiceCall, controller: Controller\\n ) -> None:\\n \\\"\\\"\\\"Push weather data to the device.\\\"\\\"\\\"\\n await controller.parsers.post_data(\\n {\\n CONF_WEATHER: [\\n {\\n key: value\\n for key, value in call.data.items()\\n if key != CONF_DEVICE_ID\\n }\\n ]\\n }\\n )\\n\\n @call_with_controller()\\n async def async_restrict_watering(\\n call: ServiceCall, controller: Controller\\n ) -> None:\\n \\\"\\\"\\\"Restrict watering for a time period.\\\"\\\"\\\"\\n duration = call.data[CONF_DURATION]\\n await controller.restrictions.set_universal(\\n {\\n \\\"rainDelayStartTime\\\": round(as_timestamp(utcnow())),\\n \\\"rainDelayDuration\\\": duration.total_seconds(),\\n },\\n )\\n\\n @call_with_controller()\\n async def async_stop_all(call: ServiceCall, controller: Controller) -> None:\\n \\\"\\\"\\\"Stop all watering.\\\"\\\"\\\"\\n await controller.watering.stop_all()\\n\\n @call_with_controller()\\n async def async_unpause_watering(call: ServiceCall, controller: Controller) -> None:\\n \\\"\\\"\\\"Unpause watering.\\\"\\\"\\\"\\n await controller.watering.unpause_all()\\n\\n @call_with_controller()\\n async def async_unrestrict_watering(\\n call: ServiceCall, controller: Controller\\n ) -> None:\\n \\\"\\\"\\\"Unrestrict watering.\\\"\\\"\\\"\\n await controller.restrictions.set_universal(\\n {\\n \\\"rainDelayStartTime\\\": round(as_timestamp(utcnow())),\\n \\\"rainDelayDuration\\\": 0,\\n },\\n )\\n\\n for service_name, schema, method in (\\n (\\n SERVICE_NAME_PAUSE_WATERING,\\n SERVICE_PAUSE_WATERING_SCHEMA,\\n async_pause_watering,\\n ),\\n (\\n SERVICE_NAME_PUSH_FLOW_METER_DATA,\\n SERVICE_PUSH_FLOW_METER_DATA_SCHEMA,\\n async_push_flow_meter_data,\\n ),\\n (\\n SERVICE_NAME_PUSH_WEATHER_DATA,\\n SERVICE_PUSH_WEATHER_DATA_SCHEMA,\\n async_push_weather_data,\\n ),\\n (\\n SERVICE_NAME_RESTRICT_WATERING,\\n SERVICE_RESTRICT_WATERING_SCHEMA,\\n async_restrict_watering,\\n ),\\n (SERVICE_NAME_STOP_ALL, SERVICE_SCHEMA, async_stop_all),\\n (SERVICE_NAME_UNPAUSE_WATERING, SERVICE_SCHEMA, async_unpause_watering),\\n (\\n SERVICE_NAME_UNRESTRICT_WATERING,\\n SERVICE_SCHEMA,\\n async_unrestrict_watering,\\n ),\\n ):\\n if hass.services.has_service(DOMAIN, service_name):\\n continue\\n hass.services.async_register(DOMAIN, service_name, method, schema=schema)\\n\\n return True\",\n \"def _delay_update(now):\\n _LOGGER.debug(\\n \\\"%s Called delayed (%ssec) update\\\", self._name, self._delay\\n )\\n self.schedule_update_ha_state()\\n self._timer = None\",\n \"def _RunDHCPCD(self, **kwargs):\\n del kwargs\\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\\n interface=self.interface)\\n # -K: Don't receive link messages for carrier status. You should\\n # only have to use this with buggy device drivers or running\\n # dhcpcd through a network manager.\\n # -c: Location to the hooks file. If the default location happens to be\\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\\n timeout=self._dhcp_timeout,\\n interface=self.interface)\\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\\n timeout=self._dhcp_timeout,\\n cmd=dhcp_command)\\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\\n\\n logging.info('Killing any existing dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n logging.info('Clearing any existing ifconfig networks...')\\n self._device.Call(clear_ifconfig_command)\\n\\n logging.info('Starting dhcpcd...')\\n self._device.CheckCall(dhcp_timeout_command)\\n\\n logging.info('Verifying IP address...')\\n ip = self._LeasedIP()\\n if not ip:\\n self._device.Call(force_kill_command)\\n raise WiFiError('DHCP bind failed')\\n logging.info('Success: bound to IP %s', ip)\\n\\n yield ip # We have bound an IP; yield back to the caller.\\n\\n logging.info('Killing any remaining dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n yield # We have released the IP.\",\n \"def check_daemon(self):\\n log.debug(\\\"Checking for the DHCP daemon status.\\\")\\n\\n d = defer.Deferred()\\n args = ( self.initscript, 'status' )\\n env = dict(os.environ)\\n\\n process = reactor.spawnProcess(OutputGrabber(d), self.initscript,\\n args=args, env=env)\\n\\n def try_restart(ign, status):\\n self.script_running = False\\n\\n if not status:\\n log.debug(\\\"Scheduling for immediate restart.\\\")\\n self.schedule('restart', 0)\\n else:\\n log.debug(\\\"Scheduling the next status check.\\\")\\n self.schedule('check', self.daemon_check_interval)\\n\\n def complete(output, timer):\\n try:\\n timer.cancel()\\n except:\\n pass\\n\\n # Use heuristics to determine whether the script succeeded\\n # or not.\\n running = output.find('is running') != -1\\n\\n return defer.succeed(running).\\\\\\n addCallback(self.set_daemon_running).\\\\\\n addCallback(try_restart, running)\\n\\n def abort(process, d):\\n log.error(\\\"Script timeout while checking DHCP daemon status. \\\" + \\n \\\"Killing the script.\\\")\\n try:\\n process.signalProcess(SIGKILL)\\n except:\\n log.exception(\\\"Unable to kill the script process.\\\")\\n\\n # Don't call errback here, but let the process die and\\n # complete function above handle the error.\\n\\n #d.errback(Failure(RuntimeError('Init script timeout (check)')))\\n\\n def error(failure):\\n log.exception(failure)\\n\\n return defer.succeed(False).\\\\\\n addCallback(self.set_daemon_running).\\\\\\n addCallback(try_restart, False)\\n\\n timer = reactor.callLater(self.timeout, abort, process, d)\\n\\n return d.addCallback(complete, timer).\\\\\\n addErrback(error)\",\n \"def _maintenance_thread():\\n while True:\\n _purge_expired_items()\\n time.sleep(maintenance_sleep)\\n\\n return\",\n \"def start_srv(start, process):\\n if not \\\"conf_option\\\" in world.cfg:\\n world.cfg[\\\"conf_option\\\"] = \\\"\\\"\\n\\n world.cfg['log_file'] = build_log_path()\\n fabric_sudo_command('cat /dev/null >' + world.cfg['log_file'])\\n world.cfg[\\\"dhcp_log_file\\\"] = world.cfg['log_file']\\n\\n log = \\\"local7\\\"\\n if world.f_cfg.isc_dhcp_log_facility != \\\"\\\":\\n log = world.f_cfg.isc_dhcp_log_facility\\n\\n world.cfg['log_facility'] = '''\\\\nlog-facility {log};\\\\n'''.format(**locals())\\n\\n add_defaults()\\n cfg_write()\\n log.debug(\\\"Start ISC-DHCP with generated config:\\\")\\n convert_cfg_file(world.cfg[\\\"cfg_file\\\"])\\n fabric_send_file(world.cfg[\\\"cfg_file\\\"] + '_processed', world.cfg[\\\"cfg_file\\\"] + '_processed')\\n copy_configuration_file(world.cfg[\\\"cfg_file\\\"] + '_processed')\\n remove_local_file(world.cfg[\\\"cfg_file\\\"])\\n #set_ethernet_interface()\\n stop_srv()\\n\\n world.cfg['leases'] = build_leases_path()\\n\\n #fabric_sudo_command('echo y |rm ' + world.cfg['leases'])\\n fabric_sudo_command('touch ' + world.cfg['leases'])\\n\\n result = fabric_sudo_command('(' + os.path.join(world.f_cfg.software_install_path, 'sbin/dhcpd') + ' -cf server.cfg_processed'\\n + ' -lf ' + world.cfg['leases']\\n + '&); sleep ' + str(world.f_cfg.sleep_time_1) + ';')\\n\\n check_process_result(start, result, process)\\n\\n # clear configs in case we would like make couple configs in one test\\n world.cfg[\\\"conf_time\\\"] = \\\"\\\"\\n world.cfg[\\\"log_facility\\\"] = \\\"\\\"\\n world.cfg[\\\"custom_lines\\\"] = \\\"\\\"\\n world.cfg[\\\"conf_option\\\"] = \\\"\\\"\\n world.cfg[\\\"conf_vendor\\\"] = \\\"\\\"\",\n \"def run(self):\\n\\n self._daemon_thread.start()\\n\\n while True:\\n time.sleep(5)\",\n \"def work(self):\\n self.config_file = self.args.config\\n self.init_config()\\n self.init_db()\\n\\n self.kickoff()\",\n \"def _refresh_discovery(self):\\n if self.terminate_flag:\\n return\\n\\n self.devices = discover_drones(self.ip_range, self.skyserve_port)\\n time.sleep(self.refresh_interval / 1000)\\n self._refresh_discovery()\",\n \"def run():\\r\\n event = threading.Event()\\r\\n while (event.is_set() == False):\\r\\n # perform database backup\\r\\n backup()\\r\\n\\r\\n # sleep for the predefined amount interval\\r\\n event.wait(BACKUP_INTERVAL)\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def ping_thread(state):\\n while state.running:\\n time.sleep(PING_FREQ)\\n # for nick in users:\\n # users[nick].queue.append(('SERVER', 'PING'))\",\n \"def thread_function(client):\\n threading.Timer(30.0, thread_function).start()\\n client.publish(\\\"serverCommand/keepalive\\\", \\\"0\\\")\\n print(\\\"Message Sent. (keepalive)\\\")\",\n \"def periodic_flavor_sync(ctx, driver, scg_id_list):\\n sync_interval = CONF.powervc.flavor_sync_interval\\n\\n if sync_interval is None or sync_interval == 0:\\n return\\n\\n def flavors_sync(driver, scg_id_list):\\n FlavorSync(driver, scg_id_list).synchronize_flavors(ctx)\\n LOG.debug('Flavors synchronization completed')\\n\\n sync_flavors = loopingcall.FixedIntervalLoopingCall(flavors_sync,\\n driver,\\n scg_id_list)\\n sync_flavors.start(interval=sync_interval, initial_delay=sync_interval)\",\n \"def start(self):\\n self.build_client_snapshot()\\n self.load_local_dir_state()\\n\\n # Operations necessary to start the daemon\\n self.create_observer()\\n self.observer.start()\\n self.sync_with_server()\\n\\n self.listener_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n self.listener_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n self.listener_socket.bind((self.cfg['cmd_address'], self.cfg['cmd_port']))\\n self.listener_socket.listen(self.cfg['backlog_listener_sock'])\\n r_list = [self.listener_socket]\\n self.daemon_state = 'started'\\n self.running = 1\\n polling_counter = 0\\n try:\\n while self.running:\\n r_ready, w_ready, e_ready = select.select(r_list, [], [], self.cfg['timeout_listener_sock'])\\n\\n for s in r_ready:\\n\\n if s == self.listener_socket:\\n # handle the server socket\\n client_socket, client_address = self.listener_socket.accept()\\n r_list.append(client_socket)\\n else:\\n # handle all other sockets\\n length = s.recv(Daemon.INT_SIZE)\\n if length:\\n # i need to do [0] and cast int because the struct.unpack return a tupla like (23234234,)\\n # with the length as a string\\n length = int(struct.unpack('!i', length)[0])\\n message = json.loads(s.recv(length))\\n for cmd, data in message.items():\\n if cmd == 'shutdown':\\n raise KeyboardInterrupt\\n self.conn_mng.dispatch_request(cmd, data)\\n else:\\n s.close()\\n r_list.remove(s)\\n\\n # synchronization polling\\n # makes the polling every 3 seconds, so it waits six cycle (0.5 * 6 = 3 seconds)\\n # maybe optimizable but now functional\\n polling_counter += 1\\n if polling_counter == 6:\\n self.sync_with_server()\\n polling_counter = 0\\n\\n except KeyboardInterrupt:\\n self.stop(0)\\n self.observer.stop()\\n self.observer.join()\\n self.listener_socket.close()\",\n \"def create_daemon(self, handle, refresh_delay=5):\\n self.handle = handle\\n self.refresh_delay = refresh_delay\\n self.thread = threading.Thread(name=\\\"Reddit Daemon\\\", target=self._keep_getting_new_messages)\\n self.thread.daemon = True\\n self.thread.start()\",\n \"def start(self):\\n\\n if self.__bus_controller == None:\\n print(\\\"can't start please pass me the needed dictionaries\\\")\\n\\n self.__global_messages = {\\\"kick reason\\\": \\\"\\\", \\\"free text\\\": \\\"\\\"}\\n self.__lock_data = False\\n self.__socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n self.stop = False\\n __main_loop = threading.Thread(target=self.__main_loop, args=(), name=\\\"bus updater\\\")\\n __main_loop.start()\",\n \"def run(self):\\n while True:\\n if self.timeout - time() <= 0:\\n if self.state in [State.CANDIDATE, State.FOLLOWER]:\\n self.start_election()\\n\\n elif self.state is State.LEADER:\\n self.send_appends()\\n self.commit_entries()\\n\\n else:\\n self.unknown_state()\\n\\n self.handle_message()\",\n \"def configureDHCP():\\n dhcpStart = config.get(\\\"hotspot\\\", \\\"dhcpstart\\\")\\n dhcpEnd = config.get(\\\"hotspot\\\", \\\"dhcpend\\\")\\n dnsmasqConfig = f\\\"\\\"\\\"#PI Hotspot config\\ndomain-needed\\nbogus-priv\\ndhcp-option=option:dns-server\\ndhcp-authoritative\\ndhcp-range={dhcpStart},{dhcpEnd},1h\\n\\\"\\\"\\\"\\n confFile = open(\\\"/etc/dnsmasq.conf\\\", \\\"w\\\")\\n confFile.write(dnsmasqConfig)\\n confFile.close()\",\n \"def run(self):\\n while self.running:\\n self.__update_battery()\\n self.__update_signal()\\n time.sleep(5)\",\n \"def sync_dns(self,):\\n\\n for server_name, server_ip in self.get_instances():\\n self.dnsmanager.ensure_a_record(server_name, server_ip)\",\n \"def refresh():\\n\\n if _TRAFFICCTL:\\n cmd = _traffic_ctl(\\\"config\\\", \\\"reload\\\")\\n else:\\n cmd = _traffic_line(\\\"-x\\\")\\n\\n return _subprocess(cmd)\",\n \"def run(self):\\n last_time = time.time()\\n while self.running:\\n now_time = time.time()\\n interval = now_time - last_time\\n last_time = now_time\\n self.update(interval)\\n time.sleep(Options['update interval'])\",\n \"def main():\\n\\tlogger.warn(\\\"leprechaun rsync started.\\\")\\n\\n\\ttime.sleep(1)\\n\\tts_rsync()\\n\\tsqlite_rsync()\\n\\n\\twhile True:\\n\\t\\ttry:\\n\\t\\t\\t# schedule crash guard:\\n\\t\\t\\timportlib.reload(schedule)\\n\\t\\t\\tschedule.every(1301).seconds.do(ts_rsync)\\n\\t\\t\\tschedule.every(300).seconds.do(sqlite_rsync)\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\t\\tschedule.run_pending()\\n\\t\\t\\t\\texcept Exception as ex:\\n\\t\\t\\t\\t\\tlogger.exception(ex)\\n\\t\\t\\t\\t\\ttime.sleep(2)\\n\\t\\texcept Exception as ex:\\n\\t\\t\\tlogger.exception(ex)\\n\\t\\t\\ttime.sleep(2)\",\n \"def run(self):\\r\\n while not self.shutdown_flag:\\r\\n try:\\r\\n # wait for connection\\r\\n if len(self.conns) == 0:\\r\\n self._wait()\\r\\n\\r\\n # receive message\\r\\n conn, (command, args) = self.recv()\\r\\n\\r\\n # process according to request\\r\\n if command == 'update_policy_network':\\r\\n self.agent.update_policy_network()\\r\\n elif command == 'get_action':\\r\\n action = self.agent.get_action(**args)\\r\\n self.send(conn, action)\\r\\n elif command == 'memorize':\\r\\n self.agent.memorize(**args)\\r\\n elif command == 'save_model':\\r\\n self.agent.save_model(**args)\\r\\n elif command == 'load_model':\\r\\n self.agent.load_model(**args)\\r\\n elif command == 'save_memory':\\r\\n self.agent.save_memory(**args)\\r\\n elif command == 'load_memory':\\r\\n self.agent.load_memory(**args)\\r\\n elif command == 'update_target_network':\\r\\n self.agent.update_target_network()\\r\\n elif command == 'detach':\\r\\n self.disconnect(conn)\\r\\n elif command == 'shutdown':\\r\\n self.shutdown_flag = True\\r\\n else:\\r\\n print('unknown command: {}'.format(command))\\r\\n except KeyboardInterrupt:\\r\\n print('interrupted')\\r\\n self.shutdown_flag = True\\r\\n break\",\n \"def start(self):\\n self.open()\\n #t = Thread(target=self._cache_update, args=())\\n #t.daemon = True\\n #t.start()\",\n \"def _inst_run(self):\\r\\n self._inst_get_img_info_from_db()\\r\\n print(\\\"run method: \\\", time.ctime())\\r\\n th = threading.Timer(\\r\\n 10,\\r\\n self._inst_run\\r\\n )\\r\\n th.start()\",\n \"def config_and_verify_dhcp_option(ssh_conn_obj, dut, ztp_params, data, expect_reboot=False, reboot_on_success=list(), cli_type=\\\"\\\"):\\n cli_type = st.get_ui_type(dut,cli_type=cli_type)\\n cli_type = \\\"klish\\\" if cli_type in [\\\"rest-put\\\", \\\"rest-patch\\\"] else cli_type\\n retry_count = data.retry_count if \\\"retry_count\\\" in data and data.retry_count else 0\\n iteration = data.iteration if \\\"iteration\\\" in data and data.iteration else 300\\n delay = data.delay if \\\"delay\\\" in data and data.delay else 3\\n if \\\"func_name\\\" in data:\\n syslog_file_names = [\\\"syslog_1_{}\\\".format(data.func_name), \\\"syslog_{}\\\".format(data.func_name)]\\n # basic_obj.copy_config_db_to_temp(dut, data.config_db_path, data.config_db_temp)\\n if \\\"config_file_type\\\" in data and data.config_file_type == \\\"text\\\":\\n file_path = \\\"/tmp/file_temp.json\\\"\\n basic_obj.write_to_file(ssh_conn_obj, data.json_content, file_path, device=\\\"server\\\")\\n elif \\\"config_file_type\\\" in data and data.config_file_type == \\\"EoL\\\":\\n file_path = \\\"\\\"\\n else:\\n file_path = basic_obj.write_to_json_file(data.json_content)\\n if file_path:\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_file)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"config_db_location\\\" in data and data.config_db_location == \\\"json\\\":\\n st.download_file_from_dut(dut, data.config_db_temp, file_path)\\n destination_path = \\\"{}{}/{}\\\".format(ztp_params.home_path, ztp_params.config_path, data.config_db_file_name)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n if \\\"scenario\\\" in data and data.scenario == \\\"invalid-json\\\":\\n st.log(\\\"Writing invalid content to make invalid json ...\\\")\\n basic_obj.write_to_file_to_line(ssh_conn_obj, \\\",\\\", 5, destination_path, \\\"server\\\")\\n if data.option_type == \\\"67\\\":\\n st.log(\\\"Creating {} file on DHCP server ...\\\".format(data.config_file))\\n data.search_pattern = r'\\\\s*option\\\\s+bootfile-name\\\\s*\\\\S*\\\\s*\\\"\\\\S+\\\";'\\n data.option_string = \\\"option bootfile-name\\\"\\n if data.type == \\\"http\\\":\\n data.option_url = \\\"http://{}{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"tftp\\\":\\n data.option_url = \\\"tftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n elif data.type == \\\"ftp\\\":\\n data.option_url = \\\"ftp://{}/{}/{}\\\".format(data.static_ip, data.config_path, data.config_file)\\n write_option_to_dhcp_server(ssh_conn_obj, data)\\n basic_obj.service_operations(ssh_conn_obj, data.dhcp_service_name, data.action, data.device)\\n if not verify_dhcpd_service_status(ssh_conn_obj, data.dhcpd_pid):\\n st.log(\\\"{} service not running\\\".format(data.dhcp_service_name))\\n st.report_fail(\\\"service_not_running\\\", data.dhcp_service_name)\\n # write_option_67_to_dhcp_server(ssh_conn_obj, data)\\n data.device_action = \\\"reboot\\\" if cli_type == \\\"klish\\\" else data.device_action\\n if data.device_action == \\\"reboot\\\":\\n reboot_type = data.reboot_type if \\\"reboot_type\\\" in data and data.reboot_type else \\\"normal\\\"\\n basic_obj.remove_file(dut, data.config_db_path)\\n st.reboot(dut, reboot_type, skip_port_wait=True)\\n st.wait_system_status(dut, 500)\\n elif data.device_action == \\\"run\\\":\\n ztp_operations(dut, data.device_action)\\n if \\\"band_type\\\" in data and data.band_type==\\\"inband\\\":\\n if not basic_obj.poll_for_system_status(dut):\\n st.log(\\\"Sytem is not ready ..\\\")\\n st.report_env_fail(\\\"system_not_ready\\\")\\n if not basic_obj.check_interface_status(dut, ztp_params.oob_port,\\\"up\\\"):\\n basic_obj.ifconfig_operation(dut, ztp_params.oob_port, \\\"down\\\")\\n interface_status = basic_obj.check_interface_status(dut, ztp_params.inband_port, \\\"up\\\")\\n if interface_status is not None:\\n if not interface_status:\\n intf_obj.interface_noshutdown(dut, ztp_params.inband_port, cli_type=cli_type)\\n if \\\"service\\\" in data and data.service == \\\"disable\\\":\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"stop\\\")\\n if basic_obj.verify_service_status(dut, \\\"ztp\\\"):\\n st.log(\\\"ZTP status is not stopped\\\")\\n st.report_fail(\\\"service_not_stopped\\\", \\\"ztp\\\")\\n basic_obj.service_operations_by_systemctl(dut, \\\"ztp\\\", \\\"start\\\")\\n if not poll_ztp_status(dut, [\\\"IN-PROGRESS\\\", \\\"Not Started\\\", \\\"SUCCESS\\\"], cli_type=cli_type):\\n st.report_fail(\\\"ztp_max_polling_interval\\\")\\n if \\\"check\\\" in data and data.check == \\\"not\\\":\\n if verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n else:\\n st.log(\\\"Iteration count {}\\\".format(iteration))\\n st.log(\\\"REBOOT ON SUCCESS - {}\\\".format(reboot_on_success))\\n if reboot_on_success:\\n if \\\"configdb-json\\\" in reboot_on_success:\\n st.wait_system_reboot(dut)\\n st.wait_system_status(dut, 300)\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=reboot_on_success, cli_type=cli_type)\\n else:\\n result = verify_ztp_status(dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\\n if not result:\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n if reboot_on_success:\\n output = show_ztp_status(dut, cli_type=cli_type)\\n if output[\\\"status\\\"] != \\\"SUCCESS\\\":\\n st.wait(300, \\\"Waiting for device to reboot after success...\\\")\\n st.wait_system_status(dut, 300)\\n # st.wait_system_reboot(dut)\\n if not verify_ztp_status(dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in data and \\\"func_name\\\" in data:\\n capture_syslogs(dut, data.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n st.banner(boot_up_obj.sonic_installer_list(dut))\\n verify_ztp_filename_logs(dut, data)\\n if \\\"ztp_log_string\\\" in data and data.ztp_log_string:\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path, data.ztp_log_string))\\n if not basic_obj.poll_for_error_logs(dut, data.ztp_log_path_1, data.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(data.ztp_log_path_1, data.ztp_log_string))\\n st.report_fail(\\\"ztp_log_verification_failed\\\", data.ztp_log_path_1, data.ztp_log_string)\\n if \\\"result\\\" in data and data.result == \\\"pass\\\":\\n st.report_pass(\\\"test_case_passed\\\")\",\n \"def update_cmts_isc_dhcp_config(self, board_config):\\n self.setup_dhcp_config(board_config)\\n self.setup_dhcp6_config(board_config)\\n raise NotImplementedError\",\n \"def main():\\n parser = ArgumentParser()\\n parser.add_argument(\\\"router_id\\\", help=\\\"id of the router\\\")\\n parser.add_argument(\\\"port_no\\\", help=\\\"port no. at which the\\\\\\n router is listening\\\", type=int)\\n parser.add_argument(\\\"router_config_file\\\", help=\\\"configuration\\\\\\n file for the router\\\")\\n args = parser.parse_args()\\n DATA[\\\"port\\\"] = args.port_no\\n DATA[\\\"router_id\\\"] = args.router_id\\n read_config_file(args.router_config_file)\\n\\n # after the read_config_file func completes we set a variable\\n # saying that neighbor data is ready\\n global READ_CONFIG_COMP\\n READ_CONFIG_COMP = True\\n\\n initial_dvec_and_forw_insert()\\n\\n SOCKET1.bind((\\\"\\\", DATA[\\\"port\\\"])) # converts the port to a listening state\\n # print(DATA['neighbor'])\\n\\n # read thread is listening for incoming messages\\n recv_th = Thread(target=recving)\\n recv_th.start()\\n\\n # sending thread sends its distance vector to its direct neighbors\\n send_th = Thread(target=sending)\\n send_th.start()\\n\\n # link cost change interface thread\\n intf_th = Thread(target=interface_thread, args=(\\n args.router_config_file, ), daemon=True)\\n intf_th.start()\\n\\n # find thread is checking if every router is available/alive\\n find_th = Thread(target=check_if_alive, daemon=True)\\n find_th.start()\\n\\n # we don't need thread.join because all of our threads\\n # are non daemon threads\\n\\n # SOCKET1.close()\\n return\",\n \"def _dht_updater(self):\\n while True:\\n self.temp_previous = self.temp\\n self.humi_previous = self.humi\\n self.timelast = self.measurement_time\\n\\n # Download page sources\\n sockt = urllib.urlopen('http://' + self.ip_address + '/temp')\\n srct = sockt.read()\\n sockt.close()\\n # Set new measurement time\\n self.measurement_time = dt.datetime.now()\\n sockh = urllib.urlopen('http://' + self.ip_address + '/humidity')\\n srch = sockh.read()\\n sockh.close()\\n\\n # Split, strip strings and set values\\n tempf = srct.split(' ', 1)[1].strip(' F')\\n self.humi = int(srch.split(' ', 1)[1].strip('%'))\\n self.temp = (float(tempf) - 32) / 1.8 # Convert temperature to celcius\\n\\n # Calculate change values\\n self.temp_change = self.temp - self.temp_previous\\n self.humi_change = self.humi - self.humi_previous\\n\\n # Flag change\\n if (self.temp_change or self.humi_change) != 0:\\n self.has_changed = True\\n else:\\n self.has_changed = False\\n\\n time.sleep(2)\",\n \"def __init__(self, conf, logger=None):\\n self.logger = logger or SimpleLogger(conf).get_logger_object()\\n Daemon.__init__(self, conf, self.logger)\\n\\tlibraryUtils.OSDLoggerImpl(\\\"account-updater-monitoring\\\").initialize_logger()\\n create_recovery_file('account-updater-server')\\n self.conf = conf\\n self.__interval = int(conf.get('interval', 1800))\\n self.__ll_port = int(conf.get('llport', 61014))\\n self.__account_updater_port = int(\\\\\\n conf.get('account_updater_port', 61009))\\n self.__service_id = gethostname() + \\\"_\\\" + str(self.__ll_port) + \\\\\\n \\\"_account-updater-server\\\"\\n self.__param = self.__get_param(conf)\\n self.msg = GlobalVariables(self.logger)\\n self.msg.set_service_id(self.__service_id)\\n self.walker_map = WalkerMap()\\n self.reader_map = ReaderMap()\\n\\n # Start sending health to local leader\\n self.logger.info(\\\"Loading health monitoring library\\\")\\n self.health_instance = healthMonitoring(self.__get_node_ip\\\\\\n (gethostname()), self.__account_updater_port, \\\\\\n self.__ll_port, self.__service_id)\\n self.logger.info(\\\"Loaded health monitoring library\\\") \\n remove_recovery_file('account-updater-server')\\n\\n\\t# load global map\\n if not self.msg.load_gl_map():\\n sys.exit(130)\\n self.logger.info(\\\"Account updater started\\\")\",\n \"def update_worker():\\n from test import get_remote_runner\\n runner = get_remote_runner()\\n runner.run(\\\"python2.7 /vagrant/bootstrap_lxc_manager.py --update_only=True\\\")\",\n \"def run(self):\\n self.stopped = False\\n # receives incoming 'host up' requests\\n serverSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)\\n serverSocket.bind(('255.255.255.255', Globals.BROADCAST_PORT))\\n \\n # wait for UDP broadcast, send TCP ACK\\n while 1:\\n \\n # open a socket and listen for a message\\n value,address = serverSocket.recvfrom(256)\\n host,port = address\\n \\n # this actually prevents a seg fault ;( for some reason\\n if self.stopped:\\n return\\n \\n if value == 'host up':\\n \\n sendSocket = socket.socket(socket.AF_INET, \\n socket.SOCK_STREAM, 0)\\n sendSocket.connect((host, Globals.ACK_PORT))\\n sendSocket.send('host up ack')\\n sendSocket.close()\\n sendSocket = None\\n self._addHost(host)\\n \\n elif value.find('host down') == 0:\\n self._removeHost(host)\\n \\n elif value.find('add group') == 0:\\n self._postEvent(value)\\n \\n elif value.find('remove group') == 0:\\n self._postEvent(value)\\n \\n elif value.find('group beat') == 0:\\n self._postEvent(value)\\n \\n serverSocket.close()\",\n \"def start(interval: int, devices_list: list):\\n t = threading.Thread(target=start_monitoring, args=(interval, devices_list))\\n t.start()\\n with lock:\\n global running\\n running = True\",\n \"def dhcp_utilization(self, dhcp_utilization):\\n\\n self._dhcp_utilization = dhcp_utilization\",\n \"def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\\n self._ifname = ifname\\n self._slave_dhcp_process = None\\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\\n self._new_lease_event = threading.Event() # At initialisation, event is cleared\",\n \"def do_heartbeat(self):\\n try:\\n self.api.heartbeat(\\n uuid=self.agent.get_node_uuid(),\\n advertise_address=self.agent.advertise_address\\n )\\n self.error_delay = self.initial_delay\\n self.log.info('heartbeat successful')\\n except Exception:\\n self.log.exception('error sending heartbeat')\\n self.error_delay = min(self.error_delay * self.backoff_factor,\\n self.max_delay)\",\n \"def ScheduleUpdate(self):\\r\\n self.__fCachedFiles = None\\r\\n if self._timeout_id is not None:\\r\\n return\\r\\n app = wingapi.gApplication\\r\\n self._timeout_id = app.InstallTimeout(1000, self.__CB_DoUpdate)\",\n \"def execute(timeSync):\\r\\n while True:\\r\\n runSched(timeSync)\\r\\n # Syncdb()\\r\",\n \"def dns_sync(self, args):\\r\\n dns = DNSManager(self.client)\\r\\n vsi = VSManager(self.client)\\r\\n\\r\\n vs_id = resolve_id(vsi.resolve_ids, args.get('<identifier>'), 'VS')\\r\\n instance = vsi.get_instance(vs_id)\\r\\n zone_id = resolve_id(dns.resolve_ids, instance['domain'], name='zone')\\r\\n\\r\\n def sync_a_record():\\r\\n \\\"\\\"\\\" Sync A record \\\"\\\"\\\"\\r\\n records = dns.get_records(\\r\\n zone_id,\\r\\n host=instance['hostname'],\\r\\n )\\r\\n\\r\\n if not records:\\r\\n # don't have a record, lets add one to the base zone\\r\\n dns.create_record(\\r\\n zone['id'],\\r\\n instance['hostname'],\\r\\n 'a',\\r\\n instance['primaryIpAddress'],\\r\\n ttl=args['--ttl'])\\r\\n else:\\r\\n recs = [x for x in records if x['type'].lower() == 'a']\\r\\n if len(recs) != 1:\\r\\n raise CLIAbort(\\\"Aborting A record sync, found %d \\\"\\r\\n \\\"A record exists!\\\" % len(recs))\\r\\n rec = recs[0]\\r\\n rec['data'] = instance['primaryIpAddress']\\r\\n rec['ttl'] = args['--ttl']\\r\\n dns.edit_record(rec)\\r\\n\\r\\n def sync_ptr_record():\\r\\n \\\"\\\"\\\" Sync PTR record \\\"\\\"\\\"\\r\\n host_rec = instance['primaryIpAddress'].split('.')[-1]\\r\\n ptr_domains = self.client['Virtual_Guest'].\\\\\\r\\n getReverseDomainRecords(id=instance['id'])[0]\\r\\n edit_ptr = None\\r\\n for ptr in ptr_domains['resourceRecords']:\\r\\n if ptr['host'] == host_rec:\\r\\n ptr['ttl'] = args['--ttl']\\r\\n edit_ptr = ptr\\r\\n break\\r\\n\\r\\n if edit_ptr:\\r\\n edit_ptr['data'] = instance['fullyQualifiedDomainName']\\r\\n dns.edit_record(edit_ptr)\\r\\n else:\\r\\n dns.create_record(\\r\\n ptr_domains['id'],\\r\\n host_rec,\\r\\n 'ptr',\\r\\n instance['fullyQualifiedDomainName'],\\r\\n ttl=args['--ttl'])\\r\\n\\r\\n if not instance['primaryIpAddress']:\\r\\n raise CLIAbort('No primary IP address associated with this VS')\\r\\n\\r\\n zone = dns.get_zone(zone_id)\\r\\n\\r\\n go_for_it = args['--really'] or confirm(\\r\\n \\\"Attempt to update DNS records for %s\\\"\\r\\n % instance['fullyQualifiedDomainName'])\\r\\n\\r\\n if not go_for_it:\\r\\n raise CLIAbort(\\\"Aborting DNS sync\\\")\\r\\n\\r\\n both = False\\r\\n if not args['--ptr'] and not args['-a']:\\r\\n both = True\\r\\n\\r\\n if both or args['-a']:\\r\\n sync_a_record()\\r\\n\\r\\n if both or args['--ptr']:\\r\\n sync_ptr_record()\",\n \"def activate(self):\\n super(Pfsense, self).activate()\\n default_identifier = self.config.get('DEFAULT_IDENTIFIER_STR', '')\\n if not default_identifier:\\n self.log.warn('No default identifier set')\\n\\n self.default_identifier = self.build_identifier(default_identifier)\\n self.thread = threading.Thread(target=log_thread, args=(self,))\\n\\n self.dns_cache = DNSCache()\\n self.dns_cache.start()\\n self.running = False\",\n \"def exchange_timestamps_thread(other_uuid: str, other_ip: str, other_tcp_port: int):\\n print_yellow(f\\\"ATTEMPTING TO CONNECT TO {other_uuid}\\\")\\n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n try:\\n client.connect((other_ip, other_tcp_port))\\n data = client.recv(1024)\\n except ConnectionError:\\n print_red(\\\"connection refused\\\") \\n exit()\\n timestamp = struct.unpack(\\\"!d\\\", data)\\n # calculate delay\\n my_timestamp = datetime.datetime.utcnow().timestamp()\\n delay = my_timestamp - timestamp[0]\\n\\n if other_uuid in neighbor_information:\\n neighbor_information[other_uuid].delay = delay\\n # neighbor_information[other_uuid].last_timestamp = timestamp[0]\\n\\n else:\\n neighbour_node = NeighborInfo(delay, other_ip, other_tcp_port)\\n neighbor_information.update({other_uuid: neighbour_node})\\n\\n client.close()\",\n \"def run(self):\\n \\n # Loop through all checkers to do an initial state check\\n for checker in self.checkers:\\n checker.update_last_state()\\n\\n # Send initial heartbeat\\n self._send_heartbeat()\\n \\n # Main loop\\n while True: \\n html = \\\"\\\"\\n for checker in self.checkers:\\n if checker.just_changed_state():\\n log.warn(\\\"Checker {} has changed state.\\\"\\n .format(checker.name))\\n html += \\\"<li>\\\" + checker.html() + \\\"</li>\\\\n\\\"\\n \\n if isinstance(checker, Process) and checker.state() == FAIL:\\n log.warn(\\\"Process {} is not running.\\\"\\n .format(checker.name))\\n html += (\\\"<li>Attempting to restart \\\" + \\n escape(checker.name) + \\\"...</li>\\\\n\\\")\\n try:\\n checker.restart()\\n except MaxRetriesError, e:\\n self.shutdown_reason = str(e)\\n return\\n time.sleep(5)\\n html += (\\\"<li>State after restart: \\\" + \\n checker.html() + \\\"</li>\\\\n\\\")\\n\\n if html:\\n html = \\\"<h2>STATE CHANGED:</h2>\\\\n<ul>\\\\n\\\" + html + \\\"</ul>\\\\n\\\" \\n html += self.html()\\n html += run_commands(self.state_change_cmds)\\n self.send_email_with_time(html=html,\\n subject=\\\"Babysitter detected\\\"\\n \\\" state change.\\\")\\n\\n if self._need_to_send_heartbeat():\\n self._send_heartbeat()\\n\\n # Check if a new data subdir has been created\\n if self.base_data_dir and self.sub_data_dir:\\n if self._find_last_numeric_subdir() != self.sub_data_dir:\\n self._send_heartbeat(\\\"<p>New subdir found so about to restart \\\"\\n \\\"babysitter. Below are the last stats \\\"\\n \\\"for the old data subdirectory.</p>\\\\n\\\")\\n raise NewDataDirError()\\n \\n time.sleep(UPDATE_PERIOD)\",\n \"def monitor(self):\\n if self.startup():\\n time.sleep(0.250)\\n self.run()\",\n \"def run():\\n logger.verbose(\\\"bwmon: Thread started\\\")\\n while True:\\n lock.wait()\\n logger.verbose(\\\"bwmon: Event received. Running.\\\")\\n database.db_lock.acquire()\\n nmdbcopy = copy.deepcopy(database.db)\\n database.db_lock.release()\\n try:\\n if getDefaults(nmdbcopy) and len(bwlimit.tc(\\\"class show dev %s\\\" % dev_default)) > 0:\\n # class show to check if net:InitNodeLimit:bwlimit.init has run.\\n sync(nmdbcopy)\\n else: logger.log(\\\"bwmon: BW limits DISABLED.\\\")\\n except: logger.log_exc(\\\"bwmon failed\\\")\\n lock.clear()\",\n \"def _async_update_callback(self):\\n self._async_update_zone_data()\\n self.async_write_ha_state()\",\n \"def _run(self):\\n #print(\\\"try to connect run\\\")\\n while True:\\n self._connect()\\n while not self.connected and self.auto_retry is not None:\\n gevent.sleep(self.auto_retry)\\n self._connect()\\n if self.connected:\\n self.run()\\n if self.auto_retry is None:\\n break\",\n \"def initial_sync_thread(self, engine_id):\\n\\n while True:\\n # Catch exceptions so the thread does not die.\\n try:\\n eventlet.greenthread.sleep(SYNC_INTERVAL)\\n self._initial_sync_subclouds(engine_id)\\n except eventlet.greenlet.GreenletExit:\\n # We have been told to exit\\n return\\n except Exception as e:\\n LOG.exception(e)\",\n \"def postStartup(self):\\n daemon = component.getUtility(interfaces.ICollector)\\n task = NmapPingTask(\\n \\\"NmapPingTask\\\", \\\"NmapPingTask\\\", taskConfig=daemon._prefs\\n )\\n # introduce a small delay to can have a chance to load some config\\n task.startDelay = 5\\n daemon._scheduler.addTask(task)\\n if not daemon.options.disableCorrelator:\\n correlationBackend = daemon.options.correlationBackend\\n task._correlate = component.getUtility(\\n IPingTaskCorrelator, correlationBackend\\n )\\n task.disable_correlator = daemon.options.disableCorrelator\",\n \"async def test_aiodiscover_finds_new_hosts_after_interval(hass: HomeAssistant) -> None:\\n with patch.object(hass.config_entries.flow, \\\"async_init\\\") as mock_init, patch(\\n \\\"homeassistant.components.dhcp.DiscoverHosts.async_discover\\\",\\n return_value=[],\\n ):\\n device_tracker_watcher = dhcp.NetworkWatcher(\\n hass,\\n {},\\n [{\\\"domain\\\": \\\"mock-domain\\\", \\\"hostname\\\": \\\"connect\\\", \\\"macaddress\\\": \\\"B8B7F1*\\\"}],\\n )\\n await device_tracker_watcher.async_start()\\n await hass.async_block_till_done()\\n\\n assert len(mock_init.mock_calls) == 0\\n\\n with patch.object(hass.config_entries.flow, \\\"async_init\\\") as mock_init, patch(\\n \\\"homeassistant.components.dhcp.DiscoverHosts.async_discover\\\",\\n return_value=[\\n {\\n dhcp.DISCOVERY_IP_ADDRESS: \\\"192.168.210.56\\\",\\n dhcp.DISCOVERY_HOSTNAME: \\\"connect\\\",\\n dhcp.DISCOVERY_MAC_ADDRESS: \\\"b8b7f16db533\\\",\\n }\\n ],\\n ):\\n async_fire_time_changed(hass, dt_util.utcnow() + datetime.timedelta(minutes=65))\\n await hass.async_block_till_done()\\n await device_tracker_watcher.async_stop()\\n await hass.async_block_till_done()\\n\\n assert len(mock_init.mock_calls) == 1\\n assert mock_init.mock_calls[0][1][0] == \\\"mock-domain\\\"\\n assert mock_init.mock_calls[0][2][\\\"context\\\"] == {\\n \\\"source\\\": config_entries.SOURCE_DHCP\\n }\\n assert mock_init.mock_calls[0][2][\\\"data\\\"] == dhcp.DhcpServiceInfo(\\n ip=\\\"192.168.210.56\\\",\\n hostname=\\\"connect\\\",\\n macaddress=\\\"b8b7f16db533\\\",\\n )\",\n \"def handleClient(self, connection, address):\\r\\n # time.sleep(5) #server Action\\r\\n while True:\\r\\n try:\\r\\n data = connection.recv(1024).decode(\\\"utf-8\\\")\\r\\n except:\\r\\n print('client disconnect: ', address, 'at', self.now())\\r\\n data = \\\"\\\"\\r\\n\\r\\n if not data: break\\r\\n\\r\\n data = self.change_host(data, address)\\r\\n result = self.manag_bd.dispatcher(data)\\r\\n\\r\\n mutex = thread.allocate_lock()\\r\\n\\r\\n\\r\\n if type(result)==type(list()):\\r\\n mutex.acquire() #Lock interrupt\\r\\n l = len(result)\\r\\n reply = str(l)\\r\\n connection.send(reply.encode(\\\"utf-8\\\"))\\r\\n for line in result:\\r\\n time.sleep(0.0025)\\r\\n reply = line\\r\\n connection.send(reply.encode(\\\"utf-8\\\"))\\r\\n mutex.release()# permission to interrupt\\r\\n else:\\r\\n reply = str(self.now())\\r\\n connection.send(reply.encode(\\\"utf-8\\\"))\\r\\n\\r\\n\\r\\n\\r\\n connection.close()\",\n \"def sync():\\n sync_ssda()\",\n \"def on_enter(self):\\n\\n Clock.schedule_once(partial(check_connection.is_alive,\\n self.ids[\\\"ico_connection\\\"]\\n )\\n )\\n self.check_connection = Clock.schedule_interval(partial(check_connection.is_alive,\\n self.ids[\\\"ico_connection\\\"]\\n ),\\n 5.0\\n )\",\n \"def start_daemon(interval=600):\\n thread = threading.Thread(target=daemon_job, args=(interval, ))\\n thread.daemon = True\\n thread.start()\",\n \"def __init__( self,\\\\\\n targetip,\\\\\\n gatewayip,\\\\\\n name='arpoison'\\n ):\\n self._stopevent = threading.Event( )\\n self._sleepperiod = 1.0\\n\\n threading.Thread.__init__(self, name=name)\\n\\n self.targetip = targetip\\n self.gatewayip = gatewayip\",\n \"def sync_loop(self):\\n\\n while True:\\n print(\\\"Sync loop\\\")\\n self.sync()\\n time.sleep(SYNC_TIME_OUT)\",\n \"def __init__(self, _log: logging.Logger, _config: dict = None, _devices_callback=None):\\n Thread.__init__(self)\\n self.command_lock = Lock()\\n self.log = _log\\n self.config = _config\\n self.available_devices_callback = _devices_callback\\n self.s_socket = None\\n self.on_config_change = None\\n self.is_running = False\\n self.config_hash = dict()\\n self.config_hash['version'] = self.__get_hashstr(self.config)\\n self.config_hash['al_working'] = 'none'\\n # self.is_socket_usable = True\\n self.log.debug(\\\"instantiate RadioCommandServer...\\\")\",\n \"def __work__(self):\\n while not self.is_done:\\n self.refreshSignal.emit()\\n time.sleep(0.05)\",\n \"async def _main(self):\\n while True:\\n time.sleep(1)\",\n \"def ping_daemon(self):\\n s = self.ping_interval\\n while True:\\n p = domintell.messages.Ping()\\n self.send(p)\\n time.sleep(s)\",\n \"def run(self):\\n while True:\\n # Do something\\n print('Doing something imporant in the background')\\n\\n self.loadData()\\n time.sleep(self.interval)\",\n \"def receive_broadcast_thread():\\n global neighbor_information\\n client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\\n client.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\\n client.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n client.bind(('', get_broadcast_port()))\\n\\n while True:\\n data, (ip, port) = client.recvfrom(4096)\\n data_v2 = data.decode(\\\"utf-8\\\")\\n parsed = data_v2.split(\\\" \\\")\\n print(parsed)\\n newnodeflag=checkIfsameNode(parsed)\\n\\n if(newnodeflag == 1):\\n tcpClient=daemon_thread_builder(exchange_timestamps_thread,args=(parsed[0],\\\"127.0.0.1\\\",parsed[2],))\\n tcpClient.start() \\n tcpClient.join()\\n elif(newnodeflag == 2):\\n x = neighbor_information.get(parsed[0])\\n neighbor_information.update({parsed[0]:NeighborInfo(x.delay,x.last_timestamp,x.broadcast_count+1)})\\n if (x.broadcast_count == 9): \\n neighbor_information.pop(parsed[0])\\n\\n print_blue(f\\\"RECV: {data} FROM: {ip}:{port}\\\")\",\n \"def main():\\n populate_satellites_array()\\n latitude = float(os.environ['LATITUDE'])\\n longitude = float(os.environ['LONGITUDE'])\\n radius = int(os.environ['RADIUS'])\\n timeout = 1\\n previous_satellites = []\\n while True:\\n if (last_updated[0] + 86400) < int(time.time()):\\n print('Expired data, updating from spacetrack')\\n cron_refresh_spacetrack_cache()\\n populate_satellites_array()\\n print('Checking {}, {}'.format(latitude, longitude))\\n currently_overhead = get_overhead_satellites_dicts(latitude, longitude, radius)\\n for sat in currently_overhead:\\n if not sat['name'] in previous_satellites:\\n announce_satellite(sat)\\n previous_satellites = [x['name'] for x in currently_overhead]\\n time.sleep(timeout)\",\n \"def start(self):\\n gevent.spawn_later(self._period, self._run)\",\n \"def start(self):\\r\\n t = Thread(target=self.update, args=())\\r\\n t.daemon = True\\r\\n t.start()\\r\\n return self\",\n \"def start(self):\\r\\n t = Thread(target=self.update, args=())\\r\\n t.daemon = True\\r\\n t.start()\\r\\n return self\",\n \"async def _autoconfigure_run(self):\\n self.logger.info(\\\"cellular auto configuration\\\")\\n try:\\n # configure the SkyCtrl to use this apc token for the cellular connection.\\n await self._aconfigure()\\n except (HTTPError, TimeoutError, RuntimeError) as e:\\n # raises an autoconfigure failure Event\\n event = CellularAutoconfigureFailureEvent(exception=e)\\n self.logger.info(str(event))\\n self._controller.scheduler.process_event(event)\",\n \"def backgroundStart(): #Background checks thread\\n global currentStatus\\n while True:\\n currentStatus = checkClassChanges()\\n time.sleep(10)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6033633","0.559053","0.5557032","0.55510986","0.5474557","0.5411881","0.5409579","0.5318271","0.53177303","0.5296048","0.5247797","0.5238221","0.5237003","0.5179221","0.5164641","0.5161137","0.515453","0.5152708","0.51398647","0.5128912","0.51281554","0.50753385","0.50750446","0.5062125","0.50621116","0.5046356","0.50461763","0.50344646","0.50159615","0.5013876","0.5006799","0.49915683","0.4979255","0.4976592","0.49736726","0.49559996","0.49559242","0.49478287","0.4947695","0.49452746","0.49395126","0.49353424","0.49227744","0.49143225","0.49130508","0.49130362","0.49119067","0.49079198","0.49015063","0.48960105","0.4894955","0.48866287","0.48588997","0.48515114","0.4851506","0.48075682","0.47890127","0.47860754","0.47818416","0.47725257","0.4765562","0.47640562","0.4762251","0.47592586","0.4757376","0.4757066","0.47563037","0.47520703","0.4748653","0.4741464","0.47358304","0.47357932","0.47351664","0.47257552","0.47248638","0.4723265","0.4718525","0.47130483","0.47068888","0.47065917","0.4705076","0.47048268","0.47003347","0.46965212","0.46937773","0.46928495","0.46920696","0.46911526","0.46903634","0.46857545","0.4685239","0.46758565","0.46696296","0.46696264","0.46691856","0.46645197","0.4664453","0.4664453","0.46498924","0.46478194"],"string":"[\n \"0.6033633\",\n \"0.559053\",\n \"0.5557032\",\n \"0.55510986\",\n \"0.5474557\",\n \"0.5411881\",\n \"0.5409579\",\n \"0.5318271\",\n \"0.53177303\",\n \"0.5296048\",\n \"0.5247797\",\n \"0.5238221\",\n \"0.5237003\",\n \"0.5179221\",\n \"0.5164641\",\n \"0.5161137\",\n \"0.515453\",\n \"0.5152708\",\n \"0.51398647\",\n \"0.5128912\",\n \"0.51281554\",\n \"0.50753385\",\n \"0.50750446\",\n \"0.5062125\",\n \"0.50621116\",\n \"0.5046356\",\n \"0.50461763\",\n \"0.50344646\",\n \"0.50159615\",\n \"0.5013876\",\n \"0.5006799\",\n \"0.49915683\",\n \"0.4979255\",\n \"0.4976592\",\n \"0.49736726\",\n \"0.49559996\",\n \"0.49559242\",\n \"0.49478287\",\n \"0.4947695\",\n \"0.49452746\",\n \"0.49395126\",\n \"0.49353424\",\n \"0.49227744\",\n \"0.49143225\",\n \"0.49130508\",\n \"0.49130362\",\n \"0.49119067\",\n \"0.49079198\",\n \"0.49015063\",\n \"0.48960105\",\n \"0.4894955\",\n \"0.48866287\",\n \"0.48588997\",\n \"0.48515114\",\n \"0.4851506\",\n \"0.48075682\",\n \"0.47890127\",\n \"0.47860754\",\n \"0.47818416\",\n \"0.47725257\",\n \"0.4765562\",\n \"0.47640562\",\n \"0.4762251\",\n \"0.47592586\",\n \"0.4757376\",\n \"0.4757066\",\n \"0.47563037\",\n \"0.47520703\",\n \"0.4748653\",\n \"0.4741464\",\n \"0.47358304\",\n \"0.47357932\",\n \"0.47351664\",\n \"0.47257552\",\n \"0.47248638\",\n \"0.4723265\",\n \"0.4718525\",\n \"0.47130483\",\n \"0.47068888\",\n \"0.47065917\",\n \"0.4705076\",\n \"0.47048268\",\n \"0.47003347\",\n \"0.46965212\",\n \"0.46937773\",\n \"0.46928495\",\n \"0.46920696\",\n \"0.46911526\",\n \"0.46903634\",\n \"0.46857545\",\n \"0.4685239\",\n \"0.46758565\",\n \"0.46696296\",\n \"0.46696264\",\n \"0.46691856\",\n \"0.46645197\",\n \"0.4664453\",\n \"0.4664453\",\n \"0.46498924\",\n \"0.46478194\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.50748193"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":79787,"cells":{"query":{"kind":"string","value":"Enable DHCP for a network that meets enabling criteria."},"document":{"kind":"string","value":"def enable_dhcp_helper(self, network_id):\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n if not network.admin_state_up:\n return\n\n for subnet in network.subnets:\n if subnet.enable_dhcp:\n if self.call_driver('enable', network):\n self.cache.put(network)\n break"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def enable_network_management(self):\n self._request({\"enable-network-management\": True})","def elAddNetworkConfigurationWithDhcp(self, device):\n commandSection = self.sectionByName(\"command\")\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\n deviceMatch = re.match(r\"([^0-9]+)([0-9])\", device)\n if deviceMatch:\n # e.g. \"eth0\"\n devicePrefix = deviceMatch.group(1)\n deviceNumber = deviceMatch.group(2)\n deviceNumber = int(deviceNumber)\n for i in range(8, deviceNumber - 1, -1):\n deviceI = devicePrefix + str(i)\n deviceIPlus1 = devicePrefix + str(i + 1)\n # move up by one device each network configuration\n commandSection.string = re.sub(r\"(?m)^([ \\t]*network[ \\t]+.*--device[ \\t]*(?:=|[ \\t])[ \\t]*)\" + re.escape(deviceI) + r\"(.*)$\",\n r\"\\g<1>\" + deviceIPlus1 + r\"\\g<2>\",\n commandSection.string)\n # not --noipv6\n networkConfiguration = \"network --device=\" + device + \" --bootproto=dhcp --onboot=yes --activate\"\n if deviceMatch and deviceNumber == 0:\n # having configuration of eth0 first appears to be more conducive to overall success,\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\n # \"... in installer environment. Device of the first network command is activated if network is required,\n # e.g. in case of network installation ...\",\n commandSection.string = networkConfiguration + \"\\n\" \\\n + \"#\\n\" \\\n + commandSection.string\n else:\n commandSection.string = commandSection.string \\\n + \"#\\n\" \\\n + networkConfiguration + \"\\n\"","def network_create_end(self, payload):\n network_id = payload['network']['id']\n self.enable_dhcp_helper(network_id)","def enable(self):\n # Netmiko reports enable and config mode as being enabled\n if not self.native.check_enable_mode():\n self.native.enable()\n # Ensure device is not in config mode\n if self.native.check_config_mode():\n self.native.exit_config_mode()\n\n log.debug(\"Host %s: Device enabled.\", self.host)","async def enable_protection(self) -> None:\n await self._request(\n \"dns_config\", method=\"POST\", json_data={\"protection_enabled\": True},\n )","def test_set_enabled(self):\n networktables_mock = unittest.mock.Mock()\n table_mock = unittest.mock.Mock()\n networktables_mock.getTable.return_value = table_mock\n\n network_instance = network.Network(networktables_mock, None, None)\n\n network_instance.set_enabled(True)\n table_mock.putBoolean.assert_called_with(\"/enabled\", True)\n\n network_instance.set_enabled(False)\n table_mock.putBoolean.assert_called_with(\"/enabled\", False)","def enable(ctx):\n\n config_db = ConfigDBConnector()\n config_db.connect()\n config_db.mod_entry(\"NAT_GLOBAL\", \"Values\", {\"admin_mode\": \"enabled\"})","def _set_rules_pxeboot(self, gnp_config, network, host):\n\n addr_pool = self.dbapi.address_pool_get(network.pool_uuid)\n ip_version = IPAddress(f\"{addr_pool.network}\").version\n self._add_source_net_filter(gnp_config[\"spec\"][\"ingress\"],\n f\"{addr_pool.network}/{addr_pool.prefix}\")\n if (ip_version == 6):\n self._add_source_net_filter(gnp_config[\"spec\"][\"ingress\"], LINK_LOCAL)\n if (ip_version == 4):\n # add rule to allow DHCP requests (dhcp-offer have src addr == 0.0.0.0)\n rule = self._get_dhcp_rule(host.personality, \"UDP\", ip_version)\n gnp_config[\"spec\"][\"ingress\"].append(rule)","def set_configure_with_dhcp(self, bConfigureWithDhcp):\n\t\tcall_sdk_function('PrlVmDevNet_SetConfigureWithDhcp', self.handle, bConfigureWithDhcp)","def set_dhcpserver_enabled(self, bEnabled):\n\t\tcall_sdk_function('PrlVirtNet_SetDHCPServerEnabled', self.handle, bEnabled)","def set_dhcp_conn(nic):\n nic.EnableDHCP()\n # After static DNS servers are specified to start using Dynamic Host\n # Configuration Protocol (DHCP) instead of static DNS servers,\n # you can call the method without supplying \"in\" parameters.\n nic.SetDNSServerSearchOrder()","async def async_turn_on(self):\n path = \"/ip/firewall/nat\"\n param = \".id\"\n value = None\n for uid in self._ctrl.data[\"nat\"]:\n if (\n self._ctrl.data[\"nat\"][uid][\"name\"]\n == f\"{self._data['protocol']}:{self._data['dst-port']}\"\n ):\n value = self._ctrl.data[\"nat\"][uid][\".id\"]\n\n mod_param = \"disabled\"\n mod_value = False\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n await self._ctrl.force_update()","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def is_configure_with_dhcp(self):\n\t\treturn bool(call_sdk_function('PrlVmDevNet_IsConfigureWithDhcp', self.handle))","def set_network_security_policy(options):\n network = get_network_on_vc(options)\n name = get_network_name(options)\n\n config_spec = Vim.Dvs.DistributedVirtualPortgroup.ConfigSpec()\n config_info = network.GetConfig()\n config_spec.description = config_info.name\n config_spec.name = name\n config_spec.configVersion = config_info.configVersion\n\n true_policy = Vim.BoolPolicy()\n true_policy.value = True\n dvs_port_setting = Vim.VMwareDVSPortSetting()\n security_policy = Vim.DVSSecurityPolicy()\n security_policy.allowPromiscuous = true_policy\n security_policy.forgedTransmits = true_policy\n security_policy.macChanges = true_policy\n security_policy.inherited = False\n dvs_port_setting.securityPolicy = security_policy\n config_spec.defaultPortConfig = dvs_port_setting\n\n network.ReconfigureDVPortgroup_Task(config_spec)","def setup_dhcp_config(self, board_config):\n raise NotImplementedError","def enableNatlink(self, silent=None):\n if not self.isElevated: raise ElevationError(\"needed for enabling Natlink\")\n # if self.isNatSpeakRunning(): raise NatSpeakRunningError(\"Probably needed for enabling Natlink\")\n\n self.registerNatlinkPyd(silent=1)\n nssystemini = self.getNSSYSTEMIni()\n section1 = self.section1\n key1 = self.key1\n value1 = self.value1\n #\n try:\n win32api.WriteProfileVal(section1, key1, value1, nssystemini)\n except pywintypes.error as details:\n if details[0] == 5:\n print('cannot enable Natlink (1), you probably need administrator rights')\n else:\n print('unexpected error at enable Natlink (1)')\n raise\n\n result = self.NatlinkIsEnabled(silent=1)\n if (result is None) or (not result):\n nsappsini = self.getNSAPPSIni()\n section2 = self.section2\n key2 = self.key2\n value2 = self.value2\n try:\n win32api.WriteProfileVal(section2, key2, value2, nsappsini)\n except pywintypes.error as details:\n if details[0] == 5:\n print('cannot enable Natlink (2), you probably need administrator rights')\n else:\n print('unexpected error at enable Natlink (2)')\n raise\n result = self.NatlinkIsEnabled(silent=1)\n if result == None:\n text = \\\n\"\"\"Cannot set the nsapps.ini setting in order to complete enableNatlink.\n\nProbably you did not run this program in \"elevated mode\". Please try to do so.\n\"\"\"\n self.warning(text)\n return\n result = self.NatlinkIsEnabled(silent=1)\n if result:\n if not silent:\n print(('Natlink enabled, you can now restart %s'% self.DNSName))\n else:\n if not silent:\n self.warning(\"failed to enable Natlink\")","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def commissioning_wifi(devCtrl, ssid, password, nodeId):\n\n # Inject the credentials to the device\n err, res = send_zcl_command(devCtrl,\n \"NetworkCommissioning \"\n \"AddOrUpdateWiFiNetwork {} 0 0 \"\n \"ssid=str:{} credentials=str:{} breadcrumb=0 timeoutMs=1000\".format(\n nodeId,\n ssid,\n password\n ))\n if err != 0 and res[\"Status\"] != 0:\n log.error(\"Set Wi-Fi credentials failed [{}]\".format(err))\n return err\n\n # Enable the Wi-Fi interface\n err, res = send_zcl_command(\n devCtrl, \"NetworkCommissioning ConnectNetwork {} 0 0 networkID=str:{} breadcrumb=0 timeoutMs=1000\".format(nodeId, ssid))\n if err != 0 and res[\"Status\"] != 0:\n log.error(\"Enable Wi-Fi failed [{}]\".format(err))\n return err\n\n return err","def is_configure_with_dhcp(self):\n\t\treturn bool(call_sdk_function('PrlSrvCfgNet_IsConfigureWithDhcp', self.handle))","def refresh_dhcp_helper(self, network_id):\n old_network = self.cache.get_network_by_id(network_id)\n if not old_network:\n # DHCP current not running for network.\n return self.enable_dhcp_helper(network_id)\n\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\n\n if new_cidrs and old_cidrs == new_cidrs:\n self.call_driver('reload_allocations', network)\n self.cache.put(network)\n elif new_cidrs:\n if self.call_driver('restart', network):\n self.cache.put(network)\n else:\n self.disable_dhcp_helper(network.id)","def run(self, enable_snat=True, network_create_args=None,\n router_create_args=None):\n network_create_args = network_create_args or {}\n router_create_args = router_create_args or {}\n\n ext_net = self.neutron.create_network(**network_create_args)\n router = self.neutron.create_router(**router_create_args)\n self.neutron.add_gateway_to_router(router_id=router[\"id\"],\n network_id=ext_net[\"id\"],\n enable_snat=enable_snat)\n self.neutron.remove_gateway_from_router(router[\"id\"])","def disable_dhcp_helper(self, network_id):\n network = self.cache.get_network_by_id(network_id)\n if network:\n if self.call_driver('disable', network):\n self.cache.remove(network)","async def enable(self, **kwargs) -> None: # pylint: disable=unused-argument\r\n await self.set_ena(True)","def apply_network_settings(**settings):\n if 'require_reboot' not in settings:\n settings['require_reboot'] = False\n if 'apply_hostname' not in settings:\n settings['apply_hostname'] = False\n\n hostname_res = True\n if settings['apply_hostname'] in _CONFIG_TRUE:\n if 'hostname' in settings:\n hostname_res = __salt__['network.mod_hostname'](settings['hostname'])\n else:\n log.warning(\n 'The network state sls is trying to apply hostname '\n 'changes but no hostname is defined.'\n )\n hostname_res = False\n\n res = True\n if settings['require_reboot'] in _CONFIG_TRUE:\n log.warning(\n 'The network state sls is requiring a reboot of the system to '\n 'properly apply network configuration.'\n )\n res = True\n else:\n res = __salt__['cmd.run']('/etc/netstart restart')\n\n return hostname_res and res","def enable_vlan(self, mgr, vlanid, vlanname):\n confstr = snipp.CMD_VLAN_CONF_SNIPPET % (vlanid, vlanname)\n confstr = self.create_xml_snippet(confstr)\n mgr.edit_config(target='running', config=confstr)","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def set_static_conn(nic, ip_addr, subnet_mask, default_gateway, dns_servers):\n if isinstance(ip_addr, str):\n ip_addr = [ip_addr,]\n if isinstance(subnet_mask, str):\n subnet_mask = [subnet_mask,]\n if isinstance(default_gateway, str):\n default_gateway = [default_gateway, ]\n\n # set defult gateway. return value:\n # 0: success & no reboot required, \n # 1: sucess & reboot required\n ret = nic.SetGateways(default_gateway)\n print 'Default Gateway updated (status %d)' % ret\n\n # Set IP adrress & subnet mask. return value:\n # 0: success & no reboot required, \n # 1: sucess & reboot required\n ret = nic.EnableStatic(IPAddress=ip_addr, SubnetMask=subnet_mask)\n print 'IP Address / Subnet Mask updated (status %d)' % ret\n\n # set dns servers\n if dns_servers:\n #assert 0 == nic.EnableDNS(DNSServerSearchOrder=dns_servers)\n # or \n ret = nic.SetDNSServerSearchOrder(dns_servers)\n print 'DNS Server updated (status %d)' % ret","def auto_schedule_networks(self, plugin, context, host):\n agents_per_network = cfg.CONF.dhcp_agents_per_network\n # a list of (agent, net_ids) tuples\n bindings_to_add = []\n with context.session.begin(subtransactions=True):\n fields = ['network_id', 'enable_dhcp']\n subnets = plugin.get_subnets(context, fields=fields)\n net_ids = set(s['network_id'] for s in subnets\n if s['enable_dhcp'])\n if not net_ids:\n LOG.debug('No non-hosted networks')\n return False\n query = context.session.query(agents_db.Agent)\n query = query.filter(agents_db.Agent.agent_type ==\n constants.AGENT_TYPE_DHCP,\n agents_db.Agent.host == host,\n agents_db.Agent.admin_state_up == sql.true())\n dhcp_agents = query.all()\n for dhcp_agent in dhcp_agents:\n if agents_db.AgentDbMixin.is_agent_down(\n dhcp_agent.heartbeat_timestamp):\n LOG.warn(_LW('DHCP agent %s is not active'), dhcp_agent.id)\n continue\n for net_id in net_ids:\n agents = plugin.get_dhcp_agents_hosting_networks(\n context, [net_id], active=True)\n if len(agents) >= agents_per_network:\n continue\n if any(dhcp_agent.id == agent.id for agent in agents):\n continue\n bindings_to_add.append((dhcp_agent, net_id))\n # do it outside transaction so particular scheduling results don't\n # make other to fail\n for agent, net_id in bindings_to_add:\n self._schedule_bind_network(context, [agent], net_id)\n return True","def add_network(self, name_of_vm, port_group):\n adapter_type = 'e1000'\n vds = \"yes\"\n try:\n # import sys,pdb;pdb.Pdb(stdout=sys.__stdout__).set_trace()\n vmachine = self.vcenter.get_dc_object([vim.VirtualMachine], name_of_vm)\n\n if vds == 'yes':\n network = self.vcenter.get_dc_object([vim.dvs.DistributedVirtualPortgroup], port_group)\n else:\n network = self.get_network(port_group)\n\n new_nic = vim.vm.ConfigSpec()\n nic_changes = []\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.add\n if adapter_type == 'e1000':\n nic_spec.device = vim.vm.device.VirtualE1000()\n elif adapter_type == 'vmxnet2':\n nic_spec.device = vim.vm.device.VirtualVmxnet2()\n else:\n nic_spec.device = vim.vm.device.VirtualVmxnet3()\n nic_spec.device.deviceInfo = vim.Description()\n if vds == 'yes':\n vir_port = vim.vm.device.VirtualEthernetCard.DistributedVirtualPortBackingInfo()\n nic_spec.device.backing = vir_port\n dvs_port_connection = vim.dvs.PortConnection()\n dvs_port_connection.portgroupKey = network.key\n dvs_port_connection.switchUuid = network.config.distributedVirtualSwitch.uuid\n nic_spec.device.backing.port = dvs_port_connection\n else:\n nic_spec.device.backing = vim.vm.device.VirtualEthernetCard.NetworkBackingInfo()\n nic_spec.device.backing.useAutoDetect = False\n nic_spec.device.backing.network = network\n nic_spec.device.backing.deviceName = port_group\n nic_spec.device.connectable = vim.vm.device.VirtualDevice.ConnectInfo()\n nic_spec.device.connectable.startConnected = True\n nic_spec.device.connectable.connected = True\n nic_spec.device.connectable.allowGuestControl = True\n nic_spec.device.connectable.status = 'untried'\n nic_spec.device.wakeOnLanEnabled = True\n nic_spec.device.addressType = 'assigned'\n nic_changes.append(nic_spec)\n new_nic.deviceChange = nic_changes\n add_nic = vmachine.ReconfigVM_Task(spec=new_nic)\n log.info('Adding Network adapter to the VM...')\n while add_nic.info.state not in ['success', 'error']:\n time.sleep(1)\n status = add_nic.info.state\n if status == 'success':\n log.info('Nic added successfully: {}'.format(name_of_vm))\n if status == 'error':\n log.info('Could not add Network adapter {}'.format(name_of_vm))\n return status\n\n except Exception as error:\n log.info(\"Caught exception: {} \\n {}\".format(error, error.message))","def test_ap_hs20_network_preference(dev, apdev):\n bssid = apdev[0]['bssid']\n params = hs20_ap_params()\n hostapd.add_ap(apdev[0]['ifname'], params)\n\n dev[0].hs20_enable()\n values = { 'realm': \"example.com\",\n 'username': \"hs20-test\",\n 'password': \"password\",\n 'domain': \"example.com\" }\n dev[0].add_cred_values(values)\n\n id = dev[0].add_network()\n dev[0].set_network_quoted(id, \"ssid\", \"home\")\n dev[0].set_network_quoted(id, \"psk\", \"12345678\")\n dev[0].set_network(id, \"priority\", \"1\")\n dev[0].request(\"ENABLE_NETWORK %s no-connect\" % id)\n\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\"], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if bssid not in ev:\n raise Exception(\"Unexpected network selected\")\n\n bssid2 = apdev[1]['bssid']\n params = hostapd.wpa2_params(ssid=\"home\", passphrase=\"12345678\")\n hostapd.add_ap(apdev[1]['ifname'], params)\n\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\",\n \"INTERWORKING-ALREADY-CONNECTED\" ], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if \"INTERWORKING-ALREADY-CONNECTED\" in ev:\n raise Exception(\"No roam to higher priority network\")\n if bssid2 not in ev:\n raise Exception(\"Unexpected network selected\")","def enableAddressLayer(self):\n\n self._deladdressaction.setEnabled(True)\n self._createnewaddressaction.setEnabled(True)\n self._moveaddressaction.setEnabled(True)\n self._updateaddressaction.setEnabled(True)\n self._highlightaction.setEnabled(True)","def enable_dns_management(self):\n self._request({\"enable-dns-management\": True})","def ethernet_on(self):\n if not self.healthy:\n self.health_check()\n if not self._ethernet_switch:\n raise errors.CapabilityNotReadyError(\n device_name=self._device_name,\n msg=\"Not set up for ethernet switching.\")\n self._ethernet_switch.switch_power.power_on(self.ethernet_port_number)","def enable_ad():\n for host in online_hosts:\n with settings(warn_only=True):\n\n result1 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgActiveDirectory -o cfgADEnable 1\")\n if result1.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Enabling Active Directory failed \")\n\n result2 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgActiveDirectory -o cfgADType 2\")\n if result2.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Setting of standard schema for AD failed \")","def enable():\n request = dict(id='gbn')\n _gbn_enable(request)","def AddNetworkFlag(parser):\n help_text = \"\"\"\\\n The VPC network from which the AlloyDB instance is accessible via private\n IP. For example, projects/myProject/global/networks/default. This setting\n cannot be updated after it is set.\n \"\"\"\n parser.add_argument('--network', help=help_text)","def update_network(**kwargs):\n\n ip_addr = kwargs.get('ip_addr')\n is_private = kwargs.get('is_private')\n name = kwargs.get('name')\n dns_names = kwargs.get('dns_names')\n is_scanning = kwargs.get('is_scanning', False)\n network_id = make_shortuuid(name)\n\n network = {\n 'dns_names': dns_names,\n 'ip_addr': ip_addr,\n 'is_private' : is_private,\n 'name': name,\n 'id': network_id,\n 'is_scanning': is_scanning,\n 'updated_count': 0\n\n }\n\n network_exists = r.table(\"networks\").insert([network], conflict=\"update\")\n\n return network_exists.run(conn)","def add_network_adapter(self, network_obj):\n\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.add\n nic_spec.device = vim.vm.device.VirtualVmxnet3()\n nic_spec.device.wakeOnLanEnabled = True\n nic_spec.device.addressType = \"assigned\"\n nic_spec.device.deviceInfo = vim.Description()\n nic_spec.device.backing = vim.vm.device.VirtualEthernetCard.NetworkBackingInfo()\n nic_spec.device.backing.network = network_obj\n nic_spec.device.backing.deviceName = network_obj.name\n nic_spec.device.backing.useAutoDetect = False\n nic_spec.device.connectable = vim.vm.device.VirtualDevice.ConnectInfo()\n nic_spec.device.connectable.startConnected = True\n nic_spec.device.connectable.connected = True\n nic_spec.device.connectable.allowGuestControl = True\n config_spec = vim.vm.ConfigSpec()\n config_spec.deviceChange = [nic_spec]\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)","def networkMode(self, networkMode):\n\n # Setting the network mode can take a bit of time, so give it 10 seconds\n # to finish\n response = self.at.sendCommand(f\"AT+CFUN={networkMode}\", timeout = 10)\n\n if not response:\n raise modem.AtError(response, \"Failed to set network mode\")","def enable(self):\n try:\n self.bus.open(self.BUS_NUMBER)\n self.write(AntennaDeployerCommand.ARM_ANTS, 0x00)\n self.bus.close()\n return True\n except:\n return False","def enable_charge_pump(enable):\n send_command(0x8D)\n if enable:\n send_command(0x14)\n else:\n send_command(0x10)","def enable_gateway(self, enable_gateway):\n\n self._enable_gateway = enable_gateway","def test_enable_dpdk(self):\n logging.info('Pre-flight check')\n self._dpdk_pre_post_flight_check()\n self._ovs_br_ex_port_is_system_interface()\n\n self.enable_hugepages_vfio_on_hvs_in_vms(4)\n with self.config_change(\n {\n 'enable-dpdk': False,\n 'dpdk-driver': '',\n },\n {\n 'enable-dpdk': True,\n 'dpdk-driver': 'vfio-pci',\n },\n application_name='ovn-chassis'):\n logging.info('Checking openvswitch-switch-dpdk is installed')\n self._openvswitch_switch_dpdk_installed()\n logging.info('Checking DPDK is configured in OVS')\n self._ovs_dpdk_init_configured()\n logging.info('Checking DPDK is successfully initialized in OVS')\n self._ovs_dpdk_initialized()\n logging.info('Checking that br-ex configed with DPDK interface...')\n self._ovs_br_ex_port_is_dpdk_interface()\n logging.info('and is not in error.')\n self._ovs_br_ex_interface_not_in_error()\n\n logging.info('Post-flight check')\n self._dpdk_pre_post_flight_check()\n\n self.disable_hugepages_vfio_on_hvs_in_vms()\n self._ovs_br_ex_port_is_system_interface()","def enable(self, enable):\n\n self._enable = enable","def bdev_nvme_set_hotplug(client, enable, period_us=None):\n params = {'enable': enable}\n\n if period_us:\n params['period_us'] = period_us\n\n return client.call('bdev_nvme_set_hotplug', params)","def schedule(self, plugin, context, network):\n agents_per_network = cfg.CONF.dhcp_agents_per_network\n\n #TODO(gongysh) don't schedule the networks with only\n # subnets whose enable_dhcp is false\n with context.session.begin(subtransactions=True):\n dhcp_agents = plugin.get_dhcp_agents_hosting_networks(\n context, [network['id']], active=True)\n if len(dhcp_agents) >= agents_per_network:\n LOG.debug('Network %s is hosted already',\n network['id'])\n return\n n_agents = agents_per_network - len(dhcp_agents)\n enabled_dhcp_agents = plugin.get_agents_db(\n context, filters={\n 'agent_type': [constants.AGENT_TYPE_DHCP],\n 'admin_state_up': [True]})\n if not enabled_dhcp_agents:\n LOG.warn(_LW('No more DHCP agents'))\n return\n active_dhcp_agents = [\n agent for agent in set(enabled_dhcp_agents)\n if not agents_db.AgentDbMixin.is_agent_down(\n agent['heartbeat_timestamp'])\n and agent not in dhcp_agents\n ]\n if not active_dhcp_agents:\n LOG.warn(_LW('No more DHCP agents'))\n return\n n_agents = min(len(active_dhcp_agents), n_agents)\n chosen_agents = random.sample(active_dhcp_agents, n_agents)\n self._schedule_bind_network(context, chosen_agents, network['id'])\n return chosen_agents","def start_network(self):\n try:\n self.topo.build_topo()\n except:\n error('Cannot build the topology.')\n try:\n self.net = IPNet(topo=self.topo, use_v4=False, use_v6=True)\n self.net.start()\n except:\n self.stop_network()\n error('Cannot start the network.')","def configureDHCP():\n dhcpStart = config.get(\"hotspot\", \"dhcpstart\")\n dhcpEnd = config.get(\"hotspot\", \"dhcpend\")\n dnsmasqConfig = f\"\"\"#PI Hotspot config\ndomain-needed\nbogus-priv\ndhcp-option=option:dns-server\ndhcp-authoritative\ndhcp-range={dhcpStart},{dhcpEnd},1h\n\"\"\"\n confFile = open(\"/etc/dnsmasq.conf\", \"w\")\n confFile.write(dnsmasqConfig)\n confFile.close()","def dhcp_used(self, dhcp_used):\n\n self._dhcp_used = dhcp_used","def change_adp(self, network: str):\r\n self.ip = network\r\n self.adp = self.ipv4_adp[network]\r\n self.mac = self.ipv4_mac[network].replace('-', ':')\r\n # print(self.adp, self.ip, self.mac)\r","def create_network(options, vsm_obj):\n edge_id = get_edge(vsm_obj)\n if not edge_id:\n if not add_edge(options):\n print(\"Failed to create edge\")\n return False\n edge_id = get_edge(vsm_obj)\n\n vdn_scope = get_transport_zone(options)\n virtual_wire = VirtualWire(vdn_scope)\n name = get_network_name(options)\n response = virtual_wire.read_by_name(name)\n if response != \"FAILURE\":\n print(\"Found network %s already exists\" % options.name)\n return True\n\n virtual_wire_create = VirtualWireCreateSpecSchema()\n virtual_wire_create.name = name\n virtual_wire_create.tenantId = name\n virtual_wire_create.description = 'NSX network %s' % name\n\n # check if user needs to enable guest vlan tagging,\n # this is require if one needs to run vlan tests in nested\n # environment.\n if hasattr(options, 'guest_vlan'):\n if options.guest_vlan is True:\n print(\"network %s has guest vlan tagging enabled\"\\\n % options.name)\n virtual_wire_create.guestVlanAllowed = True\n\n print(\"Creating network %s\" % options.name)\n result = virtual_wire.create(virtual_wire_create)\n if (result[0].response.status != 201):\n print \"response: %s\" % result[0].response.status\n print \"response: %s\" % result[0].response.reason\n return False\n print(\"Changing security settings on the network\")\n set_network_security_policy(options)\n return add_edge_interface(options, edge_id)","def connect(self):\n self.net.active(True)\n self.net.config(essid=self.ssid, password=self.pwd, channel=3)\n\n while not self.net.active():\n pass\n\n self.net.ifconfig((\"192.168.4.5\", \"255.255.255.0\", \"192.168.4.1\", \"208.67.222.222\"))","def test_ap_hs20_network_preference2(dev, apdev):\n bssid2 = apdev[1]['bssid']\n params = hostapd.wpa2_params(ssid=\"home\", passphrase=\"12345678\")\n hostapd.add_ap(apdev[1]['ifname'], params)\n\n dev[0].hs20_enable()\n values = { 'realm': \"example.com\",\n 'username': \"hs20-test\",\n 'password': \"password\",\n 'domain': \"example.com\",\n 'priority': \"1\" }\n dev[0].add_cred_values(values)\n\n id = dev[0].add_network()\n dev[0].set_network_quoted(id, \"ssid\", \"home\")\n dev[0].set_network_quoted(id, \"psk\", \"12345678\")\n dev[0].request(\"ENABLE_NETWORK %s no-connect\" % id)\n\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\"], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if bssid2 not in ev:\n raise Exception(\"Unexpected network selected\")\n\n bssid = apdev[0]['bssid']\n params = hs20_ap_params()\n hostapd.add_ap(apdev[0]['ifname'], params)\n\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\",\n \"INTERWORKING-ALREADY-CONNECTED\" ], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if \"INTERWORKING-ALREADY-CONNECTED\" in ev:\n raise Exception(\"No roam to higher priority network\")\n if bssid not in ev:\n raise Exception(\"Unexpected network selected\")","def setup(self, network, reuse_existing=False):\n device_id = self.get_device_id(network)\n port = self.plugin.get_dhcp_port(network.id, device_id)\n\n interface_name = self.get_interface_name(network, port)\n\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n\n if ip_lib.device_exists(interface_name,\n self.conf.root_helper,\n namespace):\n if not reuse_existing:\n raise exceptions.PreexistingDeviceFailure(\n dev_name=interface_name)\n\n LOG.debug(_('Reusing existing device: %s.') % interface_name)\n else:\n self.driver.plug(network.id,\n port.id,\n interface_name,\n port.mac_address,\n namespace=namespace)\n ip_cidrs = []\n for fixed_ip in port.fixed_ips:\n subnet = fixed_ip.subnet\n net = netaddr.IPNetwork(subnet.cidr)\n ip_cidr = '%s/%s' % (fixed_ip.ip_address, net.prefixlen)\n ip_cidrs.append(ip_cidr)\n\n self.driver.init_l3(interface_name, ip_cidrs,\n namespace=namespace)\n\n return interface_name","def modif_network(self):\n print \"preparation du fichier network interfaces\"\n if version_os[\"OS\"] == \"CentOS\":\n self.exec_cmd(\"cp %s/etc/sysconfig/network_scripts/ifcfg-eth0 %s/etc/sysconfig/network_scripts/ifcfg-eth0.pre.p2v\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\n else:\n self.exec_cmd(\"cp %s/etc/network/interfaces %s/etc/network/interfaces.post.p2v\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\n self.exec_cmd(\"cp %s/etc/network/interfaces.pre.p2v %s/etc/network/interfaces\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))","def set_enabled(self, bEnabled):\n\t\tcall_sdk_function('PrlVirtNet_SetEnabled', self.handle, bEnabled)","def enable_gprs(self):\n if not has_internet():\n try:\n if not self.is_power_on():\n self.turn_on_modem()\n except TimeoutError:\n self.disable_gprs()\n self.turn_off_modem() # restart modem\n self.turn_on_modem()\n time.sleep(1)\n os.system(\n 'sudo pon {}'.format(os.path.basename(self.ppp_config_file))\n )\n # wait 420 seconds for ppp to start, if not raise TimeoutError\n self.check_internet_connection()\n else:\n self._logger.info('The device is already connected to the internet!')","def enable_relays(self):\n #ensure clock and data are low\n self.e.clear_bit(7)\n self.e.clear_bit(5)\n time.sleep(0.01)\n\n #pulse the clock line\n self.e.set_bit(7)\n time.sleep(0.01)\n self.e.clear_bit(7)","def test_ap_hs20_network_preference4(dev, apdev):\n bssid = apdev[0]['bssid']\n params = hs20_ap_params()\n hostapd.add_ap(apdev[0]['ifname'], params)\n\n bssid2 = apdev[1]['bssid']\n params = hs20_ap_params(ssid=\"test-hs20b\")\n params['hessid'] = bssid2\n params['anqp_3gpp_cell_net'] = \"555,444\"\n params['domain_name'] = \"wlan.mnc444.mcc555.3gppnetwork.org\"\n hostapd.add_ap(apdev[1]['ifname'], params)\n\n dev[0].hs20_enable()\n values = { 'realm': \"example.com\",\n 'username': \"hs20-test\",\n 'password': \"password\",\n 'priority': \"1\" }\n dev[0].add_cred_values(values)\n values = { 'imsi': \"555444-333222111\",\n 'eap': \"SIM\",\n 'milenage': \"5122250214c33e723a5dd523fc145fc0:981d464c7c52eb6e5036234984ad0bcf:000000000123\" }\n id = dev[0].add_cred_values(values)\n\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\"], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if bssid not in ev:\n raise Exception(\"Unexpected network selected\")\n\n dev[0].set_cred(id, \"priority\", \"2\")\n dev[0].request(\"INTERWORKING_SELECT auto freq=2412\")\n ev = dev[0].wait_event([\"CTRL-EVENT-CONNECTED\",\n \"INTERWORKING-ALREADY-CONNECTED\" ], timeout=15)\n if ev is None:\n raise Exception(\"Connection timed out\")\n if \"INTERWORKING-ALREADY-CONNECTED\" in ev:\n raise Exception(\"No roam to higher priority network\")\n if bssid2 not in ev:\n raise Exception(\"Unexpected network selected\")","def set_enable(self, enable):\n\n with AutoUpdater._lock:\n if isinstance(enable, Bus):\n AutoUpdater.remove_link(self._enable)\n AutoUpdater.add_link(\n enable,\n self._enable)\n else:\n raise ValueError(\n \"ERROR: Invalid Enable input. Enable must be a \"\n \"1-bit Bus or a Connector.\")","def wifi_connect(self, vap: VirtualAPHostapd) -> bool:\n config_file_name = \"boardfarm_tmp.conf\"\n config_file_path = \"/tmp/{}\".format(config_file_name)\n\n # Create network configuration for SSID\n bssid = \"bssid={}\".format(vap.bssid)\n ssid = \"ssid=\\\"{}\\\"\".format(vap.get_ssid())\n key = \"psk=\\\"{}\\\"\".format(vap.get_psk())\n network_config = \"network={{\\n{}\\n{}\\n{}\\n}}\".format(bssid, ssid, key)\n # Clean up previous configuration\n self.sendline(\"rm -f \\\"{}\\\"\".format(config_file_path))\n self.expect(self.prompt)\n self.sendline(\"echo \\'{}\\' > \\\"{}\\\"\".format(network_config, config_file_path))\n self.expect(self.prompt)\n # Start wpa_supplicant with created configuration\n # Typical coommand on RPI: wpa_supplicant -B -c/tmp/temp.conf -iwlan0 -Dnl80211,wext\n self.sudo_sendline(\"wpa_supplicant -B -D{} -i{} -c{}\".format(\n self.driver_name, self.iface_wifi, config_file_path))\n self.expect(\"Successfully initialized wpa_supplicant\")\n return bool(self.match)","def set_network(self, path, ip=\"\", netmask=\"255.255.255.0\", gateway=\"\"):\n\n with open(os.path.join(path, 'etc', 'network', 'interfaces'), 'w') \\\n as f:\n f.write(\"auto lo\\niface lo inet loopback\\n\\n\")\n\n if len(ip) <= 0:\n f.write(\"auto eth0\\niface eth0 inet dhcp\\n\")\n else:\n f.write(\"auto eth0\\niface eth0 inet static\\n\")\n f.write(\"\\taddress {0}\\n\\tnetmask {1}\\n\\tgateway {2}\\n\".\\\n format(ip, netmask, gateway))","def set_management_icmp(enabled=True, deploy=False):\n\n if enabled is True:\n value = \"no\"\n elif enabled is False:\n value = \"yes\"\n else:\n raise CommandExecutionError(\n \"Invalid option provided for service enabled option.\"\n )\n\n ret = {}\n\n query = {\n \"type\": \"config\",\n \"action\": \"set\",\n \"xpath\": \"/config/devices/entry[@name='localhost.localdomain']/deviceconfig/system/service\",\n \"element\": \"<disable-icmp>{}</disable-icmp>\".format(value),\n }\n\n ret.update(__proxy__[\"panos.call\"](query))\n\n if deploy is True:\n ret.update(commit())\n\n return ret","def call_driver(self, action, network):\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n try:\n # the Driver expects something that is duck typed similar to\n # the base models.\n driver = self.dhcp_driver_cls(self.conf,\n network,\n self.conf.root_helper,\n self.device_manager,\n namespace)\n getattr(driver, action)()\n return True\n\n except Exception, e:\n self.needs_resync = True\n LOG.exception('Unable to %s dhcp.' % action)","def config_networking(\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\n ):\n\n global_ip = vim.vm.customization.GlobalIPSettings()\n adapter_map = vim.vm.customization.AdapterMapping()\n adapter_map.adapter = vim.vm.customization.IPSettings()\n adapter_map.macAddress = network_obj.macAddress\n if ip:\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\n adapter_map.adapter.ip.ipAddress = ip\n else:\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\n adapter_map.adapter.subnetMask = netmask\n adapter_map.adapter.gateway = gateway\n global_ip.dnsServerList = dns\n adapter_map.adapter.dnsDomain = domain\n ident = vim.vm.customization.LinuxPrep()\n ident.hostName = vim.vm.customization.FixedName()\n if guest_hostname:\n ident.hostName.name = guest_hostname\n else:\n ident.hostName.name = self.vm_obj.name\n custom_spec = vim.vm.customization.Specification()\n custom_spec.nicSettingMap = [adapter_map]\n custom_spec.identity = ident\n custom_spec.globalIPSettings = global_ip\n return self.vm_obj.Customize(spec=custom_spec)","async def enable_ping(\n client,\n event,\n allow: ('bool', 'Enable?') = True,\n):\n guild = event.guild\n if guild is None:\n abort('Guild only command.')\n \n if not event.user_permissions.can_administrator:\n abort('You must have administrator permission to invoke this command.')\n \n application_commands = await client.application_command_guild_get_all(guild)\n for application_command in application_commands:\n # If you are not working with overlapping names, a name check should be enough.\n if application_command.name == ping.name:\n command_present = True\n break\n else:\n command_present = False\n \n if allow:\n if command_present:\n content = 'The command is already present.'\n else:\n await client.application_command_guild_create(guild, ping.get_schema())\n content = 'The command has been added.'\n else:\n if command_present:\n await client.application_command_guild_delete(guild, application_command)\n content = 'The command has been disabled.'\n else:\n content = 'The command is not present.'\n \n return Embed('Success', content)","def test_add_remove_network_from_dhcp_agent(self):\n # The agent is now bound to the network, we can free the port\n self.ports_client.delete_port(self.port['id'])\n agent = dict()\n agent['agent_type'] = None\n body = self.admin_agents_client.list_agents()\n agents = body['agents']\n for a in agents:\n if a['agent_type'] == 'DHCP agent':\n agent = a\n break\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\n 'DHCP agent in agent list though dhcp_agent_scheduler'\n ' is enabled.')\n network = self.create_network()\n network_id = network['id']\n if self._check_network_in_dhcp_agent(network_id, agent):\n self._remove_network_from_dhcp_agent(network_id, agent)\n self._add_dhcp_agent_to_network(network_id, agent)\n else:\n self._add_dhcp_agent_to_network(network_id, agent)\n self._remove_network_from_dhcp_agent(network_id, agent)","def test_add_dhcp_agent_to_network(self):\n network_id = self._create_and_prepare_network_for_agent(\n self.agent['id'])\n\n with self.override_role():\n self.agents_client.add_dhcp_agent_to_network(\n self.agent['id'], network_id=network_id)\n # Clean up is not necessary and might result in 409 being raised.","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def setEnableCondition(*args):","def enable_server(self, server):\n log.info(\"Enabling %s in netscaler\", server)\n return self.post(\"server?action=enable\", {\"server\": {\"name\": server}}, content_type=self.content_type(\"server\"))","def _setup_wifi_ap(self):\n context = self._get_ap_context()\n try:\n check_output(['ifconfig', context['hostname']])\n logger.info('wifi ap {} already setup'.format(context['hostname']))\n return True\n except CalledProcessError:\n logger.info('Setting up virtual access point interface')\n call(['service', 'hostapd', 'stop'])\n call(['service', 'dnsmasq', 'stop'])\n\n self._write_system_template('/etc/dnsmasq.conf', 'access_point/dnsmasq.conf')\n self._write_system_template('/etc/hostapd/hostapd.conf', 'access_point/hostapd.conf', context)\n self._write_system_template('/etc/network/interfaces', 'access_point/interfaces.conf', context)\n self._write_system_template('/etc/default/hostapd', 'access_point/default_hostapd.conf', context)\n self._write_system_template('/etc/dhcpcd.conf', 'access_point/dhcpcd.conf', context)\n \n call(['systemctl', 'enable', 'hostapd', ])\n call(['systemctl', 'enable', 'dnsmasq', ])\n return True","def renew_dhcp_lease(self):\n\t\tresponse = os.system(\"/sbin/dhclient -r;/sbin/dhclient\")\n\t\tif response != 0:\n\t\t\tprint \"Network restart failed. DHCP Lease failed.\"","async def async_turn_on(self):\n path = \"/interface\"\n param = \"default-name\"\n if \"-\" in self._data[\"port-mac-address\"]:\n param = \"name\"\n value = self._data[param]\n mod_param = \"disabled\"\n mod_value = False\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n\n if self._data[\"poe-out\"] == \"off\":\n path = \"/interface/ethernet\"\n self._ctrl.set_value(path, param, value, \"poe-out\", \"auto-on\")\n\n await self._ctrl.force_update()","async def enable(self) -> None:\n try:\n await self.adguard.request(\n \"parental/enable\", method=\"POST\", data=\"sensitivity=TEEN\"\n )\n except AdGuardHomeError as exception:\n raise AdGuardHomeError(\n \"Enabling AdGuard Home parental control failed\"\n ) from exception","def enable(self, request):\n if request.data:\n if self.setpoint_valid:\n self.enabled = True\n self.setpoint_valid = True\n return [True,\"DP Controller Enabled\"]\n else:\n rospy.logwarn(\"No valid setpoint received yet.\")\n return [False,\"No valid setpoint received yet.\"]\n else:\n self.enabled = False\n return [False,\"DP Controller Disabled\"]","def enable(self):\n self.switch.enable()\n self._enabled = True","def set_network(self, addr, netmask, value):\n\n if len(addr) == 4:\n ipset.ipmap_ipv4_set_network(self.map, addr, netmask, value)\n return\n\n elif len(addr) == 16:\n ipset.ipmap_ipv6_set_network(self.map, addr, netmask, value)\n return\n\n else:\n raise ValueError(\"Invalid address\")","def enable(self):\n if not self.tm_started:\n for name, tm in self.air_traffic_manager.items():\n logging.debug(\"Starting tm %s\" % name)\n tm.start()\n tm_started = True\n\n logging.debug(\"Enabling switch %s\" % self.name)\n self.disabled = False","def create_external_network(self, extnet_info, ignore_privious=False):\n LOG_OBJ.debug(\"Creating External Network : \")\n _tenant_name = config.cloud_admin_project\n _net_name = extnet_info['extnet_name']\n _gateway = extnet_info['gateway']\n _cidr = extnet_info['cidr']\n _start_ip = extnet_info['start_ip']\n _end_ip = extnet_info['end_ip']\n\n if not ignore_privious:\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks\"\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_project\"]}\n _body = None\n\n response = self.request(\"GET\", _url, _headers, _body)\n output = json.loads(response.data)\n if output is None:\n LOG_OBJ.error(\"No response from server while getting\"\n \" networks.\")\n return\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Getting networks list Failed with status %s \" %\n response.status)\n return response.status\n\n for nets in output['networks']:\n if nets['router:external']:\n LOG_OBJ.info(\"External Network already created\")\n return\n\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks.json\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.cloud_admin_info[\"token_project\"]}\n _extnet_info = {\"network\": {\n \"tenant_id\": self.cloud_admin_info[\"project_id\"],\n \"name\": _net_name,\n \"router:external\": \"True\",\n \"admin_state_up\": True}}\n _body = json.dumps(_extnet_info)\n\n response = self.request(\"POST\", _url, _headers, _body)\n output = json.loads(response.data)\n if output is None:\n LOG_OBJ.error(\"No response from server while creating ext net.\")\n return\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Create ext network Failed with status %s \" %\n response.status)\n return response.status\n\n _ext_net_id = output['network']['id']\n LOG_OBJ.debug(\"External Network created successfully. ID:%s\" %\n _ext_net_id)\n\n # Creating External Subnet\n _url = \"http://\" + self.host_ip + \":9696/v2.0/subnets.json\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.cloud_admin_info[\"token_project\"]}\n _ext_subnet_info = {\"subnet\": {\n \"ip_version\": 4,\n \"allocation_pools\": [{\"start\": _start_ip,\n \"end\": _end_ip}],\n \"gateway_ip\": _gateway,\n \"enable_dhcp\": \"False\",\n \"network_id\": _ext_net_id,\n \"tenant_id\": self.cloud_admin_info[\"project_id\"],\n \"cidr\": _cidr,\n \"name\": _net_name + \"-sub\"}}\n _body = json.dumps(_ext_subnet_info)\n output = self.request(\"POST\", _url, _headers, _body)\n if output is None:\n LOG_OBJ.error(\"No response from server while creating ext-subet\")\n return\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Create subnet Failed with status %s \" %\n response.status)\n return response.status\n\n return _ext_net_id","def _disable_wifi_ap(self):\n call(['systemctl', 'disable', 'hostapd', ])\n call(['systemctl', 'disable', 'dnsmasq', ])\n\n context = self._get_ap_context()\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)","def _set_rules_mgmt(self, gnp_config, network, host):\n addr_pool = self.dbapi.address_pool_get(network.pool_uuid)\n ip_version = IPAddress(f\"{addr_pool.network}\").version\n self._add_source_net_filter(gnp_config[\"spec\"][\"ingress\"],\n f\"{addr_pool.network}/{addr_pool.prefix}\")\n if (ip_version == 6):\n self._add_source_net_filter(gnp_config[\"spec\"][\"ingress\"], LINK_LOCAL)\n if (ip_version == 4):\n # add rule to allow DHCP requests (dhcp-offer have src addr == 0.0.0.0)\n # worker/storage nodes request IP dynamically\n rule = self._get_dhcp_rule(host.personality, \"UDP\", ip_version)\n gnp_config[\"spec\"][\"ingress\"].append(rule)\n\n # copy the TCP rule and do the same for IGMP\n igmp_proto = 2\n igmp_egr_rule = copy.deepcopy(gnp_config[\"spec\"][\"egress\"][0])\n igmp_egr_rule[\"protocol\"] = igmp_proto\n igmp_egr_rule[\"metadata\"][\"annotations\"][\"name\"] = \\\n f\"stx-egr-{host.personality}-{network.type}-igmp{ip_version}\"\n gnp_config[\"spec\"][\"egress\"].append(igmp_egr_rule)\n igmp_ingr_rule = copy.deepcopy(gnp_config[\"spec\"][\"ingress\"][0])\n igmp_ingr_rule[\"protocol\"] = igmp_proto\n igmp_ingr_rule[\"metadata\"][\"annotations\"][\"name\"] = \\\n f\"stx-ingr-{host.personality}-{network.type}-igmp{ip_version}\"\n gnp_config[\"spec\"][\"ingress\"].append(igmp_ingr_rule)","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def set_ipaddress(modulo):\n\n print ('Configuring IP address...')\n\n modulo.write('AT+NETOPEN\\r\\n'.encode())\n\n if _valid_net(modulo): \n try:\n modulo.write('AT+IPADDR\\r\\n'.encode())\n time.sleep(0.1)\n except serial.SerialException:\n print ('... Whitout IP address, try again')\n if _valid_ip(modulo):\n print ('IP address configurated')\n else:\n print ('IP not configurated')\n else:\n print ('Net Already configurated')\n \n data = _read_line(modulo)\n return data","def enable(self):\n\t\tresponse = self.client.post(self._endpoint + \"/enable\")\n\t\treturn bool(response.json[\"success\"])","def enable_host(self, name):\n from soppa.local import aslocal\n self.guest_ip = self.guest_ip()\n self.guest_host_name = name\n # Host (remote) change\n self.file.set_setting('/etc/hosts', '{0} {1}'.format('127.0.0.1', self.guest_host_name))\n # local change\n aslocal()\n self.file.set_setting('/etc/hosts', '{0} {1}'.format(self.guest_ip, name))","def dhcp_agent_network_remove(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def setAdhocParameters(self, host, mode, **params):\n self.mode = mode\n latency = 10\n self.host = host\n #delay = 5 * distance\n try:\n options = dict( params )\n self.interface = options[ 'interface' ]\n except: \n self.interface = 'wlan0'\n \n bandwidth = wifiParameters.set_bw(mode)\n #self.host.cmd(host, \"tc qdisc replace dev %s-%s root netem rate %.2fmbit latency %.2fms delay %.2fms\" % (host, self.interface, rate, latency, delay)) \n self.host.cmd(\"tc qdisc add dev %s-%s root tbf rate %smbit latency %sms burst 1540\" % (str(host), self.interface, bandwidth, latency))","def set_management_telnet(enabled=True, deploy=False):\n\n if enabled is True:\n value = \"no\"\n elif enabled is False:\n value = \"yes\"\n else:\n raise CommandExecutionError(\n \"Invalid option provided for service enabled option.\"\n )\n\n ret = {}\n\n query = {\n \"type\": \"config\",\n \"action\": \"set\",\n \"xpath\": \"/config/devices/entry[@name='localhost.localdomain']/deviceconfig/system/service\",\n \"element\": \"<disable-telnet>{}</disable-telnet>\".format(value),\n }\n\n ret.update(__proxy__[\"panos.call\"](query))\n\n if deploy is True:\n ret.update(commit())\n\n return ret"],"string":"[\n \"def dhcp_agent_network_add(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def enable_network_management(self):\\n self._request({\\\"enable-network-management\\\": True})\",\n \"def elAddNetworkConfigurationWithDhcp(self, device):\\n commandSection = self.sectionByName(\\\"command\\\")\\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\\n deviceMatch = re.match(r\\\"([^0-9]+)([0-9])\\\", device)\\n if deviceMatch:\\n # e.g. \\\"eth0\\\"\\n devicePrefix = deviceMatch.group(1)\\n deviceNumber = deviceMatch.group(2)\\n deviceNumber = int(deviceNumber)\\n for i in range(8, deviceNumber - 1, -1):\\n deviceI = devicePrefix + str(i)\\n deviceIPlus1 = devicePrefix + str(i + 1)\\n # move up by one device each network configuration\\n commandSection.string = re.sub(r\\\"(?m)^([ \\\\t]*network[ \\\\t]+.*--device[ \\\\t]*(?:=|[ \\\\t])[ \\\\t]*)\\\" + re.escape(deviceI) + r\\\"(.*)$\\\",\\n r\\\"\\\\g<1>\\\" + deviceIPlus1 + r\\\"\\\\g<2>\\\",\\n commandSection.string)\\n # not --noipv6\\n networkConfiguration = \\\"network --device=\\\" + device + \\\" --bootproto=dhcp --onboot=yes --activate\\\"\\n if deviceMatch and deviceNumber == 0:\\n # having configuration of eth0 first appears to be more conducive to overall success,\\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\\n # \\\"... in installer environment. Device of the first network command is activated if network is required,\\n # e.g. in case of network installation ...\\\",\\n commandSection.string = networkConfiguration + \\\"\\\\n\\\" \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + commandSection.string\\n else:\\n commandSection.string = commandSection.string \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + networkConfiguration + \\\"\\\\n\\\"\",\n \"def network_create_end(self, payload):\\n network_id = payload['network']['id']\\n self.enable_dhcp_helper(network_id)\",\n \"def enable(self):\\n # Netmiko reports enable and config mode as being enabled\\n if not self.native.check_enable_mode():\\n self.native.enable()\\n # Ensure device is not in config mode\\n if self.native.check_config_mode():\\n self.native.exit_config_mode()\\n\\n log.debug(\\\"Host %s: Device enabled.\\\", self.host)\",\n \"async def enable_protection(self) -> None:\\n await self._request(\\n \\\"dns_config\\\", method=\\\"POST\\\", json_data={\\\"protection_enabled\\\": True},\\n )\",\n \"def test_set_enabled(self):\\n networktables_mock = unittest.mock.Mock()\\n table_mock = unittest.mock.Mock()\\n networktables_mock.getTable.return_value = table_mock\\n\\n network_instance = network.Network(networktables_mock, None, None)\\n\\n network_instance.set_enabled(True)\\n table_mock.putBoolean.assert_called_with(\\\"/enabled\\\", True)\\n\\n network_instance.set_enabled(False)\\n table_mock.putBoolean.assert_called_with(\\\"/enabled\\\", False)\",\n \"def enable(ctx):\\n\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n config_db.mod_entry(\\\"NAT_GLOBAL\\\", \\\"Values\\\", {\\\"admin_mode\\\": \\\"enabled\\\"})\",\n \"def _set_rules_pxeboot(self, gnp_config, network, host):\\n\\n addr_pool = self.dbapi.address_pool_get(network.pool_uuid)\\n ip_version = IPAddress(f\\\"{addr_pool.network}\\\").version\\n self._add_source_net_filter(gnp_config[\\\"spec\\\"][\\\"ingress\\\"],\\n f\\\"{addr_pool.network}/{addr_pool.prefix}\\\")\\n if (ip_version == 6):\\n self._add_source_net_filter(gnp_config[\\\"spec\\\"][\\\"ingress\\\"], LINK_LOCAL)\\n if (ip_version == 4):\\n # add rule to allow DHCP requests (dhcp-offer have src addr == 0.0.0.0)\\n rule = self._get_dhcp_rule(host.personality, \\\"UDP\\\", ip_version)\\n gnp_config[\\\"spec\\\"][\\\"ingress\\\"].append(rule)\",\n \"def set_configure_with_dhcp(self, bConfigureWithDhcp):\\n\\t\\tcall_sdk_function('PrlVmDevNet_SetConfigureWithDhcp', self.handle, bConfigureWithDhcp)\",\n \"def set_dhcpserver_enabled(self, bEnabled):\\n\\t\\tcall_sdk_function('PrlVirtNet_SetDHCPServerEnabled', self.handle, bEnabled)\",\n \"def set_dhcp_conn(nic):\\n nic.EnableDHCP()\\n # After static DNS servers are specified to start using Dynamic Host\\n # Configuration Protocol (DHCP) instead of static DNS servers,\\n # you can call the method without supplying \\\"in\\\" parameters.\\n nic.SetDNSServerSearchOrder()\",\n \"async def async_turn_on(self):\\n path = \\\"/ip/firewall/nat\\\"\\n param = \\\".id\\\"\\n value = None\\n for uid in self._ctrl.data[\\\"nat\\\"]:\\n if (\\n self._ctrl.data[\\\"nat\\\"][uid][\\\"name\\\"]\\n == f\\\"{self._data['protocol']}:{self._data['dst-port']}\\\"\\n ):\\n value = self._ctrl.data[\\\"nat\\\"][uid][\\\".id\\\"]\\n\\n mod_param = \\\"disabled\\\"\\n mod_value = False\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n await self._ctrl.force_update()\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def is_configure_with_dhcp(self):\\n\\t\\treturn bool(call_sdk_function('PrlVmDevNet_IsConfigureWithDhcp', self.handle))\",\n \"def set_network_security_policy(options):\\n network = get_network_on_vc(options)\\n name = get_network_name(options)\\n\\n config_spec = Vim.Dvs.DistributedVirtualPortgroup.ConfigSpec()\\n config_info = network.GetConfig()\\n config_spec.description = config_info.name\\n config_spec.name = name\\n config_spec.configVersion = config_info.configVersion\\n\\n true_policy = Vim.BoolPolicy()\\n true_policy.value = True\\n dvs_port_setting = Vim.VMwareDVSPortSetting()\\n security_policy = Vim.DVSSecurityPolicy()\\n security_policy.allowPromiscuous = true_policy\\n security_policy.forgedTransmits = true_policy\\n security_policy.macChanges = true_policy\\n security_policy.inherited = False\\n dvs_port_setting.securityPolicy = security_policy\\n config_spec.defaultPortConfig = dvs_port_setting\\n\\n network.ReconfigureDVPortgroup_Task(config_spec)\",\n \"def setup_dhcp_config(self, board_config):\\n raise NotImplementedError\",\n \"def enableNatlink(self, silent=None):\\n if not self.isElevated: raise ElevationError(\\\"needed for enabling Natlink\\\")\\n # if self.isNatSpeakRunning(): raise NatSpeakRunningError(\\\"Probably needed for enabling Natlink\\\")\\n\\n self.registerNatlinkPyd(silent=1)\\n nssystemini = self.getNSSYSTEMIni()\\n section1 = self.section1\\n key1 = self.key1\\n value1 = self.value1\\n #\\n try:\\n win32api.WriteProfileVal(section1, key1, value1, nssystemini)\\n except pywintypes.error as details:\\n if details[0] == 5:\\n print('cannot enable Natlink (1), you probably need administrator rights')\\n else:\\n print('unexpected error at enable Natlink (1)')\\n raise\\n\\n result = self.NatlinkIsEnabled(silent=1)\\n if (result is None) or (not result):\\n nsappsini = self.getNSAPPSIni()\\n section2 = self.section2\\n key2 = self.key2\\n value2 = self.value2\\n try:\\n win32api.WriteProfileVal(section2, key2, value2, nsappsini)\\n except pywintypes.error as details:\\n if details[0] == 5:\\n print('cannot enable Natlink (2), you probably need administrator rights')\\n else:\\n print('unexpected error at enable Natlink (2)')\\n raise\\n result = self.NatlinkIsEnabled(silent=1)\\n if result == None:\\n text = \\\\\\n\\\"\\\"\\\"Cannot set the nsapps.ini setting in order to complete enableNatlink.\\n\\nProbably you did not run this program in \\\"elevated mode\\\". Please try to do so.\\n\\\"\\\"\\\"\\n self.warning(text)\\n return\\n result = self.NatlinkIsEnabled(silent=1)\\n if result:\\n if not silent:\\n print(('Natlink enabled, you can now restart %s'% self.DNSName))\\n else:\\n if not silent:\\n self.warning(\\\"failed to enable Natlink\\\")\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def commissioning_wifi(devCtrl, ssid, password, nodeId):\\n\\n # Inject the credentials to the device\\n err, res = send_zcl_command(devCtrl,\\n \\\"NetworkCommissioning \\\"\\n \\\"AddOrUpdateWiFiNetwork {} 0 0 \\\"\\n \\\"ssid=str:{} credentials=str:{} breadcrumb=0 timeoutMs=1000\\\".format(\\n nodeId,\\n ssid,\\n password\\n ))\\n if err != 0 and res[\\\"Status\\\"] != 0:\\n log.error(\\\"Set Wi-Fi credentials failed [{}]\\\".format(err))\\n return err\\n\\n # Enable the Wi-Fi interface\\n err, res = send_zcl_command(\\n devCtrl, \\\"NetworkCommissioning ConnectNetwork {} 0 0 networkID=str:{} breadcrumb=0 timeoutMs=1000\\\".format(nodeId, ssid))\\n if err != 0 and res[\\\"Status\\\"] != 0:\\n log.error(\\\"Enable Wi-Fi failed [{}]\\\".format(err))\\n return err\\n\\n return err\",\n \"def is_configure_with_dhcp(self):\\n\\t\\treturn bool(call_sdk_function('PrlSrvCfgNet_IsConfigureWithDhcp', self.handle))\",\n \"def refresh_dhcp_helper(self, network_id):\\n old_network = self.cache.get_network_by_id(network_id)\\n if not old_network:\\n # DHCP current not running for network.\\n return self.enable_dhcp_helper(network_id)\\n\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\\n\\n if new_cidrs and old_cidrs == new_cidrs:\\n self.call_driver('reload_allocations', network)\\n self.cache.put(network)\\n elif new_cidrs:\\n if self.call_driver('restart', network):\\n self.cache.put(network)\\n else:\\n self.disable_dhcp_helper(network.id)\",\n \"def run(self, enable_snat=True, network_create_args=None,\\n router_create_args=None):\\n network_create_args = network_create_args or {}\\n router_create_args = router_create_args or {}\\n\\n ext_net = self.neutron.create_network(**network_create_args)\\n router = self.neutron.create_router(**router_create_args)\\n self.neutron.add_gateway_to_router(router_id=router[\\\"id\\\"],\\n network_id=ext_net[\\\"id\\\"],\\n enable_snat=enable_snat)\\n self.neutron.remove_gateway_from_router(router[\\\"id\\\"])\",\n \"def disable_dhcp_helper(self, network_id):\\n network = self.cache.get_network_by_id(network_id)\\n if network:\\n if self.call_driver('disable', network):\\n self.cache.remove(network)\",\n \"async def enable(self, **kwargs) -> None: # pylint: disable=unused-argument\\r\\n await self.set_ena(True)\",\n \"def apply_network_settings(**settings):\\n if 'require_reboot' not in settings:\\n settings['require_reboot'] = False\\n if 'apply_hostname' not in settings:\\n settings['apply_hostname'] = False\\n\\n hostname_res = True\\n if settings['apply_hostname'] in _CONFIG_TRUE:\\n if 'hostname' in settings:\\n hostname_res = __salt__['network.mod_hostname'](settings['hostname'])\\n else:\\n log.warning(\\n 'The network state sls is trying to apply hostname '\\n 'changes but no hostname is defined.'\\n )\\n hostname_res = False\\n\\n res = True\\n if settings['require_reboot'] in _CONFIG_TRUE:\\n log.warning(\\n 'The network state sls is requiring a reboot of the system to '\\n 'properly apply network configuration.'\\n )\\n res = True\\n else:\\n res = __salt__['cmd.run']('/etc/netstart restart')\\n\\n return hostname_res and res\",\n \"def enable_vlan(self, mgr, vlanid, vlanname):\\n confstr = snipp.CMD_VLAN_CONF_SNIPPET % (vlanid, vlanname)\\n confstr = self.create_xml_snippet(confstr)\\n mgr.edit_config(target='running', config=confstr)\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def set_static_conn(nic, ip_addr, subnet_mask, default_gateway, dns_servers):\\n if isinstance(ip_addr, str):\\n ip_addr = [ip_addr,]\\n if isinstance(subnet_mask, str):\\n subnet_mask = [subnet_mask,]\\n if isinstance(default_gateway, str):\\n default_gateway = [default_gateway, ]\\n\\n # set defult gateway. return value:\\n # 0: success & no reboot required, \\n # 1: sucess & reboot required\\n ret = nic.SetGateways(default_gateway)\\n print 'Default Gateway updated (status %d)' % ret\\n\\n # Set IP adrress & subnet mask. return value:\\n # 0: success & no reboot required, \\n # 1: sucess & reboot required\\n ret = nic.EnableStatic(IPAddress=ip_addr, SubnetMask=subnet_mask)\\n print 'IP Address / Subnet Mask updated (status %d)' % ret\\n\\n # set dns servers\\n if dns_servers:\\n #assert 0 == nic.EnableDNS(DNSServerSearchOrder=dns_servers)\\n # or \\n ret = nic.SetDNSServerSearchOrder(dns_servers)\\n print 'DNS Server updated (status %d)' % ret\",\n \"def auto_schedule_networks(self, plugin, context, host):\\n agents_per_network = cfg.CONF.dhcp_agents_per_network\\n # a list of (agent, net_ids) tuples\\n bindings_to_add = []\\n with context.session.begin(subtransactions=True):\\n fields = ['network_id', 'enable_dhcp']\\n subnets = plugin.get_subnets(context, fields=fields)\\n net_ids = set(s['network_id'] for s in subnets\\n if s['enable_dhcp'])\\n if not net_ids:\\n LOG.debug('No non-hosted networks')\\n return False\\n query = context.session.query(agents_db.Agent)\\n query = query.filter(agents_db.Agent.agent_type ==\\n constants.AGENT_TYPE_DHCP,\\n agents_db.Agent.host == host,\\n agents_db.Agent.admin_state_up == sql.true())\\n dhcp_agents = query.all()\\n for dhcp_agent in dhcp_agents:\\n if agents_db.AgentDbMixin.is_agent_down(\\n dhcp_agent.heartbeat_timestamp):\\n LOG.warn(_LW('DHCP agent %s is not active'), dhcp_agent.id)\\n continue\\n for net_id in net_ids:\\n agents = plugin.get_dhcp_agents_hosting_networks(\\n context, [net_id], active=True)\\n if len(agents) >= agents_per_network:\\n continue\\n if any(dhcp_agent.id == agent.id for agent in agents):\\n continue\\n bindings_to_add.append((dhcp_agent, net_id))\\n # do it outside transaction so particular scheduling results don't\\n # make other to fail\\n for agent, net_id in bindings_to_add:\\n self._schedule_bind_network(context, [agent], net_id)\\n return True\",\n \"def add_network(self, name_of_vm, port_group):\\n adapter_type = 'e1000'\\n vds = \\\"yes\\\"\\n try:\\n # import sys,pdb;pdb.Pdb(stdout=sys.__stdout__).set_trace()\\n vmachine = self.vcenter.get_dc_object([vim.VirtualMachine], name_of_vm)\\n\\n if vds == 'yes':\\n network = self.vcenter.get_dc_object([vim.dvs.DistributedVirtualPortgroup], port_group)\\n else:\\n network = self.get_network(port_group)\\n\\n new_nic = vim.vm.ConfigSpec()\\n nic_changes = []\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.add\\n if adapter_type == 'e1000':\\n nic_spec.device = vim.vm.device.VirtualE1000()\\n elif adapter_type == 'vmxnet2':\\n nic_spec.device = vim.vm.device.VirtualVmxnet2()\\n else:\\n nic_spec.device = vim.vm.device.VirtualVmxnet3()\\n nic_spec.device.deviceInfo = vim.Description()\\n if vds == 'yes':\\n vir_port = vim.vm.device.VirtualEthernetCard.DistributedVirtualPortBackingInfo()\\n nic_spec.device.backing = vir_port\\n dvs_port_connection = vim.dvs.PortConnection()\\n dvs_port_connection.portgroupKey = network.key\\n dvs_port_connection.switchUuid = network.config.distributedVirtualSwitch.uuid\\n nic_spec.device.backing.port = dvs_port_connection\\n else:\\n nic_spec.device.backing = vim.vm.device.VirtualEthernetCard.NetworkBackingInfo()\\n nic_spec.device.backing.useAutoDetect = False\\n nic_spec.device.backing.network = network\\n nic_spec.device.backing.deviceName = port_group\\n nic_spec.device.connectable = vim.vm.device.VirtualDevice.ConnectInfo()\\n nic_spec.device.connectable.startConnected = True\\n nic_spec.device.connectable.connected = True\\n nic_spec.device.connectable.allowGuestControl = True\\n nic_spec.device.connectable.status = 'untried'\\n nic_spec.device.wakeOnLanEnabled = True\\n nic_spec.device.addressType = 'assigned'\\n nic_changes.append(nic_spec)\\n new_nic.deviceChange = nic_changes\\n add_nic = vmachine.ReconfigVM_Task(spec=new_nic)\\n log.info('Adding Network adapter to the VM...')\\n while add_nic.info.state not in ['success', 'error']:\\n time.sleep(1)\\n status = add_nic.info.state\\n if status == 'success':\\n log.info('Nic added successfully: {}'.format(name_of_vm))\\n if status == 'error':\\n log.info('Could not add Network adapter {}'.format(name_of_vm))\\n return status\\n\\n except Exception as error:\\n log.info(\\\"Caught exception: {} \\\\n {}\\\".format(error, error.message))\",\n \"def test_ap_hs20_network_preference(dev, apdev):\\n bssid = apdev[0]['bssid']\\n params = hs20_ap_params()\\n hostapd.add_ap(apdev[0]['ifname'], params)\\n\\n dev[0].hs20_enable()\\n values = { 'realm': \\\"example.com\\\",\\n 'username': \\\"hs20-test\\\",\\n 'password': \\\"password\\\",\\n 'domain': \\\"example.com\\\" }\\n dev[0].add_cred_values(values)\\n\\n id = dev[0].add_network()\\n dev[0].set_network_quoted(id, \\\"ssid\\\", \\\"home\\\")\\n dev[0].set_network_quoted(id, \\\"psk\\\", \\\"12345678\\\")\\n dev[0].set_network(id, \\\"priority\\\", \\\"1\\\")\\n dev[0].request(\\\"ENABLE_NETWORK %s no-connect\\\" % id)\\n\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\"], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if bssid not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\\n\\n bssid2 = apdev[1]['bssid']\\n params = hostapd.wpa2_params(ssid=\\\"home\\\", passphrase=\\\"12345678\\\")\\n hostapd.add_ap(apdev[1]['ifname'], params)\\n\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\",\\n \\\"INTERWORKING-ALREADY-CONNECTED\\\" ], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if \\\"INTERWORKING-ALREADY-CONNECTED\\\" in ev:\\n raise Exception(\\\"No roam to higher priority network\\\")\\n if bssid2 not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\",\n \"def enableAddressLayer(self):\\n\\n self._deladdressaction.setEnabled(True)\\n self._createnewaddressaction.setEnabled(True)\\n self._moveaddressaction.setEnabled(True)\\n self._updateaddressaction.setEnabled(True)\\n self._highlightaction.setEnabled(True)\",\n \"def enable_dns_management(self):\\n self._request({\\\"enable-dns-management\\\": True})\",\n \"def ethernet_on(self):\\n if not self.healthy:\\n self.health_check()\\n if not self._ethernet_switch:\\n raise errors.CapabilityNotReadyError(\\n device_name=self._device_name,\\n msg=\\\"Not set up for ethernet switching.\\\")\\n self._ethernet_switch.switch_power.power_on(self.ethernet_port_number)\",\n \"def enable_ad():\\n for host in online_hosts:\\n with settings(warn_only=True):\\n\\n result1 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgActiveDirectory -o cfgADEnable 1\\\")\\n if result1.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Enabling Active Directory failed \\\")\\n\\n result2 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgActiveDirectory -o cfgADType 2\\\")\\n if result2.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Setting of standard schema for AD failed \\\")\",\n \"def enable():\\n request = dict(id='gbn')\\n _gbn_enable(request)\",\n \"def AddNetworkFlag(parser):\\n help_text = \\\"\\\"\\\"\\\\\\n The VPC network from which the AlloyDB instance is accessible via private\\n IP. For example, projects/myProject/global/networks/default. This setting\\n cannot be updated after it is set.\\n \\\"\\\"\\\"\\n parser.add_argument('--network', help=help_text)\",\n \"def update_network(**kwargs):\\n\\n ip_addr = kwargs.get('ip_addr')\\n is_private = kwargs.get('is_private')\\n name = kwargs.get('name')\\n dns_names = kwargs.get('dns_names')\\n is_scanning = kwargs.get('is_scanning', False)\\n network_id = make_shortuuid(name)\\n\\n network = {\\n 'dns_names': dns_names,\\n 'ip_addr': ip_addr,\\n 'is_private' : is_private,\\n 'name': name,\\n 'id': network_id,\\n 'is_scanning': is_scanning,\\n 'updated_count': 0\\n\\n }\\n\\n network_exists = r.table(\\\"networks\\\").insert([network], conflict=\\\"update\\\")\\n\\n return network_exists.run(conn)\",\n \"def add_network_adapter(self, network_obj):\\n\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.add\\n nic_spec.device = vim.vm.device.VirtualVmxnet3()\\n nic_spec.device.wakeOnLanEnabled = True\\n nic_spec.device.addressType = \\\"assigned\\\"\\n nic_spec.device.deviceInfo = vim.Description()\\n nic_spec.device.backing = vim.vm.device.VirtualEthernetCard.NetworkBackingInfo()\\n nic_spec.device.backing.network = network_obj\\n nic_spec.device.backing.deviceName = network_obj.name\\n nic_spec.device.backing.useAutoDetect = False\\n nic_spec.device.connectable = vim.vm.device.VirtualDevice.ConnectInfo()\\n nic_spec.device.connectable.startConnected = True\\n nic_spec.device.connectable.connected = True\\n nic_spec.device.connectable.allowGuestControl = True\\n config_spec = vim.vm.ConfigSpec()\\n config_spec.deviceChange = [nic_spec]\\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)\",\n \"def networkMode(self, networkMode):\\n\\n # Setting the network mode can take a bit of time, so give it 10 seconds\\n # to finish\\n response = self.at.sendCommand(f\\\"AT+CFUN={networkMode}\\\", timeout = 10)\\n\\n if not response:\\n raise modem.AtError(response, \\\"Failed to set network mode\\\")\",\n \"def enable(self):\\n try:\\n self.bus.open(self.BUS_NUMBER)\\n self.write(AntennaDeployerCommand.ARM_ANTS, 0x00)\\n self.bus.close()\\n return True\\n except:\\n return False\",\n \"def enable_charge_pump(enable):\\n send_command(0x8D)\\n if enable:\\n send_command(0x14)\\n else:\\n send_command(0x10)\",\n \"def enable_gateway(self, enable_gateway):\\n\\n self._enable_gateway = enable_gateway\",\n \"def test_enable_dpdk(self):\\n logging.info('Pre-flight check')\\n self._dpdk_pre_post_flight_check()\\n self._ovs_br_ex_port_is_system_interface()\\n\\n self.enable_hugepages_vfio_on_hvs_in_vms(4)\\n with self.config_change(\\n {\\n 'enable-dpdk': False,\\n 'dpdk-driver': '',\\n },\\n {\\n 'enable-dpdk': True,\\n 'dpdk-driver': 'vfio-pci',\\n },\\n application_name='ovn-chassis'):\\n logging.info('Checking openvswitch-switch-dpdk is installed')\\n self._openvswitch_switch_dpdk_installed()\\n logging.info('Checking DPDK is configured in OVS')\\n self._ovs_dpdk_init_configured()\\n logging.info('Checking DPDK is successfully initialized in OVS')\\n self._ovs_dpdk_initialized()\\n logging.info('Checking that br-ex configed with DPDK interface...')\\n self._ovs_br_ex_port_is_dpdk_interface()\\n logging.info('and is not in error.')\\n self._ovs_br_ex_interface_not_in_error()\\n\\n logging.info('Post-flight check')\\n self._dpdk_pre_post_flight_check()\\n\\n self.disable_hugepages_vfio_on_hvs_in_vms()\\n self._ovs_br_ex_port_is_system_interface()\",\n \"def enable(self, enable):\\n\\n self._enable = enable\",\n \"def bdev_nvme_set_hotplug(client, enable, period_us=None):\\n params = {'enable': enable}\\n\\n if period_us:\\n params['period_us'] = period_us\\n\\n return client.call('bdev_nvme_set_hotplug', params)\",\n \"def schedule(self, plugin, context, network):\\n agents_per_network = cfg.CONF.dhcp_agents_per_network\\n\\n #TODO(gongysh) don't schedule the networks with only\\n # subnets whose enable_dhcp is false\\n with context.session.begin(subtransactions=True):\\n dhcp_agents = plugin.get_dhcp_agents_hosting_networks(\\n context, [network['id']], active=True)\\n if len(dhcp_agents) >= agents_per_network:\\n LOG.debug('Network %s is hosted already',\\n network['id'])\\n return\\n n_agents = agents_per_network - len(dhcp_agents)\\n enabled_dhcp_agents = plugin.get_agents_db(\\n context, filters={\\n 'agent_type': [constants.AGENT_TYPE_DHCP],\\n 'admin_state_up': [True]})\\n if not enabled_dhcp_agents:\\n LOG.warn(_LW('No more DHCP agents'))\\n return\\n active_dhcp_agents = [\\n agent for agent in set(enabled_dhcp_agents)\\n if not agents_db.AgentDbMixin.is_agent_down(\\n agent['heartbeat_timestamp'])\\n and agent not in dhcp_agents\\n ]\\n if not active_dhcp_agents:\\n LOG.warn(_LW('No more DHCP agents'))\\n return\\n n_agents = min(len(active_dhcp_agents), n_agents)\\n chosen_agents = random.sample(active_dhcp_agents, n_agents)\\n self._schedule_bind_network(context, chosen_agents, network['id'])\\n return chosen_agents\",\n \"def start_network(self):\\n try:\\n self.topo.build_topo()\\n except:\\n error('Cannot build the topology.')\\n try:\\n self.net = IPNet(topo=self.topo, use_v4=False, use_v6=True)\\n self.net.start()\\n except:\\n self.stop_network()\\n error('Cannot start the network.')\",\n \"def configureDHCP():\\n dhcpStart = config.get(\\\"hotspot\\\", \\\"dhcpstart\\\")\\n dhcpEnd = config.get(\\\"hotspot\\\", \\\"dhcpend\\\")\\n dnsmasqConfig = f\\\"\\\"\\\"#PI Hotspot config\\ndomain-needed\\nbogus-priv\\ndhcp-option=option:dns-server\\ndhcp-authoritative\\ndhcp-range={dhcpStart},{dhcpEnd},1h\\n\\\"\\\"\\\"\\n confFile = open(\\\"/etc/dnsmasq.conf\\\", \\\"w\\\")\\n confFile.write(dnsmasqConfig)\\n confFile.close()\",\n \"def dhcp_used(self, dhcp_used):\\n\\n self._dhcp_used = dhcp_used\",\n \"def change_adp(self, network: str):\\r\\n self.ip = network\\r\\n self.adp = self.ipv4_adp[network]\\r\\n self.mac = self.ipv4_mac[network].replace('-', ':')\\r\\n # print(self.adp, self.ip, self.mac)\\r\",\n \"def create_network(options, vsm_obj):\\n edge_id = get_edge(vsm_obj)\\n if not edge_id:\\n if not add_edge(options):\\n print(\\\"Failed to create edge\\\")\\n return False\\n edge_id = get_edge(vsm_obj)\\n\\n vdn_scope = get_transport_zone(options)\\n virtual_wire = VirtualWire(vdn_scope)\\n name = get_network_name(options)\\n response = virtual_wire.read_by_name(name)\\n if response != \\\"FAILURE\\\":\\n print(\\\"Found network %s already exists\\\" % options.name)\\n return True\\n\\n virtual_wire_create = VirtualWireCreateSpecSchema()\\n virtual_wire_create.name = name\\n virtual_wire_create.tenantId = name\\n virtual_wire_create.description = 'NSX network %s' % name\\n\\n # check if user needs to enable guest vlan tagging,\\n # this is require if one needs to run vlan tests in nested\\n # environment.\\n if hasattr(options, 'guest_vlan'):\\n if options.guest_vlan is True:\\n print(\\\"network %s has guest vlan tagging enabled\\\"\\\\\\n % options.name)\\n virtual_wire_create.guestVlanAllowed = True\\n\\n print(\\\"Creating network %s\\\" % options.name)\\n result = virtual_wire.create(virtual_wire_create)\\n if (result[0].response.status != 201):\\n print \\\"response: %s\\\" % result[0].response.status\\n print \\\"response: %s\\\" % result[0].response.reason\\n return False\\n print(\\\"Changing security settings on the network\\\")\\n set_network_security_policy(options)\\n return add_edge_interface(options, edge_id)\",\n \"def connect(self):\\n self.net.active(True)\\n self.net.config(essid=self.ssid, password=self.pwd, channel=3)\\n\\n while not self.net.active():\\n pass\\n\\n self.net.ifconfig((\\\"192.168.4.5\\\", \\\"255.255.255.0\\\", \\\"192.168.4.1\\\", \\\"208.67.222.222\\\"))\",\n \"def test_ap_hs20_network_preference2(dev, apdev):\\n bssid2 = apdev[1]['bssid']\\n params = hostapd.wpa2_params(ssid=\\\"home\\\", passphrase=\\\"12345678\\\")\\n hostapd.add_ap(apdev[1]['ifname'], params)\\n\\n dev[0].hs20_enable()\\n values = { 'realm': \\\"example.com\\\",\\n 'username': \\\"hs20-test\\\",\\n 'password': \\\"password\\\",\\n 'domain': \\\"example.com\\\",\\n 'priority': \\\"1\\\" }\\n dev[0].add_cred_values(values)\\n\\n id = dev[0].add_network()\\n dev[0].set_network_quoted(id, \\\"ssid\\\", \\\"home\\\")\\n dev[0].set_network_quoted(id, \\\"psk\\\", \\\"12345678\\\")\\n dev[0].request(\\\"ENABLE_NETWORK %s no-connect\\\" % id)\\n\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\"], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if bssid2 not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\\n\\n bssid = apdev[0]['bssid']\\n params = hs20_ap_params()\\n hostapd.add_ap(apdev[0]['ifname'], params)\\n\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\",\\n \\\"INTERWORKING-ALREADY-CONNECTED\\\" ], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if \\\"INTERWORKING-ALREADY-CONNECTED\\\" in ev:\\n raise Exception(\\\"No roam to higher priority network\\\")\\n if bssid not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\",\n \"def setup(self, network, reuse_existing=False):\\n device_id = self.get_device_id(network)\\n port = self.plugin.get_dhcp_port(network.id, device_id)\\n\\n interface_name = self.get_interface_name(network, port)\\n\\n if self.conf.use_namespaces:\\n namespace = NS_PREFIX + network.id\\n else:\\n namespace = None\\n\\n if ip_lib.device_exists(interface_name,\\n self.conf.root_helper,\\n namespace):\\n if not reuse_existing:\\n raise exceptions.PreexistingDeviceFailure(\\n dev_name=interface_name)\\n\\n LOG.debug(_('Reusing existing device: %s.') % interface_name)\\n else:\\n self.driver.plug(network.id,\\n port.id,\\n interface_name,\\n port.mac_address,\\n namespace=namespace)\\n ip_cidrs = []\\n for fixed_ip in port.fixed_ips:\\n subnet = fixed_ip.subnet\\n net = netaddr.IPNetwork(subnet.cidr)\\n ip_cidr = '%s/%s' % (fixed_ip.ip_address, net.prefixlen)\\n ip_cidrs.append(ip_cidr)\\n\\n self.driver.init_l3(interface_name, ip_cidrs,\\n namespace=namespace)\\n\\n return interface_name\",\n \"def modif_network(self):\\n print \\\"preparation du fichier network interfaces\\\"\\n if version_os[\\\"OS\\\"] == \\\"CentOS\\\":\\n self.exec_cmd(\\\"cp %s/etc/sysconfig/network_scripts/ifcfg-eth0 %s/etc/sysconfig/network_scripts/ifcfg-eth0.pre.p2v\\\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\\n else:\\n self.exec_cmd(\\\"cp %s/etc/network/interfaces %s/etc/network/interfaces.post.p2v\\\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\\n self.exec_cmd(\\\"cp %s/etc/network/interfaces.pre.p2v %s/etc/network/interfaces\\\" % (self.rep_vhosts_vm,self.rep_vhosts_vm))\",\n \"def set_enabled(self, bEnabled):\\n\\t\\tcall_sdk_function('PrlVirtNet_SetEnabled', self.handle, bEnabled)\",\n \"def enable_gprs(self):\\n if not has_internet():\\n try:\\n if not self.is_power_on():\\n self.turn_on_modem()\\n except TimeoutError:\\n self.disable_gprs()\\n self.turn_off_modem() # restart modem\\n self.turn_on_modem()\\n time.sleep(1)\\n os.system(\\n 'sudo pon {}'.format(os.path.basename(self.ppp_config_file))\\n )\\n # wait 420 seconds for ppp to start, if not raise TimeoutError\\n self.check_internet_connection()\\n else:\\n self._logger.info('The device is already connected to the internet!')\",\n \"def enable_relays(self):\\n #ensure clock and data are low\\n self.e.clear_bit(7)\\n self.e.clear_bit(5)\\n time.sleep(0.01)\\n\\n #pulse the clock line\\n self.e.set_bit(7)\\n time.sleep(0.01)\\n self.e.clear_bit(7)\",\n \"def test_ap_hs20_network_preference4(dev, apdev):\\n bssid = apdev[0]['bssid']\\n params = hs20_ap_params()\\n hostapd.add_ap(apdev[0]['ifname'], params)\\n\\n bssid2 = apdev[1]['bssid']\\n params = hs20_ap_params(ssid=\\\"test-hs20b\\\")\\n params['hessid'] = bssid2\\n params['anqp_3gpp_cell_net'] = \\\"555,444\\\"\\n params['domain_name'] = \\\"wlan.mnc444.mcc555.3gppnetwork.org\\\"\\n hostapd.add_ap(apdev[1]['ifname'], params)\\n\\n dev[0].hs20_enable()\\n values = { 'realm': \\\"example.com\\\",\\n 'username': \\\"hs20-test\\\",\\n 'password': \\\"password\\\",\\n 'priority': \\\"1\\\" }\\n dev[0].add_cred_values(values)\\n values = { 'imsi': \\\"555444-333222111\\\",\\n 'eap': \\\"SIM\\\",\\n 'milenage': \\\"5122250214c33e723a5dd523fc145fc0:981d464c7c52eb6e5036234984ad0bcf:000000000123\\\" }\\n id = dev[0].add_cred_values(values)\\n\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\"], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if bssid not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\\n\\n dev[0].set_cred(id, \\\"priority\\\", \\\"2\\\")\\n dev[0].request(\\\"INTERWORKING_SELECT auto freq=2412\\\")\\n ev = dev[0].wait_event([\\\"CTRL-EVENT-CONNECTED\\\",\\n \\\"INTERWORKING-ALREADY-CONNECTED\\\" ], timeout=15)\\n if ev is None:\\n raise Exception(\\\"Connection timed out\\\")\\n if \\\"INTERWORKING-ALREADY-CONNECTED\\\" in ev:\\n raise Exception(\\\"No roam to higher priority network\\\")\\n if bssid2 not in ev:\\n raise Exception(\\\"Unexpected network selected\\\")\",\n \"def set_enable(self, enable):\\n\\n with AutoUpdater._lock:\\n if isinstance(enable, Bus):\\n AutoUpdater.remove_link(self._enable)\\n AutoUpdater.add_link(\\n enable,\\n self._enable)\\n else:\\n raise ValueError(\\n \\\"ERROR: Invalid Enable input. Enable must be a \\\"\\n \\\"1-bit Bus or a Connector.\\\")\",\n \"def wifi_connect(self, vap: VirtualAPHostapd) -> bool:\\n config_file_name = \\\"boardfarm_tmp.conf\\\"\\n config_file_path = \\\"/tmp/{}\\\".format(config_file_name)\\n\\n # Create network configuration for SSID\\n bssid = \\\"bssid={}\\\".format(vap.bssid)\\n ssid = \\\"ssid=\\\\\\\"{}\\\\\\\"\\\".format(vap.get_ssid())\\n key = \\\"psk=\\\\\\\"{}\\\\\\\"\\\".format(vap.get_psk())\\n network_config = \\\"network={{\\\\n{}\\\\n{}\\\\n{}\\\\n}}\\\".format(bssid, ssid, key)\\n # Clean up previous configuration\\n self.sendline(\\\"rm -f \\\\\\\"{}\\\\\\\"\\\".format(config_file_path))\\n self.expect(self.prompt)\\n self.sendline(\\\"echo \\\\'{}\\\\' > \\\\\\\"{}\\\\\\\"\\\".format(network_config, config_file_path))\\n self.expect(self.prompt)\\n # Start wpa_supplicant with created configuration\\n # Typical coommand on RPI: wpa_supplicant -B -c/tmp/temp.conf -iwlan0 -Dnl80211,wext\\n self.sudo_sendline(\\\"wpa_supplicant -B -D{} -i{} -c{}\\\".format(\\n self.driver_name, self.iface_wifi, config_file_path))\\n self.expect(\\\"Successfully initialized wpa_supplicant\\\")\\n return bool(self.match)\",\n \"def set_network(self, path, ip=\\\"\\\", netmask=\\\"255.255.255.0\\\", gateway=\\\"\\\"):\\n\\n with open(os.path.join(path, 'etc', 'network', 'interfaces'), 'w') \\\\\\n as f:\\n f.write(\\\"auto lo\\\\niface lo inet loopback\\\\n\\\\n\\\")\\n\\n if len(ip) <= 0:\\n f.write(\\\"auto eth0\\\\niface eth0 inet dhcp\\\\n\\\")\\n else:\\n f.write(\\\"auto eth0\\\\niface eth0 inet static\\\\n\\\")\\n f.write(\\\"\\\\taddress {0}\\\\n\\\\tnetmask {1}\\\\n\\\\tgateway {2}\\\\n\\\".\\\\\\n format(ip, netmask, gateway))\",\n \"def set_management_icmp(enabled=True, deploy=False):\\n\\n if enabled is True:\\n value = \\\"no\\\"\\n elif enabled is False:\\n value = \\\"yes\\\"\\n else:\\n raise CommandExecutionError(\\n \\\"Invalid option provided for service enabled option.\\\"\\n )\\n\\n ret = {}\\n\\n query = {\\n \\\"type\\\": \\\"config\\\",\\n \\\"action\\\": \\\"set\\\",\\n \\\"xpath\\\": \\\"/config/devices/entry[@name='localhost.localdomain']/deviceconfig/system/service\\\",\\n \\\"element\\\": \\\"<disable-icmp>{}</disable-icmp>\\\".format(value),\\n }\\n\\n ret.update(__proxy__[\\\"panos.call\\\"](query))\\n\\n if deploy is True:\\n ret.update(commit())\\n\\n return ret\",\n \"def call_driver(self, action, network):\\n if self.conf.use_namespaces:\\n namespace = NS_PREFIX + network.id\\n else:\\n namespace = None\\n try:\\n # the Driver expects something that is duck typed similar to\\n # the base models.\\n driver = self.dhcp_driver_cls(self.conf,\\n network,\\n self.conf.root_helper,\\n self.device_manager,\\n namespace)\\n getattr(driver, action)()\\n return True\\n\\n except Exception, e:\\n self.needs_resync = True\\n LOG.exception('Unable to %s dhcp.' % action)\",\n \"def config_networking(\\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\\n ):\\n\\n global_ip = vim.vm.customization.GlobalIPSettings()\\n adapter_map = vim.vm.customization.AdapterMapping()\\n adapter_map.adapter = vim.vm.customization.IPSettings()\\n adapter_map.macAddress = network_obj.macAddress\\n if ip:\\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\\n adapter_map.adapter.ip.ipAddress = ip\\n else:\\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\\n adapter_map.adapter.subnetMask = netmask\\n adapter_map.adapter.gateway = gateway\\n global_ip.dnsServerList = dns\\n adapter_map.adapter.dnsDomain = domain\\n ident = vim.vm.customization.LinuxPrep()\\n ident.hostName = vim.vm.customization.FixedName()\\n if guest_hostname:\\n ident.hostName.name = guest_hostname\\n else:\\n ident.hostName.name = self.vm_obj.name\\n custom_spec = vim.vm.customization.Specification()\\n custom_spec.nicSettingMap = [adapter_map]\\n custom_spec.identity = ident\\n custom_spec.globalIPSettings = global_ip\\n return self.vm_obj.Customize(spec=custom_spec)\",\n \"async def enable_ping(\\n client,\\n event,\\n allow: ('bool', 'Enable?') = True,\\n):\\n guild = event.guild\\n if guild is None:\\n abort('Guild only command.')\\n \\n if not event.user_permissions.can_administrator:\\n abort('You must have administrator permission to invoke this command.')\\n \\n application_commands = await client.application_command_guild_get_all(guild)\\n for application_command in application_commands:\\n # If you are not working with overlapping names, a name check should be enough.\\n if application_command.name == ping.name:\\n command_present = True\\n break\\n else:\\n command_present = False\\n \\n if allow:\\n if command_present:\\n content = 'The command is already present.'\\n else:\\n await client.application_command_guild_create(guild, ping.get_schema())\\n content = 'The command has been added.'\\n else:\\n if command_present:\\n await client.application_command_guild_delete(guild, application_command)\\n content = 'The command has been disabled.'\\n else:\\n content = 'The command is not present.'\\n \\n return Embed('Success', content)\",\n \"def test_add_remove_network_from_dhcp_agent(self):\\n # The agent is now bound to the network, we can free the port\\n self.ports_client.delete_port(self.port['id'])\\n agent = dict()\\n agent['agent_type'] = None\\n body = self.admin_agents_client.list_agents()\\n agents = body['agents']\\n for a in agents:\\n if a['agent_type'] == 'DHCP agent':\\n agent = a\\n break\\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\\n 'DHCP agent in agent list though dhcp_agent_scheduler'\\n ' is enabled.')\\n network = self.create_network()\\n network_id = network['id']\\n if self._check_network_in_dhcp_agent(network_id, agent):\\n self._remove_network_from_dhcp_agent(network_id, agent)\\n self._add_dhcp_agent_to_network(network_id, agent)\\n else:\\n self._add_dhcp_agent_to_network(network_id, agent)\\n self._remove_network_from_dhcp_agent(network_id, agent)\",\n \"def test_add_dhcp_agent_to_network(self):\\n network_id = self._create_and_prepare_network_for_agent(\\n self.agent['id'])\\n\\n with self.override_role():\\n self.agents_client.add_dhcp_agent_to_network(\\n self.agent['id'], network_id=network_id)\\n # Clean up is not necessary and might result in 409 being raised.\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def setEnableCondition(*args):\",\n \"def enable_server(self, server):\\n log.info(\\\"Enabling %s in netscaler\\\", server)\\n return self.post(\\\"server?action=enable\\\", {\\\"server\\\": {\\\"name\\\": server}}, content_type=self.content_type(\\\"server\\\"))\",\n \"def _setup_wifi_ap(self):\\n context = self._get_ap_context()\\n try:\\n check_output(['ifconfig', context['hostname']])\\n logger.info('wifi ap {} already setup'.format(context['hostname']))\\n return True\\n except CalledProcessError:\\n logger.info('Setting up virtual access point interface')\\n call(['service', 'hostapd', 'stop'])\\n call(['service', 'dnsmasq', 'stop'])\\n\\n self._write_system_template('/etc/dnsmasq.conf', 'access_point/dnsmasq.conf')\\n self._write_system_template('/etc/hostapd/hostapd.conf', 'access_point/hostapd.conf', context)\\n self._write_system_template('/etc/network/interfaces', 'access_point/interfaces.conf', context)\\n self._write_system_template('/etc/default/hostapd', 'access_point/default_hostapd.conf', context)\\n self._write_system_template('/etc/dhcpcd.conf', 'access_point/dhcpcd.conf', context)\\n \\n call(['systemctl', 'enable', 'hostapd', ])\\n call(['systemctl', 'enable', 'dnsmasq', ])\\n return True\",\n \"def renew_dhcp_lease(self):\\n\\t\\tresponse = os.system(\\\"/sbin/dhclient -r;/sbin/dhclient\\\")\\n\\t\\tif response != 0:\\n\\t\\t\\tprint \\\"Network restart failed. DHCP Lease failed.\\\"\",\n \"async def async_turn_on(self):\\n path = \\\"/interface\\\"\\n param = \\\"default-name\\\"\\n if \\\"-\\\" in self._data[\\\"port-mac-address\\\"]:\\n param = \\\"name\\\"\\n value = self._data[param]\\n mod_param = \\\"disabled\\\"\\n mod_value = False\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n\\n if self._data[\\\"poe-out\\\"] == \\\"off\\\":\\n path = \\\"/interface/ethernet\\\"\\n self._ctrl.set_value(path, param, value, \\\"poe-out\\\", \\\"auto-on\\\")\\n\\n await self._ctrl.force_update()\",\n \"async def enable(self) -> None:\\n try:\\n await self.adguard.request(\\n \\\"parental/enable\\\", method=\\\"POST\\\", data=\\\"sensitivity=TEEN\\\"\\n )\\n except AdGuardHomeError as exception:\\n raise AdGuardHomeError(\\n \\\"Enabling AdGuard Home parental control failed\\\"\\n ) from exception\",\n \"def enable(self, request):\\n if request.data:\\n if self.setpoint_valid:\\n self.enabled = True\\n self.setpoint_valid = True\\n return [True,\\\"DP Controller Enabled\\\"]\\n else:\\n rospy.logwarn(\\\"No valid setpoint received yet.\\\")\\n return [False,\\\"No valid setpoint received yet.\\\"]\\n else:\\n self.enabled = False\\n return [False,\\\"DP Controller Disabled\\\"]\",\n \"def enable(self):\\n self.switch.enable()\\n self._enabled = True\",\n \"def set_network(self, addr, netmask, value):\\n\\n if len(addr) == 4:\\n ipset.ipmap_ipv4_set_network(self.map, addr, netmask, value)\\n return\\n\\n elif len(addr) == 16:\\n ipset.ipmap_ipv6_set_network(self.map, addr, netmask, value)\\n return\\n\\n else:\\n raise ValueError(\\\"Invalid address\\\")\",\n \"def enable(self):\\n if not self.tm_started:\\n for name, tm in self.air_traffic_manager.items():\\n logging.debug(\\\"Starting tm %s\\\" % name)\\n tm.start()\\n tm_started = True\\n\\n logging.debug(\\\"Enabling switch %s\\\" % self.name)\\n self.disabled = False\",\n \"def create_external_network(self, extnet_info, ignore_privious=False):\\n LOG_OBJ.debug(\\\"Creating External Network : \\\")\\n _tenant_name = config.cloud_admin_project\\n _net_name = extnet_info['extnet_name']\\n _gateway = extnet_info['gateway']\\n _cidr = extnet_info['cidr']\\n _start_ip = extnet_info['start_ip']\\n _end_ip = extnet_info['end_ip']\\n\\n if not ignore_privious:\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks\\\"\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_project\\\"]}\\n _body = None\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n output = json.loads(response.data)\\n if output is None:\\n LOG_OBJ.error(\\\"No response from server while getting\\\"\\n \\\" networks.\\\")\\n return\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Getting networks list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n for nets in output['networks']:\\n if nets['router:external']:\\n LOG_OBJ.info(\\\"External Network already created\\\")\\n return\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks.json\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.cloud_admin_info[\\\"token_project\\\"]}\\n _extnet_info = {\\\"network\\\": {\\n \\\"tenant_id\\\": self.cloud_admin_info[\\\"project_id\\\"],\\n \\\"name\\\": _net_name,\\n \\\"router:external\\\": \\\"True\\\",\\n \\\"admin_state_up\\\": True}}\\n _body = json.dumps(_extnet_info)\\n\\n response = self.request(\\\"POST\\\", _url, _headers, _body)\\n output = json.loads(response.data)\\n if output is None:\\n LOG_OBJ.error(\\\"No response from server while creating ext net.\\\")\\n return\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Create ext network Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n _ext_net_id = output['network']['id']\\n LOG_OBJ.debug(\\\"External Network created successfully. ID:%s\\\" %\\n _ext_net_id)\\n\\n # Creating External Subnet\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/subnets.json\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.cloud_admin_info[\\\"token_project\\\"]}\\n _ext_subnet_info = {\\\"subnet\\\": {\\n \\\"ip_version\\\": 4,\\n \\\"allocation_pools\\\": [{\\\"start\\\": _start_ip,\\n \\\"end\\\": _end_ip}],\\n \\\"gateway_ip\\\": _gateway,\\n \\\"enable_dhcp\\\": \\\"False\\\",\\n \\\"network_id\\\": _ext_net_id,\\n \\\"tenant_id\\\": self.cloud_admin_info[\\\"project_id\\\"],\\n \\\"cidr\\\": _cidr,\\n \\\"name\\\": _net_name + \\\"-sub\\\"}}\\n _body = json.dumps(_ext_subnet_info)\\n output = self.request(\\\"POST\\\", _url, _headers, _body)\\n if output is None:\\n LOG_OBJ.error(\\\"No response from server while creating ext-subet\\\")\\n return\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Create subnet Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n return _ext_net_id\",\n \"def _disable_wifi_ap(self):\\n call(['systemctl', 'disable', 'hostapd', ])\\n call(['systemctl', 'disable', 'dnsmasq', ])\\n\\n context = self._get_ap_context()\\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)\",\n \"def _set_rules_mgmt(self, gnp_config, network, host):\\n addr_pool = self.dbapi.address_pool_get(network.pool_uuid)\\n ip_version = IPAddress(f\\\"{addr_pool.network}\\\").version\\n self._add_source_net_filter(gnp_config[\\\"spec\\\"][\\\"ingress\\\"],\\n f\\\"{addr_pool.network}/{addr_pool.prefix}\\\")\\n if (ip_version == 6):\\n self._add_source_net_filter(gnp_config[\\\"spec\\\"][\\\"ingress\\\"], LINK_LOCAL)\\n if (ip_version == 4):\\n # add rule to allow DHCP requests (dhcp-offer have src addr == 0.0.0.0)\\n # worker/storage nodes request IP dynamically\\n rule = self._get_dhcp_rule(host.personality, \\\"UDP\\\", ip_version)\\n gnp_config[\\\"spec\\\"][\\\"ingress\\\"].append(rule)\\n\\n # copy the TCP rule and do the same for IGMP\\n igmp_proto = 2\\n igmp_egr_rule = copy.deepcopy(gnp_config[\\\"spec\\\"][\\\"egress\\\"][0])\\n igmp_egr_rule[\\\"protocol\\\"] = igmp_proto\\n igmp_egr_rule[\\\"metadata\\\"][\\\"annotations\\\"][\\\"name\\\"] = \\\\\\n f\\\"stx-egr-{host.personality}-{network.type}-igmp{ip_version}\\\"\\n gnp_config[\\\"spec\\\"][\\\"egress\\\"].append(igmp_egr_rule)\\n igmp_ingr_rule = copy.deepcopy(gnp_config[\\\"spec\\\"][\\\"ingress\\\"][0])\\n igmp_ingr_rule[\\\"protocol\\\"] = igmp_proto\\n igmp_ingr_rule[\\\"metadata\\\"][\\\"annotations\\\"][\\\"name\\\"] = \\\\\\n f\\\"stx-ingr-{host.personality}-{network.type}-igmp{ip_version}\\\"\\n gnp_config[\\\"spec\\\"][\\\"ingress\\\"].append(igmp_ingr_rule)\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def set_ipaddress(modulo):\\n\\n print ('Configuring IP address...')\\n\\n modulo.write('AT+NETOPEN\\\\r\\\\n'.encode())\\n\\n if _valid_net(modulo): \\n try:\\n modulo.write('AT+IPADDR\\\\r\\\\n'.encode())\\n time.sleep(0.1)\\n except serial.SerialException:\\n print ('... Whitout IP address, try again')\\n if _valid_ip(modulo):\\n print ('IP address configurated')\\n else:\\n print ('IP not configurated')\\n else:\\n print ('Net Already configurated')\\n \\n data = _read_line(modulo)\\n return data\",\n \"def enable(self):\\n\\t\\tresponse = self.client.post(self._endpoint + \\\"/enable\\\")\\n\\t\\treturn bool(response.json[\\\"success\\\"])\",\n \"def enable_host(self, name):\\n from soppa.local import aslocal\\n self.guest_ip = self.guest_ip()\\n self.guest_host_name = name\\n # Host (remote) change\\n self.file.set_setting('/etc/hosts', '{0} {1}'.format('127.0.0.1', self.guest_host_name))\\n # local change\\n aslocal()\\n self.file.set_setting('/etc/hosts', '{0} {1}'.format(self.guest_ip, name))\",\n \"def dhcp_agent_network_remove(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def setAdhocParameters(self, host, mode, **params):\\n self.mode = mode\\n latency = 10\\n self.host = host\\n #delay = 5 * distance\\n try:\\n options = dict( params )\\n self.interface = options[ 'interface' ]\\n except: \\n self.interface = 'wlan0'\\n \\n bandwidth = wifiParameters.set_bw(mode)\\n #self.host.cmd(host, \\\"tc qdisc replace dev %s-%s root netem rate %.2fmbit latency %.2fms delay %.2fms\\\" % (host, self.interface, rate, latency, delay)) \\n self.host.cmd(\\\"tc qdisc add dev %s-%s root tbf rate %smbit latency %sms burst 1540\\\" % (str(host), self.interface, bandwidth, latency))\",\n \"def set_management_telnet(enabled=True, deploy=False):\\n\\n if enabled is True:\\n value = \\\"no\\\"\\n elif enabled is False:\\n value = \\\"yes\\\"\\n else:\\n raise CommandExecutionError(\\n \\\"Invalid option provided for service enabled option.\\\"\\n )\\n\\n ret = {}\\n\\n query = {\\n \\\"type\\\": \\\"config\\\",\\n \\\"action\\\": \\\"set\\\",\\n \\\"xpath\\\": \\\"/config/devices/entry[@name='localhost.localdomain']/deviceconfig/system/service\\\",\\n \\\"element\\\": \\\"<disable-telnet>{}</disable-telnet>\\\".format(value),\\n }\\n\\n ret.update(__proxy__[\\\"panos.call\\\"](query))\\n\\n if deploy is True:\\n ret.update(commit())\\n\\n return ret\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6613713","0.64811337","0.6424179","0.6270945","0.6147416","0.61341375","0.603933","0.5958222","0.59445256","0.5920509","0.5902045","0.5895537","0.58745736","0.57734","0.5736621","0.57331413","0.5694706","0.5690121","0.5680276","0.5655933","0.56160223","0.55988586","0.55268157","0.5492817","0.5471241","0.54687357","0.5458522","0.543408","0.54322517","0.54269654","0.54242057","0.5419952","0.541913","0.54050446","0.53919554","0.5381266","0.5374348","0.53732663","0.53491783","0.53455746","0.53329474","0.5327084","0.5321916","0.5318068","0.52930826","0.5289418","0.528513","0.5284482","0.5275324","0.52696127","0.5266073","0.52555805","0.52500397","0.52152944","0.52110344","0.5208816","0.5203145","0.518202","0.51793593","0.5177554","0.51743835","0.51709783","0.51648414","0.51631844","0.5157022","0.51558846","0.51350915","0.51276803","0.51268125","0.512664","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51213014","0.51111895","0.5105012","0.51040584","0.5094791","0.5089182","0.5084398","0.50830317","0.5079805","0.50788456","0.50732785","0.50658315","0.50650996","0.50452965","0.5044574","0.5043896","0.50424576","0.50400466","0.50218284","0.50150496"],"string":"[\n \"0.6613713\",\n \"0.64811337\",\n \"0.6424179\",\n \"0.6270945\",\n \"0.6147416\",\n \"0.61341375\",\n \"0.603933\",\n \"0.5958222\",\n \"0.59445256\",\n \"0.5920509\",\n \"0.5902045\",\n \"0.5895537\",\n \"0.58745736\",\n \"0.57734\",\n \"0.5736621\",\n \"0.57331413\",\n \"0.5694706\",\n \"0.5690121\",\n \"0.5680276\",\n \"0.5655933\",\n \"0.56160223\",\n \"0.55988586\",\n \"0.55268157\",\n \"0.5492817\",\n \"0.5471241\",\n \"0.54687357\",\n \"0.5458522\",\n \"0.543408\",\n \"0.54322517\",\n \"0.54269654\",\n \"0.54242057\",\n \"0.5419952\",\n \"0.541913\",\n \"0.54050446\",\n \"0.53919554\",\n \"0.5381266\",\n \"0.5374348\",\n \"0.53732663\",\n \"0.53491783\",\n \"0.53455746\",\n \"0.53329474\",\n \"0.5327084\",\n \"0.5321916\",\n \"0.5318068\",\n \"0.52930826\",\n \"0.5289418\",\n \"0.528513\",\n \"0.5284482\",\n \"0.5275324\",\n \"0.52696127\",\n \"0.5266073\",\n \"0.52555805\",\n \"0.52500397\",\n \"0.52152944\",\n \"0.52110344\",\n \"0.5208816\",\n \"0.5203145\",\n \"0.518202\",\n \"0.51793593\",\n \"0.5177554\",\n \"0.51743835\",\n \"0.51709783\",\n \"0.51648414\",\n \"0.51631844\",\n \"0.5157022\",\n \"0.51558846\",\n \"0.51350915\",\n \"0.51276803\",\n \"0.51268125\",\n \"0.512664\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51213014\",\n \"0.51111895\",\n \"0.5105012\",\n \"0.51040584\",\n \"0.5094791\",\n \"0.5089182\",\n \"0.5084398\",\n \"0.50830317\",\n \"0.5079805\",\n \"0.50788456\",\n \"0.50732785\",\n \"0.50658315\",\n \"0.50650996\",\n \"0.50452965\",\n \"0.5044574\",\n \"0.5043896\",\n \"0.50424576\",\n \"0.50400466\",\n \"0.50218284\",\n \"0.50150496\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75427896"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79788,"cells":{"query":{"kind":"string","value":"Disable DHCP for a network known to the agent."},"document":{"kind":"string","value":"def disable_dhcp_helper(self, network_id):\n network = self.cache.get_network_by_id(network_id)\n if network:\n if self.call_driver('disable', network):\n self.cache.remove(network)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dhcp_agent_network_remove(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def _disable_wifi_ap(self):\n call(['systemctl', 'disable', 'hostapd', ])\n call(['systemctl', 'disable', 'dnsmasq', ])\n\n context = self._get_ap_context()\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)","async def async_turn_off(self):\n path = \"/ip/firewall/nat\"\n param = \".id\"\n value = None\n for uid in self._ctrl.data[\"nat\"]:\n if (\n self._ctrl.data[\"nat\"][uid][\"name\"]\n == f\"{self._data['protocol']}:{self._data['dst-port']}\"\n ):\n value = self._ctrl.data[\"nat\"][uid][\".id\"]\n\n mod_param = \"disabled\"\n mod_value = True\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n await self._ctrl.async_update()","async def disable_protection(self) -> None:\n await self._request(\n \"dns_config\", method=\"POST\", json_data={\"protection_enabled\": False},\n )","def destroy(self, network, device_name):\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n\n self.driver.unplug(device_name, namespace=namespace)\n\n self.plugin.release_dhcp_port(network.id,\n self.get_device_id(network))","def net_undefine(network, server, virt=\"Xen\"):\n\n cmd = \"virsh -c %s net-undefine %s 2>/dev/null\" % (virt2uri(virt), network)\n ret, out = run_remote(server, cmd)\n\n return ret","def disable(ctx):\n config_db = ConfigDBConnector()\n config_db.connect()\n config_db.mod_entry(\"NAT_GLOBAL\", \"Values\", {\"admin_mode\": \"disabled\"})","def remove_network_adapter(self, network_obj):\n\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\n nic_spec.device = network_obj\n config_spec = vim.vm.ConfigSpec()\n config_spec.deviceChange = [nic_spec]\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)","def remove_network_from_dhcp_agent(self, dhcp_agent, network_id):\r\n return self.delete((self.agent_path + self.DHCP_NETS + \"/%s\") % (\r\n dhcp_agent, network_id))","def disable_duplicate_address_detection(self, network):\n # For non-external networks, duplicate address detection isn't\n # necessary (and it sometimes results in race conditions for services\n # that attempt to bind to addresses before they're ready).\n\n if network.network_type != network.TYPE_EXTERNAL:\n real_ifname = self.generic_to_host(network.interface.ifname)\n try:\n utils.execute([\n 'sysctl', '-w', 'net.ipv6.conf.%s.accept_dad=0'\n % real_ifname\n ], self.root_helper)\n except RuntimeError:\n LOG.debug(\n 'Failed to disable v6 dad on %s' % real_ifname\n )","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def remove_network(self, name_of_vm):\n try:\n # vmachine = self.get_vm_by_name(name_of_vm)\n vmachine = self.get_dc_object([vim.VirtualMachine], name_of_vm)\n network = None\n devices = vmachine.config.hardware.device\n networks = []\n for device in devices:\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\n networks.append(device)\n status = 'error'\n if not networks:\n log.info(\"INFO: No network adapters connected to the VM to remove\")\n status = 'success'\n else:\n for network in networks:\n name = network.deviceInfo.label\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\n nic_spec.device = network\n remove_nic = vim.vm.ConfigSpec()\n remove_nic.deviceChange = [nic_spec]\n task = WaitForTask(vmachine.ReconfigVM_Task(spec=remove_nic))\n\n if task == 'success':\n log.info(\"removed '{}' network adapter : {}\".format(name, name_of_vm))\n else:\n log.info(\"Could not '{}' Remove Network adapter: {}\".format(name, name_of_vm))\n status = 'success'\n return status\n except Exception as error:\n log.info(\"Error in 'remove_nic' keyword... {} \\n {}\".format(error, error.message))","def network_create_end(self, payload):\n network_id = payload['network']['id']\n self.enable_dhcp_helper(network_id)","def test_delete_network_from_dhcp_agent(self):\n network_id = self._create_and_prepare_network_for_agent(\n self.agent['id'])\n self.agents_client.add_dhcp_agent_to_network(\n self.agent['id'], network_id=network_id)\n # Clean up is not necessary and might result in 409 being raised.\n\n with self.override_role():\n self.agents_client.delete_network_from_dhcp_agent(\n self.agent['id'], network_id=network_id)","def leaveNetwork():\r\n cmd = \"zerotier-cli leave {}\".format(networkID)\r\n os.system(cmd)","def stop_network(self):\n self.net.stop()\n cleanup()","def test_add_remove_network_from_dhcp_agent(self):\n # The agent is now bound to the network, we can free the port\n self.ports_client.delete_port(self.port['id'])\n agent = dict()\n agent['agent_type'] = None\n body = self.admin_agents_client.list_agents()\n agents = body['agents']\n for a in agents:\n if a['agent_type'] == 'DHCP agent':\n agent = a\n break\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\n 'DHCP agent in agent list though dhcp_agent_scheduler'\n ' is enabled.')\n network = self.create_network()\n network_id = network['id']\n if self._check_network_in_dhcp_agent(network_id, agent):\n self._remove_network_from_dhcp_agent(network_id, agent)\n self._add_dhcp_agent_to_network(network_id, agent)\n else:\n self._add_dhcp_agent_to_network(network_id, agent)\n self._remove_network_from_dhcp_agent(network_id, agent)","def disable():\n request = dict(id='gbn')\n _gbn_disable(request)","def enable_dhcp_helper(self, network_id):\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n if not network.admin_state_up:\n return\n\n for subnet in network.subnets:\n if subnet.enable_dhcp:\n if self.call_driver('enable', network):\n self.cache.put(network)\n break","def disableNatlink(self, silent=None):\n if not self.isElevated: raise ElevationError(\"needed for disabling Natlink\")\n # if self.isNatSpeakRunning(): raise NatSpeakRunningError(\"Probably needed for disabling Natlink\")\n\n nssystemini = self.getNSSYSTEMIni()\n section1 = self.section1\n key1 = self.key1\n # trick with None, see testConfigureFunctions...\n # this one disables Natlink:\n try:\n win32api.WriteProfileVal(section1, key1, None, nssystemini)\n except pywintypes.error as details:\n if details[0] == 5:\n print('cannot disable Natlink, you probably need administrator rights')\n else:\n print('unexpected error at disable Natlink')\n raise\n result = self.NatlinkIsEnabled(silent=1)\n if result:\n t = 'Natlink is NOT disabled, you probably need administrator rights, please restart the config program in \"elevated mode\"'\n print(t)\n self.warning(t)\n else:\n if not silent:\n print(('Natlink disabled, restart %s'% self.DNSName))\n print('Note natlink.pyd is NOT UNREGISTERED, but this is not necessary either')","def refresh_dhcp_helper(self, network_id):\n old_network = self.cache.get_network_by_id(network_id)\n if not old_network:\n # DHCP current not running for network.\n return self.enable_dhcp_helper(network_id)\n\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\n\n if new_cidrs and old_cidrs == new_cidrs:\n self.call_driver('reload_allocations', network)\n self.cache.put(network)\n elif new_cidrs:\n if self.call_driver('restart', network):\n self.cache.put(network)\n else:\n self.disable_dhcp_helper(network.id)","def disable_server(self, server):\n log.info(\"Disabling %s in netscaler\", server)\n return self.post(\"server?action=disable\", {\"server\": {\"name\": server}}, content_type=self.content_type(\"server\"))","def _get_net_dhcp_relay(self, context, net_id):\n pass","def delete_network(self, network):\r\n return self.delete(self.network_path % (network))","async def async_turn_off(self):\n path = \"/interface\"\n param = \"default-name\"\n if \"-\" in self._data[\"port-mac-address\"]:\n param = \"name\"\n value = self._data[param]\n mod_param = \"disabled\"\n mod_value = True\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n\n if self._data[\"poe-out\"] == \"auto-on\":\n path = \"/interface/ethernet\"\n self._ctrl.set_value(path, param, value, \"poe-out\", \"off\")\n\n await self._ctrl.async_update()","def deleteNodeNetworkConfig(self,node):\n data = self.connect('delete',\"nodes/%s/network\" % (node),None)\n return data","def disable_vlan(self, mgr, vlanid):\n confstr = snipp.CMD_NO_VLAN_CONF_SNIPPET % vlanid\n confstr = self.create_xml_snippet(confstr)\n mgr.edit_config(target='running', config=confstr)","def ethernet_off(self):\n if not self.healthy:\n self.health_check()\n if not self._ethernet_switch:\n raise errors.CapabilityNotReadyError(\n device_name=self._device_name,\n msg=\"Not set up for ethernet switching.\")\n self._ethernet_switch.switch_power.power_off(self.ethernet_port_number)","def empty_network(network_id=NETWORK_ID):\n return make_net_model({\"id\": network_id,\n \"subnets\": [],\n \"ports\": [],\n \"tenant_id\": \"calico\",\n \"mtu\": neutron_constants.DEFAULT_NETWORK_MTU})","def reset_network(self, instance):\n LOG.debug(\"reset_network\")\n return","def unassign_sdn_networks(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"unassign_sdn_networks\"), kwargs)","def stop_network_nat(self):\n\t\tcmd = [\"/sbin/iptables\",\"-t\",\"nat\",\"-F\"]\n\t\toutput = self.check_output_safe(cmd)\n\t\tself.log.info(\"iptalbes fllushed.\")","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def turn_off_internet(verbose=False):\n __tracebackhide__ = True\n if verbose:\n print(\"Internet access disabled\")\n\n socket.create_connection = check_internet_off(socket_create_connection)\n socket.socket.bind = check_internet_off(socket_bind)\n socket.socket.connect = check_internet_off(socket_connect)\n\n return socket","def delete_overlay_network(self, name=NETWORK_NAME):\n try:\n # An overlay network is usually created in host belonging to a swarm\n self.leave_swarm()\n network = self.docker_client.networks.get(name)\n network.remove()\n except docker.errors.NotFound as nf:\n print(\"Network \"+name+\" not found\")\n except docker.errors.APIError as de:\n print(\"Error deleting overlay network\")\n print de\n exit(1)\n return","def delete_network(name, host, network_type):\n logging.info(\"Deleting %s '%s' from host '%s'\", network_type, name, host.name)\n\n try:\n if network_type.lower() == \"vswitch\":\n host.configManager.networkSystem.RemoveVirtualSwitch(name)\n elif network_type.lower() == \"portgroup\":\n host.configManager.networkSystem.RemovePortGroup(name)\n except vim.fault.NotFound:\n logging.error(\"Tried to remove %s '%s' that does not exist from host '%s'\",\n network_type, name, host.name)\n except vim.fault.ResourceInUse:\n logging.error(\"%s '%s' can't be removed because there are vNICs associated with it\",\n network_type, name)","def delete_network(options, vsm_obj):\n print(\"Disconnecting edge interface attached to this network\")\n edge_id = get_edge(vsm_obj)\n edge = Edge(vsm_obj, '4.0')\n edge.id = edge_id\n vnics = Vnics(edge)\n vnics_schema = vnics.query()\n network = get_network_id(options, get_network_name_on_vc(options))\n for vnic in vnics_schema.vnics:\n if network and vnic.portgroupId == network:\n print(\"Found a matching vnic %s %s\" % (options.name, vnic.index))\n vnic.isConnected = \"False\"\n vnic.portgroupId = None\n vnic.name = \"vnic%s\" % vnic.index\n vnics_schema = VnicsSchema()\n vnics_schema.vnics = [vnic]\n result = vnics.create(vnics_schema)\n if (result[0].response.status != 204):\n print \"update vnic error: %s %s\" \\\n % (result[0].response.status, result[0].response.reason)\n return False\n else:\n break\n else:\n print (\"No matching vnic found\")\n\n vdn_scope = get_transport_zone(options)\n virtual_wire = VirtualWire(vdn_scope)\n vwire = virtual_wire.read_by_name(get_network_name(options))\n name = get_network_name(options)\n if vwire != \"FAILURE\":\n print(\"Found a matching network %s\" % (options.name))\n virtual_wire.id = vwire.objectId\n result = virtual_wire.delete()\n if (result.response.status != 200):\n print (\"Delete vwire error: %s\" % result.response.reason)\n return False\n else:\n print (\"No matching network found\")\n print(\"Network %s deleted\" % (options.name))\n\n return True","def network_delete_event(self, network_info):\n\n net_id = network_info['network_id']\n if net_id not in self.network:\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\n net_id)\n return\n\n segid = self.network[net_id].get('segmentation_id')\n tenant_id = self.network[net_id].get('tenant_id')\n tenant_name = self.get_project_name(tenant_id)\n net = utils.Dict2Obj(self.network[net_id])\n if not tenant_name:\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\n {'tenant_id': tenant_id})\n self.update_network_db(net.id, constants.DELETE_FAIL)\n return\n\n try:\n self.dcnm_client.delete_network(tenant_name, net)\n # Put back the segmentation id into the pool.\n self.seg_drvr.release_segmentation_id(segid)\n\n # Remove entry from database and cache.\n self.delete_network_db(net_id)\n del self.network[net_id]\n snets = [k for k in self.subnet if (\n self.subnet[k].get('network_id') == net_id)]\n [self.subnet.pop(s) for s in snets]\n except dexc.DfaClientRequestFailed:\n LOG.error(_LE('Failed to create network %(net)s.'),\n {'net': net.name})\n self.update_network_db(net_id, constants.DELETE_FAIL)\n # deleting all related VMs\n instances = self.get_vms()\n instances_related = [k for k in instances if k.network_id == net_id]\n for vm in instances_related:\n LOG.debug(\"deleting vm %s because network is deleted\", vm.name)\n self.delete_vm_function(vm.port_id, vm)\n self.network_del_notif(tenant_id, tenant_name, net_id)","def config_networking(\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\n ):\n\n global_ip = vim.vm.customization.GlobalIPSettings()\n adapter_map = vim.vm.customization.AdapterMapping()\n adapter_map.adapter = vim.vm.customization.IPSettings()\n adapter_map.macAddress = network_obj.macAddress\n if ip:\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\n adapter_map.adapter.ip.ipAddress = ip\n else:\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\n adapter_map.adapter.subnetMask = netmask\n adapter_map.adapter.gateway = gateway\n global_ip.dnsServerList = dns\n adapter_map.adapter.dnsDomain = domain\n ident = vim.vm.customization.LinuxPrep()\n ident.hostName = vim.vm.customization.FixedName()\n if guest_hostname:\n ident.hostName.name = guest_hostname\n else:\n ident.hostName.name = self.vm_obj.name\n custom_spec = vim.vm.customization.Specification()\n custom_spec.nicSettingMap = [adapter_map]\n custom_spec.identity = ident\n custom_spec.globalIPSettings = global_ip\n return self.vm_obj.Customize(spec=custom_spec)","def disable_relays(self):\n #ensure clock low and data high\n self.e.clear_bit(7)\n self.e.set_bit(5)\n time.sleep(0.01)\n\n #pulse the clock line\n self.e.set_bit(7)\n time.sleep(0.01)\n self.e.clear_bit(7)\n\n #clear the data line\n self.e.clear_bit(5)","def stopNAT( root ):\n # Flush any currently active rules\n root.cmd( 'iptables -F' )\n root.cmd( 'iptables -t nat -F' )\n \n # Instruct the kernel to stop forwarding\n root.cmd( 'sysctl net.ipv4.ip_forward=0' )\n \n # Restart network-manager\n root.cmd( 'service network-manager start' )","def disable_switch_port(self, mgr, interface):\n confstr = snipp.CMD_NO_SWITCHPORT % (interface)\n confstr = self.create_xml_snippet(confstr)\n LOG.debug(\"NexusDriver: %s\" % confstr)\n mgr.edit_config(target='running', config=confstr)","def _delete_network_vm(args):\n libvirtConn = libvirt.openReadOnly(None)\n if libvirtConn is None:\n print('Cannot contact hypervisor', file=sys.stderr)\n return 1\n net = None\n try:\n net = libvirtConn.networkLookupByName(args.network_name)\n except libvirt.libvirtError:\n print('Cannot find network named [%s]' % args.network_name, file=sys.stderr)\n return 1\n print('Network found:\\n')\n print(xml.dom.minidom.parseString(net.XMLDesc()).toprettyxml(indent=\" \", newl=''))\n print('')\n\n if not args.yes:\n if not input('Really destroy this network ?').strip().lower() in ('y', 'yes'):\n return 1\n return oci_utils.kvm.virt.delete_virtual_network(network_name=args.network_name)","def unplug_port_from_network(self, device_id, device_owner, hostname,\n port_id, network_id, tenant_id, sg, vnic_type,\n switch_bindings=None, segments=None):","async def async_set_wifi_led_off(self):\n return","def firewallOff():\n pass","def elAddNetworkConfigurationWithDhcp(self, device):\n commandSection = self.sectionByName(\"command\")\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\n deviceMatch = re.match(r\"([^0-9]+)([0-9])\", device)\n if deviceMatch:\n # e.g. \"eth0\"\n devicePrefix = deviceMatch.group(1)\n deviceNumber = deviceMatch.group(2)\n deviceNumber = int(deviceNumber)\n for i in range(8, deviceNumber - 1, -1):\n deviceI = devicePrefix + str(i)\n deviceIPlus1 = devicePrefix + str(i + 1)\n # move up by one device each network configuration\n commandSection.string = re.sub(r\"(?m)^([ \\t]*network[ \\t]+.*--device[ \\t]*(?:=|[ \\t])[ \\t]*)\" + re.escape(deviceI) + r\"(.*)$\",\n r\"\\g<1>\" + deviceIPlus1 + r\"\\g<2>\",\n commandSection.string)\n # not --noipv6\n networkConfiguration = \"network --device=\" + device + \" --bootproto=dhcp --onboot=yes --activate\"\n if deviceMatch and deviceNumber == 0:\n # having configuration of eth0 first appears to be more conducive to overall success,\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\n # \"... in installer environment. Device of the first network command is activated if network is required,\n # e.g. in case of network installation ...\",\n commandSection.string = networkConfiguration + \"\\n\" \\\n + \"#\\n\" \\\n + commandSection.string\n else:\n commandSection.string = commandSection.string \\\n + \"#\\n\" \\\n + networkConfiguration + \"\\n\"","def port_nic_remove(switch, port):\n client.port.detach_nic(switch, port)","def release_dhcp_port(self, network_id, device_id):\n LOG.debug(\"release_dhcp_port: %s %s\", network_id, device_id)","def disable(self):\n try:\n self.bus.open(self.BUS_NUMBER)\n self.write(AntennaDeployerCommand.DISARM_ANTS, 0x00)\n self.bus.close()\n return True\n except:\n return False","def disable_proxy_arp():\n tap_name = sys.argv[1]\n print \"Disabling proxy arp on %s\" % tap_name\n with open('/proc/sys/net/ipv4/conf/%s/proxy_arp' % tap_name, 'wb') as f:\n f.write('0')\n print \"Disabled proxy arp on %s\" % tap_name","def filter_create_network_attributes(network, context):\n try_del(network, ['status', 'subnets'])","def disable_discovery(self):","def disabled(config):\n disable(config)\n reload_service('apache2')","def fusion_api_delete_ethernet_network(self, name=None, uri=None, param='', api=None, headers=None):\n return self.ethernet_network.delete(name=name, uri=uri, param=param, api=api, headers=headers)","def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\n modem.configuration[AUTO_LED].set_value(not auto_led)\n modem.configuration[DEADMAN].set_value(not deadman)","def set_dhcp_conn(nic):\n nic.EnableDHCP()\n # After static DNS servers are specified to start using Dynamic Host\n # Configuration Protocol (DHCP) instead of static DNS servers,\n # you can call the method without supplying \"in\" parameters.\n nic.SetDNSServerSearchOrder()","async def disable(self, **kwargs) -> None: # pylint: disable=unused-argument\r\n await self.set_ena(False)","def stop(self):\n logging.debug(\"Network.stop entered:\" + str(self.id))\n # print self.cloudnet\n # res = cn.delete(self.cloudnet)\n notify(\"Stopping network %s\" % self.name)\n # if not self.cloudnet:\n # \n # self.cloudnet = cn.find(id=\"52a24319-f58d-4795-a3bd-c22d87bb65ae\")\n if self.cloudnet:\n res = self.cloudnet.delete()\n else:\n res = True\n return res","def change_adp(self, network: str):\r\n self.ip = network\r\n self.adp = self.ipv4_adp[network]\r\n self.mac = self.ipv4_mac[network].replace('-', ':')\r\n # print(self.adp, self.ip, self.mac)\r","def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def test_dhcp_bind_uninstall(self):\n self._common_uninstall_delete(\n 'esg_id|bind_id', dhcp_bind.delete,\n {'bind': {}},\n delete_args=['dhcpStaticBindingID'],\n delete_kwargs={\n 'uri_parameters': {'edgeId': 'esg_id', 'bindingID': 'bind_id'}\n }\n )","def detach_from_network(self, network):\n # type: ( Union[Network,BoundNetwork]) -> BoundAction\n return self._client.detach_from_network(self, network)","def no_ha(self, vlan_tag):\n\n if len(self.hostnames) == 1:\n LOG.debug(\"Only one machine, doesn't need to configure HA\")\n return True\n\n interface_name = self.in_interface\n if vlan_tag:\n interface_name = \"vlan.\" + vlan_tag\n\n cmd_apv_disable_cluster = ADCDevice.cluster_disable(interface_name)\n cmd_apv_clear_cluster_config = ADCDevice.cluster_clear_virtual_interface(interface_name)\n\n for base_rest_url in self.base_rest_urls:\n # disable the virtual cluster\n self.run_cli_extend(base_rest_url, cmd_apv_disable_cluster)\n\n # clear the configuration of this virtual ifname\n self.run_cli_extend(base_rest_url, cmd_apv_clear_cluster_config)","def schedule(self, plugin, context, network):\n agents_per_network = cfg.CONF.dhcp_agents_per_network\n\n #TODO(gongysh) don't schedule the networks with only\n # subnets whose enable_dhcp is false\n with context.session.begin(subtransactions=True):\n dhcp_agents = plugin.get_dhcp_agents_hosting_networks(\n context, [network['id']], active=True)\n if len(dhcp_agents) >= agents_per_network:\n LOG.debug('Network %s is hosted already',\n network['id'])\n return\n n_agents = agents_per_network - len(dhcp_agents)\n enabled_dhcp_agents = plugin.get_agents_db(\n context, filters={\n 'agent_type': [constants.AGENT_TYPE_DHCP],\n 'admin_state_up': [True]})\n if not enabled_dhcp_agents:\n LOG.warn(_LW('No more DHCP agents'))\n return\n active_dhcp_agents = [\n agent for agent in set(enabled_dhcp_agents)\n if not agents_db.AgentDbMixin.is_agent_down(\n agent['heartbeat_timestamp'])\n and agent not in dhcp_agents\n ]\n if not active_dhcp_agents:\n LOG.warn(_LW('No more DHCP agents'))\n return\n n_agents = min(len(active_dhcp_agents), n_agents)\n chosen_agents = random.sample(active_dhcp_agents, n_agents)\n self._schedule_bind_network(context, chosen_agents, network['id'])\n return chosen_agents","def setNetwork(self, network):\n # type: (str)->None\n\n self._validator.validate_one(\n 'network', VALID_OPTS['network'], network)\n self._ifAttributes['network'] = network","def run(self, network_create_args=None):\n network = self.neutron.create_network(**(network_create_args or {}))\n self.neutron.delete_network(network[\"id\"])","def wifi_off(self):\n self._clear_read_buffer()\n self._write_cmd(\"PE00\")\n time.sleep(100e-3)","def disable(self):\n self._disable_monitor()\n self._pinger.stop()","def AddNetworkFlag(parser):\n help_text = \"\"\"\\\n The VPC network from which the AlloyDB instance is accessible via private\n IP. For example, projects/myProject/global/networks/default. This setting\n cannot be updated after it is set.\n \"\"\"\n parser.add_argument('--network', help=help_text)","def _unset_dns_mitm(self, device_name: str) -> None:\n device = getattr(self.dev_mgr, device_name)\n host = f\"{device_name}.boardfarm.com\"\n if device:\n self.dev_mgr.wan.modify_dns_hosts(\n {host: device.dns.dnsv4[host] + device.dns.dnsv6[host]}\n )\n self.mitm_dns_active.remove(device_name)\n else:\n logger.warning(f\"Device {device_name} is not found in device manager.\")","def scp_disable(task):\n cmd = \"no ip scp server enable\"\n task.run(task=netmiko_send_config, config_commands=cmd)\n task.run(task=netmiko_save_config)\n c_print(f\"*** {task.host}: SCP has been disabled ***\")","def unhide_all(network=None, base_url=DEFAULT_BASE_URL):\n net_suid = networks.get_network_suid(network, base_url=base_url)\n res = None\n\n node_names = networks.get_all_nodes(net_suid, base_url=base_url)\n if len(node_names) > 0:\n res = set_node_property_bypass(node_names, new_values='true', visual_property='NODE_VISIBLE', network=network,\n base_url=base_url)\n\n edge_names = networks.get_all_edges(net_suid, base_url=base_url)\n if len(edge_names) > 0:\n res = set_edge_property_bypass(edge_names, new_values='true', visual_property='EDGE_VISIBLE', network=network,\n base_url=base_url)\n\n return res\n # TODO: res is ambiguous ... unclear what it really should be","def disable(self):\n logging.debug(\"Disabling switch %s\" % self.name)\n self.disabled = True","def remove_pvrdma(self, network_obj):\n\n self.remove_network_adapter(network_obj)","def disassociate_elastic_ip(ElasticIp=None):\n pass","def stop_dhcp_response(self, mac):\n self.change_dhcp_response_time(mac, -1)","def remove_gateway(self, network_ref):\n raise NotImplementedError()","def dcnm_network_delete_event(self, network_info):\n seg_id = network_info.get('segmentation_id')\n if not seg_id:\n LOG.error(_LE('Failed to delete network. Invalid network '\n 'info %s.'), network_info)\n query_net = self.get_network_by_segid(seg_id)\n if not query_net:\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\n 'does not exist.'), {'segid': seg_id})\n return\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\n LOG.info(_LI(\"Service network %s, returning\"), query_net.name)\n return\n # Send network delete request to neutron\n try:\n del_net = self.network.pop(query_net.network_id)\n self.neutronclient.delete_network(query_net.network_id)\n self.delete_network_db(query_net.network_id)\n except Exception as exc:\n # Failed to delete network.\n # Put back the entry to the local cache???\n self.network[query_net.network_id] = del_net\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\n '%(network)s. Reason %(err)s.'),\n {'network': query_net.name, 'err': str(exc)})","def disable_vlan_on_trunk_int(self, host, vlan_id, intf_type, interface):\n\n dbg_str = self._dbg_str(host, \"disable\", vlan_id,\n interface=interface, intf_type=intf_type)\n LOG.debug(dbg_str)\n\n conn = self._connect(host)\n try:\n if_name = self._get_ifname(intf_type, interface)\n self._rem_intf_from_vlan(conn, vlan_id, if_name)\n except Exception as e:\n raise cexc.NOSConfigFailed(config=dbg_str, exc=e)\n conn.close()","def test_list_networks_hosted_by_one_dhcp_agent(self):\n with self.override_role():\n self.agents_client.list_networks_hosted_by_one_dhcp_agent(\n self.agent['id'])","def _create_and_prepare_network_for_agent(self, agent_id):\n network_id = self.create_network()['id']\n\n if self._check_network_in_dhcp_agent(network_id, agent_id):\n self.agents_client.delete_network_from_dhcp_agent(\n agent_id=agent_id, network_id=network_id)\n\n return network_id","def do_network_detach(cs, args):\n opts = {}\n opts['container'] = args.container\n opts['network'] = args.network\n opts['port'] = args.port\n opts = zun_utils.remove_null_parms(**opts)\n try:\n cs.containers.network_detach(**opts)\n print(\"Request to detach network from container %s \"\n \"has been accepted.\" % args.container)\n except Exception as e:\n print(\"Detach network from container %(container)s \"\n \"failed: %(e)s\" % {'container': args.container, 'e': e})","def DisableSourceDestCheck(gwtable):\n ec2 = boto3.client(\"ec2\")\n for gw in gwtable:\n for networkif in gw[4]:\n if networkif[2] == True:\n ec2.modify_network_interface_attribute(NetworkInterfaceId=networkif[1],\n SourceDestCheck={'Value': False})\n print('SourceDestCheck on ENI ', networkif[1], ' was set to false')","def disableConfig(self):\n self._configdialog.setEnabled(False)","def network_settings():\n for host in online_hosts:\n with settings(warn_only=True):\n\n result1 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSDomainName <Domain Name>\")\n if result1.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSDomainName failed \")\n\n result2 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSServer1 \"+colo_dns[DEFAULT_COLO ][0])\n if result2.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSServer1 failed \")\n\n result3 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSServer2 \"+colo_dns[DEFAULT_COLO ][1])\n if result3.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSServer2 failed \")","def delete_net(self, net_id):\n LOG_OBJ.debug(\"Deleting network %s\" % net_id)\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks/\" + \\\n net_id + \".json\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n response = self.request(\"DELETE\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\"No response from Server while deleting net:%s\" %\n net_id)\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Deletion of Network Failed with status %s \" %\n response.status)\n return response.status\n\n LOG_OBJ.info(\"Deleted the network : %s \" % net_id)\n return True","def bdev_nvme_disable_controller(client, name, cntlid):\n\n params = {'name': name}\n\n if cntlid is not None:\n params['cntlid'] = cntlid\n\n return client.call('bdev_nvme_disable_controller', params)","def test_delete_network(self):\n pass","def ensure_nat_gateway_absent(ec2_client=None, module=None):\n try:\n results = dict(changed=False)\n nat_gateways_result = ec2_client.describe_nat_gateways(NatGatewayIds=[module.params.get('nat_gateway_id')])\n if nat_gateways_result:\n nat_gateway = nat_gateways_result.get('NatGateways')[0]\n if nat_gateway.get('State') in ('pending',\n 'available'):\n if module.check_mode:\n results['changed'] = True\n module.exit_json(**results)\n else:\n delete_nat_gateway(ec2_client=ec2_client,\n module=module,\n nat_gateway=nat_gateway)\n module.exit_json(**results)\n except botocore.exceptions.ClientError as ce:\n module.fail_json(msg=ce.message, **camel_dict_to_snake_dict(ce.response))","def _nixie_disable():\n # type: () -> None\n GPIO.output(NIXIE_nOE, GPIO.HIGH)","def filter_update_network_attributes(network, context):\n try_del(network, ['id', 'status', 'subnets', 'tenant_id'])","def delete_network(self, network_o):\n tenant_mo = self.moDir.lookupByDn(network_o.group)\n\n # Filters the tenant children in memory looking for the ones that belongs to the Ap class with an specific name\n ap_list = filter(lambda x: type(x).__name__ == 'Ap' and x.name == AP_NAME,\n self.query_child_objects(str(tenant_mo.dn)))\n if len(ap_list) > 0:\n network_ap = ap_list[0]\n # Filters the tenant children in memory looking for the ones that belongs to the AEPg\n # class with an specific name\n network_epgs = filter(lambda x: type(x).__name__ == 'AEPg' and x.name == network_o.name + VLAN_SUFIX +\n str(network_o.encapsulation),\n self.query_child_objects(str(network_ap.dn)))\n # Removes EPG\n if len(network_epgs) > 0:\n network_epgs[0].delete()\n self.commit(network_epgs[0])\n\n # Filters the tenant children in memory looking for the ones that belongs to the BD class and with an specific\n # name\n bd_list = filter(lambda x: type(x).__name__ == 'BD' and x.name == VLAN + str(network_o.encapsulation),\n self.query_child_objects(str(tenant_mo.dn)))\n if len(bd_list) > 0:\n # Removes bridge domain\n bd_list[0].delete()\n self.commit(bd_list[0])","def Disable(self):\n handler = self.get_command_object(\"Disable\")\n handler()","def _disable(self):\n self.enabled = False","def internet_advertising_disabled(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"internet_advertising_disabled\")","def set_network_security_policy(options):\n network = get_network_on_vc(options)\n name = get_network_name(options)\n\n config_spec = Vim.Dvs.DistributedVirtualPortgroup.ConfigSpec()\n config_info = network.GetConfig()\n config_spec.description = config_info.name\n config_spec.name = name\n config_spec.configVersion = config_info.configVersion\n\n true_policy = Vim.BoolPolicy()\n true_policy.value = True\n dvs_port_setting = Vim.VMwareDVSPortSetting()\n security_policy = Vim.DVSSecurityPolicy()\n security_policy.allowPromiscuous = true_policy\n security_policy.forgedTransmits = true_policy\n security_policy.macChanges = true_policy\n security_policy.inherited = False\n dvs_port_setting.securityPolicy = security_policy\n config_spec.defaultPortConfig = dvs_port_setting\n\n network.ReconfigureDVPortgroup_Task(config_spec)","def unconfigure_global_dual_active_recovery_reload_disable(device):\n # build a list of commands to send\n # Add stackwise-virtual as first element in the list\n # Enables dual-active recovery-reload\n command_list = ['stackwise-virtual']\n command_list.append(f'no dual-active recovery-reload-disable')\n try:\n output = device.configure(command_list)\n except SubCommandFailure:\n raise SubCommandFailure('Failed to Disable global stackwise-virtual dual-active recovery-reload')\n return output","def unconfigure_lldp(device):\r\n try:\r\n device.configure('no lldp run')\r\n except SubCommandFailure as e:\r\n raise SubCommandFailure(\r\n \"Could not configure LLDP\"\r\n \"Error: {error}\".format(error=e)\r\n )"],"string":"[\n \"def dhcp_agent_network_remove(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def _disable_wifi_ap(self):\\n call(['systemctl', 'disable', 'hostapd', ])\\n call(['systemctl', 'disable', 'dnsmasq', ])\\n\\n context = self._get_ap_context()\\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)\",\n \"async def async_turn_off(self):\\n path = \\\"/ip/firewall/nat\\\"\\n param = \\\".id\\\"\\n value = None\\n for uid in self._ctrl.data[\\\"nat\\\"]:\\n if (\\n self._ctrl.data[\\\"nat\\\"][uid][\\\"name\\\"]\\n == f\\\"{self._data['protocol']}:{self._data['dst-port']}\\\"\\n ):\\n value = self._ctrl.data[\\\"nat\\\"][uid][\\\".id\\\"]\\n\\n mod_param = \\\"disabled\\\"\\n mod_value = True\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n await self._ctrl.async_update()\",\n \"async def disable_protection(self) -> None:\\n await self._request(\\n \\\"dns_config\\\", method=\\\"POST\\\", json_data={\\\"protection_enabled\\\": False},\\n )\",\n \"def destroy(self, network, device_name):\\n if self.conf.use_namespaces:\\n namespace = NS_PREFIX + network.id\\n else:\\n namespace = None\\n\\n self.driver.unplug(device_name, namespace=namespace)\\n\\n self.plugin.release_dhcp_port(network.id,\\n self.get_device_id(network))\",\n \"def net_undefine(network, server, virt=\\\"Xen\\\"):\\n\\n cmd = \\\"virsh -c %s net-undefine %s 2>/dev/null\\\" % (virt2uri(virt), network)\\n ret, out = run_remote(server, cmd)\\n\\n return ret\",\n \"def disable(ctx):\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n config_db.mod_entry(\\\"NAT_GLOBAL\\\", \\\"Values\\\", {\\\"admin_mode\\\": \\\"disabled\\\"})\",\n \"def remove_network_adapter(self, network_obj):\\n\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\\n nic_spec.device = network_obj\\n config_spec = vim.vm.ConfigSpec()\\n config_spec.deviceChange = [nic_spec]\\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)\",\n \"def remove_network_from_dhcp_agent(self, dhcp_agent, network_id):\\r\\n return self.delete((self.agent_path + self.DHCP_NETS + \\\"/%s\\\") % (\\r\\n dhcp_agent, network_id))\",\n \"def disable_duplicate_address_detection(self, network):\\n # For non-external networks, duplicate address detection isn't\\n # necessary (and it sometimes results in race conditions for services\\n # that attempt to bind to addresses before they're ready).\\n\\n if network.network_type != network.TYPE_EXTERNAL:\\n real_ifname = self.generic_to_host(network.interface.ifname)\\n try:\\n utils.execute([\\n 'sysctl', '-w', 'net.ipv6.conf.%s.accept_dad=0'\\n % real_ifname\\n ], self.root_helper)\\n except RuntimeError:\\n LOG.debug(\\n 'Failed to disable v6 dad on %s' % real_ifname\\n )\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def remove_network(self, name_of_vm):\\n try:\\n # vmachine = self.get_vm_by_name(name_of_vm)\\n vmachine = self.get_dc_object([vim.VirtualMachine], name_of_vm)\\n network = None\\n devices = vmachine.config.hardware.device\\n networks = []\\n for device in devices:\\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\\n networks.append(device)\\n status = 'error'\\n if not networks:\\n log.info(\\\"INFO: No network adapters connected to the VM to remove\\\")\\n status = 'success'\\n else:\\n for network in networks:\\n name = network.deviceInfo.label\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\\n nic_spec.device = network\\n remove_nic = vim.vm.ConfigSpec()\\n remove_nic.deviceChange = [nic_spec]\\n task = WaitForTask(vmachine.ReconfigVM_Task(spec=remove_nic))\\n\\n if task == 'success':\\n log.info(\\\"removed '{}' network adapter : {}\\\".format(name, name_of_vm))\\n else:\\n log.info(\\\"Could not '{}' Remove Network adapter: {}\\\".format(name, name_of_vm))\\n status = 'success'\\n return status\\n except Exception as error:\\n log.info(\\\"Error in 'remove_nic' keyword... {} \\\\n {}\\\".format(error, error.message))\",\n \"def network_create_end(self, payload):\\n network_id = payload['network']['id']\\n self.enable_dhcp_helper(network_id)\",\n \"def test_delete_network_from_dhcp_agent(self):\\n network_id = self._create_and_prepare_network_for_agent(\\n self.agent['id'])\\n self.agents_client.add_dhcp_agent_to_network(\\n self.agent['id'], network_id=network_id)\\n # Clean up is not necessary and might result in 409 being raised.\\n\\n with self.override_role():\\n self.agents_client.delete_network_from_dhcp_agent(\\n self.agent['id'], network_id=network_id)\",\n \"def leaveNetwork():\\r\\n cmd = \\\"zerotier-cli leave {}\\\".format(networkID)\\r\\n os.system(cmd)\",\n \"def stop_network(self):\\n self.net.stop()\\n cleanup()\",\n \"def test_add_remove_network_from_dhcp_agent(self):\\n # The agent is now bound to the network, we can free the port\\n self.ports_client.delete_port(self.port['id'])\\n agent = dict()\\n agent['agent_type'] = None\\n body = self.admin_agents_client.list_agents()\\n agents = body['agents']\\n for a in agents:\\n if a['agent_type'] == 'DHCP agent':\\n agent = a\\n break\\n self.assertEqual(agent['agent_type'], 'DHCP agent', 'Could not find '\\n 'DHCP agent in agent list though dhcp_agent_scheduler'\\n ' is enabled.')\\n network = self.create_network()\\n network_id = network['id']\\n if self._check_network_in_dhcp_agent(network_id, agent):\\n self._remove_network_from_dhcp_agent(network_id, agent)\\n self._add_dhcp_agent_to_network(network_id, agent)\\n else:\\n self._add_dhcp_agent_to_network(network_id, agent)\\n self._remove_network_from_dhcp_agent(network_id, agent)\",\n \"def disable():\\n request = dict(id='gbn')\\n _gbn_disable(request)\",\n \"def enable_dhcp_helper(self, network_id):\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n if not network.admin_state_up:\\n return\\n\\n for subnet in network.subnets:\\n if subnet.enable_dhcp:\\n if self.call_driver('enable', network):\\n self.cache.put(network)\\n break\",\n \"def disableNatlink(self, silent=None):\\n if not self.isElevated: raise ElevationError(\\\"needed for disabling Natlink\\\")\\n # if self.isNatSpeakRunning(): raise NatSpeakRunningError(\\\"Probably needed for disabling Natlink\\\")\\n\\n nssystemini = self.getNSSYSTEMIni()\\n section1 = self.section1\\n key1 = self.key1\\n # trick with None, see testConfigureFunctions...\\n # this one disables Natlink:\\n try:\\n win32api.WriteProfileVal(section1, key1, None, nssystemini)\\n except pywintypes.error as details:\\n if details[0] == 5:\\n print('cannot disable Natlink, you probably need administrator rights')\\n else:\\n print('unexpected error at disable Natlink')\\n raise\\n result = self.NatlinkIsEnabled(silent=1)\\n if result:\\n t = 'Natlink is NOT disabled, you probably need administrator rights, please restart the config program in \\\"elevated mode\\\"'\\n print(t)\\n self.warning(t)\\n else:\\n if not silent:\\n print(('Natlink disabled, restart %s'% self.DNSName))\\n print('Note natlink.pyd is NOT UNREGISTERED, but this is not necessary either')\",\n \"def refresh_dhcp_helper(self, network_id):\\n old_network = self.cache.get_network_by_id(network_id)\\n if not old_network:\\n # DHCP current not running for network.\\n return self.enable_dhcp_helper(network_id)\\n\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\\n\\n if new_cidrs and old_cidrs == new_cidrs:\\n self.call_driver('reload_allocations', network)\\n self.cache.put(network)\\n elif new_cidrs:\\n if self.call_driver('restart', network):\\n self.cache.put(network)\\n else:\\n self.disable_dhcp_helper(network.id)\",\n \"def disable_server(self, server):\\n log.info(\\\"Disabling %s in netscaler\\\", server)\\n return self.post(\\\"server?action=disable\\\", {\\\"server\\\": {\\\"name\\\": server}}, content_type=self.content_type(\\\"server\\\"))\",\n \"def _get_net_dhcp_relay(self, context, net_id):\\n pass\",\n \"def delete_network(self, network):\\r\\n return self.delete(self.network_path % (network))\",\n \"async def async_turn_off(self):\\n path = \\\"/interface\\\"\\n param = \\\"default-name\\\"\\n if \\\"-\\\" in self._data[\\\"port-mac-address\\\"]:\\n param = \\\"name\\\"\\n value = self._data[param]\\n mod_param = \\\"disabled\\\"\\n mod_value = True\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n\\n if self._data[\\\"poe-out\\\"] == \\\"auto-on\\\":\\n path = \\\"/interface/ethernet\\\"\\n self._ctrl.set_value(path, param, value, \\\"poe-out\\\", \\\"off\\\")\\n\\n await self._ctrl.async_update()\",\n \"def deleteNodeNetworkConfig(self,node):\\n data = self.connect('delete',\\\"nodes/%s/network\\\" % (node),None)\\n return data\",\n \"def disable_vlan(self, mgr, vlanid):\\n confstr = snipp.CMD_NO_VLAN_CONF_SNIPPET % vlanid\\n confstr = self.create_xml_snippet(confstr)\\n mgr.edit_config(target='running', config=confstr)\",\n \"def ethernet_off(self):\\n if not self.healthy:\\n self.health_check()\\n if not self._ethernet_switch:\\n raise errors.CapabilityNotReadyError(\\n device_name=self._device_name,\\n msg=\\\"Not set up for ethernet switching.\\\")\\n self._ethernet_switch.switch_power.power_off(self.ethernet_port_number)\",\n \"def empty_network(network_id=NETWORK_ID):\\n return make_net_model({\\\"id\\\": network_id,\\n \\\"subnets\\\": [],\\n \\\"ports\\\": [],\\n \\\"tenant_id\\\": \\\"calico\\\",\\n \\\"mtu\\\": neutron_constants.DEFAULT_NETWORK_MTU})\",\n \"def reset_network(self, instance):\\n LOG.debug(\\\"reset_network\\\")\\n return\",\n \"def unassign_sdn_networks(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"unassign_sdn_networks\\\"), kwargs)\",\n \"def stop_network_nat(self):\\n\\t\\tcmd = [\\\"/sbin/iptables\\\",\\\"-t\\\",\\\"nat\\\",\\\"-F\\\"]\\n\\t\\toutput = self.check_output_safe(cmd)\\n\\t\\tself.log.info(\\\"iptalbes fllushed.\\\")\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def turn_off_internet(verbose=False):\\n __tracebackhide__ = True\\n if verbose:\\n print(\\\"Internet access disabled\\\")\\n\\n socket.create_connection = check_internet_off(socket_create_connection)\\n socket.socket.bind = check_internet_off(socket_bind)\\n socket.socket.connect = check_internet_off(socket_connect)\\n\\n return socket\",\n \"def delete_overlay_network(self, name=NETWORK_NAME):\\n try:\\n # An overlay network is usually created in host belonging to a swarm\\n self.leave_swarm()\\n network = self.docker_client.networks.get(name)\\n network.remove()\\n except docker.errors.NotFound as nf:\\n print(\\\"Network \\\"+name+\\\" not found\\\")\\n except docker.errors.APIError as de:\\n print(\\\"Error deleting overlay network\\\")\\n print de\\n exit(1)\\n return\",\n \"def delete_network(name, host, network_type):\\n logging.info(\\\"Deleting %s '%s' from host '%s'\\\", network_type, name, host.name)\\n\\n try:\\n if network_type.lower() == \\\"vswitch\\\":\\n host.configManager.networkSystem.RemoveVirtualSwitch(name)\\n elif network_type.lower() == \\\"portgroup\\\":\\n host.configManager.networkSystem.RemovePortGroup(name)\\n except vim.fault.NotFound:\\n logging.error(\\\"Tried to remove %s '%s' that does not exist from host '%s'\\\",\\n network_type, name, host.name)\\n except vim.fault.ResourceInUse:\\n logging.error(\\\"%s '%s' can't be removed because there are vNICs associated with it\\\",\\n network_type, name)\",\n \"def delete_network(options, vsm_obj):\\n print(\\\"Disconnecting edge interface attached to this network\\\")\\n edge_id = get_edge(vsm_obj)\\n edge = Edge(vsm_obj, '4.0')\\n edge.id = edge_id\\n vnics = Vnics(edge)\\n vnics_schema = vnics.query()\\n network = get_network_id(options, get_network_name_on_vc(options))\\n for vnic in vnics_schema.vnics:\\n if network and vnic.portgroupId == network:\\n print(\\\"Found a matching vnic %s %s\\\" % (options.name, vnic.index))\\n vnic.isConnected = \\\"False\\\"\\n vnic.portgroupId = None\\n vnic.name = \\\"vnic%s\\\" % vnic.index\\n vnics_schema = VnicsSchema()\\n vnics_schema.vnics = [vnic]\\n result = vnics.create(vnics_schema)\\n if (result[0].response.status != 204):\\n print \\\"update vnic error: %s %s\\\" \\\\\\n % (result[0].response.status, result[0].response.reason)\\n return False\\n else:\\n break\\n else:\\n print (\\\"No matching vnic found\\\")\\n\\n vdn_scope = get_transport_zone(options)\\n virtual_wire = VirtualWire(vdn_scope)\\n vwire = virtual_wire.read_by_name(get_network_name(options))\\n name = get_network_name(options)\\n if vwire != \\\"FAILURE\\\":\\n print(\\\"Found a matching network %s\\\" % (options.name))\\n virtual_wire.id = vwire.objectId\\n result = virtual_wire.delete()\\n if (result.response.status != 200):\\n print (\\\"Delete vwire error: %s\\\" % result.response.reason)\\n return False\\n else:\\n print (\\\"No matching network found\\\")\\n print(\\\"Network %s deleted\\\" % (options.name))\\n\\n return True\",\n \"def network_delete_event(self, network_info):\\n\\n net_id = network_info['network_id']\\n if net_id not in self.network:\\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\\n net_id)\\n return\\n\\n segid = self.network[net_id].get('segmentation_id')\\n tenant_id = self.network[net_id].get('tenant_id')\\n tenant_name = self.get_project_name(tenant_id)\\n net = utils.Dict2Obj(self.network[net_id])\\n if not tenant_name:\\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\\n {'tenant_id': tenant_id})\\n self.update_network_db(net.id, constants.DELETE_FAIL)\\n return\\n\\n try:\\n self.dcnm_client.delete_network(tenant_name, net)\\n # Put back the segmentation id into the pool.\\n self.seg_drvr.release_segmentation_id(segid)\\n\\n # Remove entry from database and cache.\\n self.delete_network_db(net_id)\\n del self.network[net_id]\\n snets = [k for k in self.subnet if (\\n self.subnet[k].get('network_id') == net_id)]\\n [self.subnet.pop(s) for s in snets]\\n except dexc.DfaClientRequestFailed:\\n LOG.error(_LE('Failed to create network %(net)s.'),\\n {'net': net.name})\\n self.update_network_db(net_id, constants.DELETE_FAIL)\\n # deleting all related VMs\\n instances = self.get_vms()\\n instances_related = [k for k in instances if k.network_id == net_id]\\n for vm in instances_related:\\n LOG.debug(\\\"deleting vm %s because network is deleted\\\", vm.name)\\n self.delete_vm_function(vm.port_id, vm)\\n self.network_del_notif(tenant_id, tenant_name, net_id)\",\n \"def config_networking(\\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\\n ):\\n\\n global_ip = vim.vm.customization.GlobalIPSettings()\\n adapter_map = vim.vm.customization.AdapterMapping()\\n adapter_map.adapter = vim.vm.customization.IPSettings()\\n adapter_map.macAddress = network_obj.macAddress\\n if ip:\\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\\n adapter_map.adapter.ip.ipAddress = ip\\n else:\\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\\n adapter_map.adapter.subnetMask = netmask\\n adapter_map.adapter.gateway = gateway\\n global_ip.dnsServerList = dns\\n adapter_map.adapter.dnsDomain = domain\\n ident = vim.vm.customization.LinuxPrep()\\n ident.hostName = vim.vm.customization.FixedName()\\n if guest_hostname:\\n ident.hostName.name = guest_hostname\\n else:\\n ident.hostName.name = self.vm_obj.name\\n custom_spec = vim.vm.customization.Specification()\\n custom_spec.nicSettingMap = [adapter_map]\\n custom_spec.identity = ident\\n custom_spec.globalIPSettings = global_ip\\n return self.vm_obj.Customize(spec=custom_spec)\",\n \"def disable_relays(self):\\n #ensure clock low and data high\\n self.e.clear_bit(7)\\n self.e.set_bit(5)\\n time.sleep(0.01)\\n\\n #pulse the clock line\\n self.e.set_bit(7)\\n time.sleep(0.01)\\n self.e.clear_bit(7)\\n\\n #clear the data line\\n self.e.clear_bit(5)\",\n \"def stopNAT( root ):\\n # Flush any currently active rules\\n root.cmd( 'iptables -F' )\\n root.cmd( 'iptables -t nat -F' )\\n \\n # Instruct the kernel to stop forwarding\\n root.cmd( 'sysctl net.ipv4.ip_forward=0' )\\n \\n # Restart network-manager\\n root.cmd( 'service network-manager start' )\",\n \"def disable_switch_port(self, mgr, interface):\\n confstr = snipp.CMD_NO_SWITCHPORT % (interface)\\n confstr = self.create_xml_snippet(confstr)\\n LOG.debug(\\\"NexusDriver: %s\\\" % confstr)\\n mgr.edit_config(target='running', config=confstr)\",\n \"def _delete_network_vm(args):\\n libvirtConn = libvirt.openReadOnly(None)\\n if libvirtConn is None:\\n print('Cannot contact hypervisor', file=sys.stderr)\\n return 1\\n net = None\\n try:\\n net = libvirtConn.networkLookupByName(args.network_name)\\n except libvirt.libvirtError:\\n print('Cannot find network named [%s]' % args.network_name, file=sys.stderr)\\n return 1\\n print('Network found:\\\\n')\\n print(xml.dom.minidom.parseString(net.XMLDesc()).toprettyxml(indent=\\\" \\\", newl=''))\\n print('')\\n\\n if not args.yes:\\n if not input('Really destroy this network ?').strip().lower() in ('y', 'yes'):\\n return 1\\n return oci_utils.kvm.virt.delete_virtual_network(network_name=args.network_name)\",\n \"def unplug_port_from_network(self, device_id, device_owner, hostname,\\n port_id, network_id, tenant_id, sg, vnic_type,\\n switch_bindings=None, segments=None):\",\n \"async def async_set_wifi_led_off(self):\\n return\",\n \"def firewallOff():\\n pass\",\n \"def elAddNetworkConfigurationWithDhcp(self, device):\\n commandSection = self.sectionByName(\\\"command\\\")\\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\\n deviceMatch = re.match(r\\\"([^0-9]+)([0-9])\\\", device)\\n if deviceMatch:\\n # e.g. \\\"eth0\\\"\\n devicePrefix = deviceMatch.group(1)\\n deviceNumber = deviceMatch.group(2)\\n deviceNumber = int(deviceNumber)\\n for i in range(8, deviceNumber - 1, -1):\\n deviceI = devicePrefix + str(i)\\n deviceIPlus1 = devicePrefix + str(i + 1)\\n # move up by one device each network configuration\\n commandSection.string = re.sub(r\\\"(?m)^([ \\\\t]*network[ \\\\t]+.*--device[ \\\\t]*(?:=|[ \\\\t])[ \\\\t]*)\\\" + re.escape(deviceI) + r\\\"(.*)$\\\",\\n r\\\"\\\\g<1>\\\" + deviceIPlus1 + r\\\"\\\\g<2>\\\",\\n commandSection.string)\\n # not --noipv6\\n networkConfiguration = \\\"network --device=\\\" + device + \\\" --bootproto=dhcp --onboot=yes --activate\\\"\\n if deviceMatch and deviceNumber == 0:\\n # having configuration of eth0 first appears to be more conducive to overall success,\\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\\n # \\\"... in installer environment. Device of the first network command is activated if network is required,\\n # e.g. in case of network installation ...\\\",\\n commandSection.string = networkConfiguration + \\\"\\\\n\\\" \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + commandSection.string\\n else:\\n commandSection.string = commandSection.string \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + networkConfiguration + \\\"\\\\n\\\"\",\n \"def port_nic_remove(switch, port):\\n client.port.detach_nic(switch, port)\",\n \"def release_dhcp_port(self, network_id, device_id):\\n LOG.debug(\\\"release_dhcp_port: %s %s\\\", network_id, device_id)\",\n \"def disable(self):\\n try:\\n self.bus.open(self.BUS_NUMBER)\\n self.write(AntennaDeployerCommand.DISARM_ANTS, 0x00)\\n self.bus.close()\\n return True\\n except:\\n return False\",\n \"def disable_proxy_arp():\\n tap_name = sys.argv[1]\\n print \\\"Disabling proxy arp on %s\\\" % tap_name\\n with open('/proc/sys/net/ipv4/conf/%s/proxy_arp' % tap_name, 'wb') as f:\\n f.write('0')\\n print \\\"Disabled proxy arp on %s\\\" % tap_name\",\n \"def filter_create_network_attributes(network, context):\\n try_del(network, ['status', 'subnets'])\",\n \"def disable_discovery(self):\",\n \"def disabled(config):\\n disable(config)\\n reload_service('apache2')\",\n \"def fusion_api_delete_ethernet_network(self, name=None, uri=None, param='', api=None, headers=None):\\n return self.ethernet_network.delete(name=name, uri=uri, param=param, api=api, headers=headers)\",\n \"def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\\n modem.configuration[AUTO_LED].set_value(not auto_led)\\n modem.configuration[DEADMAN].set_value(not deadman)\",\n \"def set_dhcp_conn(nic):\\n nic.EnableDHCP()\\n # After static DNS servers are specified to start using Dynamic Host\\n # Configuration Protocol (DHCP) instead of static DNS servers,\\n # you can call the method without supplying \\\"in\\\" parameters.\\n nic.SetDNSServerSearchOrder()\",\n \"async def disable(self, **kwargs) -> None: # pylint: disable=unused-argument\\r\\n await self.set_ena(False)\",\n \"def stop(self):\\n logging.debug(\\\"Network.stop entered:\\\" + str(self.id))\\n # print self.cloudnet\\n # res = cn.delete(self.cloudnet)\\n notify(\\\"Stopping network %s\\\" % self.name)\\n # if not self.cloudnet:\\n # \\n # self.cloudnet = cn.find(id=\\\"52a24319-f58d-4795-a3bd-c22d87bb65ae\\\")\\n if self.cloudnet:\\n res = self.cloudnet.delete()\\n else:\\n res = True\\n return res\",\n \"def change_adp(self, network: str):\\r\\n self.ip = network\\r\\n self.adp = self.ipv4_adp[network]\\r\\n self.mac = self.ipv4_mac[network].replace('-', ':')\\r\\n # print(self.adp, self.ip, self.mac)\\r\",\n \"def dhcp_agent_network_add(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def test_dhcp_bind_uninstall(self):\\n self._common_uninstall_delete(\\n 'esg_id|bind_id', dhcp_bind.delete,\\n {'bind': {}},\\n delete_args=['dhcpStaticBindingID'],\\n delete_kwargs={\\n 'uri_parameters': {'edgeId': 'esg_id', 'bindingID': 'bind_id'}\\n }\\n )\",\n \"def detach_from_network(self, network):\\n # type: ( Union[Network,BoundNetwork]) -> BoundAction\\n return self._client.detach_from_network(self, network)\",\n \"def no_ha(self, vlan_tag):\\n\\n if len(self.hostnames) == 1:\\n LOG.debug(\\\"Only one machine, doesn't need to configure HA\\\")\\n return True\\n\\n interface_name = self.in_interface\\n if vlan_tag:\\n interface_name = \\\"vlan.\\\" + vlan_tag\\n\\n cmd_apv_disable_cluster = ADCDevice.cluster_disable(interface_name)\\n cmd_apv_clear_cluster_config = ADCDevice.cluster_clear_virtual_interface(interface_name)\\n\\n for base_rest_url in self.base_rest_urls:\\n # disable the virtual cluster\\n self.run_cli_extend(base_rest_url, cmd_apv_disable_cluster)\\n\\n # clear the configuration of this virtual ifname\\n self.run_cli_extend(base_rest_url, cmd_apv_clear_cluster_config)\",\n \"def schedule(self, plugin, context, network):\\n agents_per_network = cfg.CONF.dhcp_agents_per_network\\n\\n #TODO(gongysh) don't schedule the networks with only\\n # subnets whose enable_dhcp is false\\n with context.session.begin(subtransactions=True):\\n dhcp_agents = plugin.get_dhcp_agents_hosting_networks(\\n context, [network['id']], active=True)\\n if len(dhcp_agents) >= agents_per_network:\\n LOG.debug('Network %s is hosted already',\\n network['id'])\\n return\\n n_agents = agents_per_network - len(dhcp_agents)\\n enabled_dhcp_agents = plugin.get_agents_db(\\n context, filters={\\n 'agent_type': [constants.AGENT_TYPE_DHCP],\\n 'admin_state_up': [True]})\\n if not enabled_dhcp_agents:\\n LOG.warn(_LW('No more DHCP agents'))\\n return\\n active_dhcp_agents = [\\n agent for agent in set(enabled_dhcp_agents)\\n if not agents_db.AgentDbMixin.is_agent_down(\\n agent['heartbeat_timestamp'])\\n and agent not in dhcp_agents\\n ]\\n if not active_dhcp_agents:\\n LOG.warn(_LW('No more DHCP agents'))\\n return\\n n_agents = min(len(active_dhcp_agents), n_agents)\\n chosen_agents = random.sample(active_dhcp_agents, n_agents)\\n self._schedule_bind_network(context, chosen_agents, network['id'])\\n return chosen_agents\",\n \"def setNetwork(self, network):\\n # type: (str)->None\\n\\n self._validator.validate_one(\\n 'network', VALID_OPTS['network'], network)\\n self._ifAttributes['network'] = network\",\n \"def run(self, network_create_args=None):\\n network = self.neutron.create_network(**(network_create_args or {}))\\n self.neutron.delete_network(network[\\\"id\\\"])\",\n \"def wifi_off(self):\\n self._clear_read_buffer()\\n self._write_cmd(\\\"PE00\\\")\\n time.sleep(100e-3)\",\n \"def disable(self):\\n self._disable_monitor()\\n self._pinger.stop()\",\n \"def AddNetworkFlag(parser):\\n help_text = \\\"\\\"\\\"\\\\\\n The VPC network from which the AlloyDB instance is accessible via private\\n IP. For example, projects/myProject/global/networks/default. This setting\\n cannot be updated after it is set.\\n \\\"\\\"\\\"\\n parser.add_argument('--network', help=help_text)\",\n \"def _unset_dns_mitm(self, device_name: str) -> None:\\n device = getattr(self.dev_mgr, device_name)\\n host = f\\\"{device_name}.boardfarm.com\\\"\\n if device:\\n self.dev_mgr.wan.modify_dns_hosts(\\n {host: device.dns.dnsv4[host] + device.dns.dnsv6[host]}\\n )\\n self.mitm_dns_active.remove(device_name)\\n else:\\n logger.warning(f\\\"Device {device_name} is not found in device manager.\\\")\",\n \"def scp_disable(task):\\n cmd = \\\"no ip scp server enable\\\"\\n task.run(task=netmiko_send_config, config_commands=cmd)\\n task.run(task=netmiko_save_config)\\n c_print(f\\\"*** {task.host}: SCP has been disabled ***\\\")\",\n \"def unhide_all(network=None, base_url=DEFAULT_BASE_URL):\\n net_suid = networks.get_network_suid(network, base_url=base_url)\\n res = None\\n\\n node_names = networks.get_all_nodes(net_suid, base_url=base_url)\\n if len(node_names) > 0:\\n res = set_node_property_bypass(node_names, new_values='true', visual_property='NODE_VISIBLE', network=network,\\n base_url=base_url)\\n\\n edge_names = networks.get_all_edges(net_suid, base_url=base_url)\\n if len(edge_names) > 0:\\n res = set_edge_property_bypass(edge_names, new_values='true', visual_property='EDGE_VISIBLE', network=network,\\n base_url=base_url)\\n\\n return res\\n # TODO: res is ambiguous ... unclear what it really should be\",\n \"def disable(self):\\n logging.debug(\\\"Disabling switch %s\\\" % self.name)\\n self.disabled = True\",\n \"def remove_pvrdma(self, network_obj):\\n\\n self.remove_network_adapter(network_obj)\",\n \"def disassociate_elastic_ip(ElasticIp=None):\\n pass\",\n \"def stop_dhcp_response(self, mac):\\n self.change_dhcp_response_time(mac, -1)\",\n \"def remove_gateway(self, network_ref):\\n raise NotImplementedError()\",\n \"def dcnm_network_delete_event(self, network_info):\\n seg_id = network_info.get('segmentation_id')\\n if not seg_id:\\n LOG.error(_LE('Failed to delete network. Invalid network '\\n 'info %s.'), network_info)\\n query_net = self.get_network_by_segid(seg_id)\\n if not query_net:\\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\\n 'does not exist.'), {'segid': seg_id})\\n return\\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\\n LOG.info(_LI(\\\"Service network %s, returning\\\"), query_net.name)\\n return\\n # Send network delete request to neutron\\n try:\\n del_net = self.network.pop(query_net.network_id)\\n self.neutronclient.delete_network(query_net.network_id)\\n self.delete_network_db(query_net.network_id)\\n except Exception as exc:\\n # Failed to delete network.\\n # Put back the entry to the local cache???\\n self.network[query_net.network_id] = del_net\\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\\n '%(network)s. Reason %(err)s.'),\\n {'network': query_net.name, 'err': str(exc)})\",\n \"def disable_vlan_on_trunk_int(self, host, vlan_id, intf_type, interface):\\n\\n dbg_str = self._dbg_str(host, \\\"disable\\\", vlan_id,\\n interface=interface, intf_type=intf_type)\\n LOG.debug(dbg_str)\\n\\n conn = self._connect(host)\\n try:\\n if_name = self._get_ifname(intf_type, interface)\\n self._rem_intf_from_vlan(conn, vlan_id, if_name)\\n except Exception as e:\\n raise cexc.NOSConfigFailed(config=dbg_str, exc=e)\\n conn.close()\",\n \"def test_list_networks_hosted_by_one_dhcp_agent(self):\\n with self.override_role():\\n self.agents_client.list_networks_hosted_by_one_dhcp_agent(\\n self.agent['id'])\",\n \"def _create_and_prepare_network_for_agent(self, agent_id):\\n network_id = self.create_network()['id']\\n\\n if self._check_network_in_dhcp_agent(network_id, agent_id):\\n self.agents_client.delete_network_from_dhcp_agent(\\n agent_id=agent_id, network_id=network_id)\\n\\n return network_id\",\n \"def do_network_detach(cs, args):\\n opts = {}\\n opts['container'] = args.container\\n opts['network'] = args.network\\n opts['port'] = args.port\\n opts = zun_utils.remove_null_parms(**opts)\\n try:\\n cs.containers.network_detach(**opts)\\n print(\\\"Request to detach network from container %s \\\"\\n \\\"has been accepted.\\\" % args.container)\\n except Exception as e:\\n print(\\\"Detach network from container %(container)s \\\"\\n \\\"failed: %(e)s\\\" % {'container': args.container, 'e': e})\",\n \"def DisableSourceDestCheck(gwtable):\\n ec2 = boto3.client(\\\"ec2\\\")\\n for gw in gwtable:\\n for networkif in gw[4]:\\n if networkif[2] == True:\\n ec2.modify_network_interface_attribute(NetworkInterfaceId=networkif[1],\\n SourceDestCheck={'Value': False})\\n print('SourceDestCheck on ENI ', networkif[1], ' was set to false')\",\n \"def disableConfig(self):\\n self._configdialog.setEnabled(False)\",\n \"def network_settings():\\n for host in online_hosts:\\n with settings(warn_only=True):\\n\\n result1 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSDomainName <Domain Name>\\\")\\n if result1.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSDomainName failed \\\")\\n\\n result2 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSServer1 \\\"+colo_dns[DEFAULT_COLO ][0])\\n if result2.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSServer1 failed \\\")\\n\\n result3 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSServer2 \\\"+colo_dns[DEFAULT_COLO ][1])\\n if result3.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSServer2 failed \\\")\",\n \"def delete_net(self, net_id):\\n LOG_OBJ.debug(\\\"Deleting network %s\\\" % net_id)\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks/\\\" + \\\\\\n net_id + \\\".json\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.project_info[\\\"token_project\\\"]}\\n _body = None\\n\\n response = self.request(\\\"DELETE\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while deleting net:%s\\\" %\\n net_id)\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Deletion of Network Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n LOG_OBJ.info(\\\"Deleted the network : %s \\\" % net_id)\\n return True\",\n \"def bdev_nvme_disable_controller(client, name, cntlid):\\n\\n params = {'name': name}\\n\\n if cntlid is not None:\\n params['cntlid'] = cntlid\\n\\n return client.call('bdev_nvme_disable_controller', params)\",\n \"def test_delete_network(self):\\n pass\",\n \"def ensure_nat_gateway_absent(ec2_client=None, module=None):\\n try:\\n results = dict(changed=False)\\n nat_gateways_result = ec2_client.describe_nat_gateways(NatGatewayIds=[module.params.get('nat_gateway_id')])\\n if nat_gateways_result:\\n nat_gateway = nat_gateways_result.get('NatGateways')[0]\\n if nat_gateway.get('State') in ('pending',\\n 'available'):\\n if module.check_mode:\\n results['changed'] = True\\n module.exit_json(**results)\\n else:\\n delete_nat_gateway(ec2_client=ec2_client,\\n module=module,\\n nat_gateway=nat_gateway)\\n module.exit_json(**results)\\n except botocore.exceptions.ClientError as ce:\\n module.fail_json(msg=ce.message, **camel_dict_to_snake_dict(ce.response))\",\n \"def _nixie_disable():\\n # type: () -> None\\n GPIO.output(NIXIE_nOE, GPIO.HIGH)\",\n \"def filter_update_network_attributes(network, context):\\n try_del(network, ['id', 'status', 'subnets', 'tenant_id'])\",\n \"def delete_network(self, network_o):\\n tenant_mo = self.moDir.lookupByDn(network_o.group)\\n\\n # Filters the tenant children in memory looking for the ones that belongs to the Ap class with an specific name\\n ap_list = filter(lambda x: type(x).__name__ == 'Ap' and x.name == AP_NAME,\\n self.query_child_objects(str(tenant_mo.dn)))\\n if len(ap_list) > 0:\\n network_ap = ap_list[0]\\n # Filters the tenant children in memory looking for the ones that belongs to the AEPg\\n # class with an specific name\\n network_epgs = filter(lambda x: type(x).__name__ == 'AEPg' and x.name == network_o.name + VLAN_SUFIX +\\n str(network_o.encapsulation),\\n self.query_child_objects(str(network_ap.dn)))\\n # Removes EPG\\n if len(network_epgs) > 0:\\n network_epgs[0].delete()\\n self.commit(network_epgs[0])\\n\\n # Filters the tenant children in memory looking for the ones that belongs to the BD class and with an specific\\n # name\\n bd_list = filter(lambda x: type(x).__name__ == 'BD' and x.name == VLAN + str(network_o.encapsulation),\\n self.query_child_objects(str(tenant_mo.dn)))\\n if len(bd_list) > 0:\\n # Removes bridge domain\\n bd_list[0].delete()\\n self.commit(bd_list[0])\",\n \"def Disable(self):\\n handler = self.get_command_object(\\\"Disable\\\")\\n handler()\",\n \"def _disable(self):\\n self.enabled = False\",\n \"def internet_advertising_disabled(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"internet_advertising_disabled\\\")\",\n \"def set_network_security_policy(options):\\n network = get_network_on_vc(options)\\n name = get_network_name(options)\\n\\n config_spec = Vim.Dvs.DistributedVirtualPortgroup.ConfigSpec()\\n config_info = network.GetConfig()\\n config_spec.description = config_info.name\\n config_spec.name = name\\n config_spec.configVersion = config_info.configVersion\\n\\n true_policy = Vim.BoolPolicy()\\n true_policy.value = True\\n dvs_port_setting = Vim.VMwareDVSPortSetting()\\n security_policy = Vim.DVSSecurityPolicy()\\n security_policy.allowPromiscuous = true_policy\\n security_policy.forgedTransmits = true_policy\\n security_policy.macChanges = true_policy\\n security_policy.inherited = False\\n dvs_port_setting.securityPolicy = security_policy\\n config_spec.defaultPortConfig = dvs_port_setting\\n\\n network.ReconfigureDVPortgroup_Task(config_spec)\",\n \"def unconfigure_global_dual_active_recovery_reload_disable(device):\\n # build a list of commands to send\\n # Add stackwise-virtual as first element in the list\\n # Enables dual-active recovery-reload\\n command_list = ['stackwise-virtual']\\n command_list.append(f'no dual-active recovery-reload-disable')\\n try:\\n output = device.configure(command_list)\\n except SubCommandFailure:\\n raise SubCommandFailure('Failed to Disable global stackwise-virtual dual-active recovery-reload')\\n return output\",\n \"def unconfigure_lldp(device):\\r\\n try:\\r\\n device.configure('no lldp run')\\r\\n except SubCommandFailure as e:\\r\\n raise SubCommandFailure(\\r\\n \\\"Could not configure LLDP\\\"\\r\\n \\\"Error: {error}\\\".format(error=e)\\r\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7135386","0.6868477","0.64515764","0.62737286","0.62714326","0.6229779","0.6223586","0.6125738","0.61127055","0.6056309","0.59756565","0.5961324","0.59271187","0.5900384","0.5843378","0.57802856","0.57735544","0.5729633","0.5697703","0.56655335","0.5631795","0.55926836","0.5544187","0.55370057","0.5530936","0.5529251","0.5525791","0.5514267","0.5451133","0.54253817","0.5423428","0.54230905","0.54204315","0.5414738","0.53991514","0.5395853","0.5384529","0.5369502","0.5361435","0.5345625","0.5342971","0.53420514","0.53269184","0.5314849","0.5311474","0.5277877","0.52649033","0.5255146","0.5239345","0.5228572","0.52127814","0.5205719","0.51988494","0.519643","0.5191826","0.5186667","0.5186416","0.5177528","0.51699835","0.51651365","0.5163934","0.5136902","0.5124169","0.51208633","0.50961965","0.5070006","0.50688654","0.5062925","0.50620675","0.505197","0.5050401","0.504948","0.504729","0.5027732","0.50236744","0.502235","0.5020305","0.5012974","0.50032985","0.50007933","0.49946392","0.49877638","0.49838987","0.49720585","0.49713564","0.49677482","0.49569097","0.49555358","0.4953368","0.49497","0.494905","0.4948835","0.4948297","0.49456143","0.4941451","0.49366277","0.49349245","0.49300212","0.4917645","0.4913021"],"string":"[\n \"0.7135386\",\n \"0.6868477\",\n \"0.64515764\",\n \"0.62737286\",\n \"0.62714326\",\n \"0.6229779\",\n \"0.6223586\",\n \"0.6125738\",\n \"0.61127055\",\n \"0.6056309\",\n \"0.59756565\",\n \"0.5961324\",\n \"0.59271187\",\n \"0.5900384\",\n \"0.5843378\",\n \"0.57802856\",\n \"0.57735544\",\n \"0.5729633\",\n \"0.5697703\",\n \"0.56655335\",\n \"0.5631795\",\n \"0.55926836\",\n \"0.5544187\",\n \"0.55370057\",\n \"0.5530936\",\n \"0.5529251\",\n \"0.5525791\",\n \"0.5514267\",\n \"0.5451133\",\n \"0.54253817\",\n \"0.5423428\",\n \"0.54230905\",\n \"0.54204315\",\n \"0.5414738\",\n \"0.53991514\",\n \"0.5395853\",\n \"0.5384529\",\n \"0.5369502\",\n \"0.5361435\",\n \"0.5345625\",\n \"0.5342971\",\n \"0.53420514\",\n \"0.53269184\",\n \"0.5314849\",\n \"0.5311474\",\n \"0.5277877\",\n \"0.52649033\",\n \"0.5255146\",\n \"0.5239345\",\n \"0.5228572\",\n \"0.52127814\",\n \"0.5205719\",\n \"0.51988494\",\n \"0.519643\",\n \"0.5191826\",\n \"0.5186667\",\n \"0.5186416\",\n \"0.5177528\",\n \"0.51699835\",\n \"0.51651365\",\n \"0.5163934\",\n \"0.5136902\",\n \"0.5124169\",\n \"0.51208633\",\n \"0.50961965\",\n \"0.5070006\",\n \"0.50688654\",\n \"0.5062925\",\n \"0.50620675\",\n \"0.505197\",\n \"0.5050401\",\n \"0.504948\",\n \"0.504729\",\n \"0.5027732\",\n \"0.50236744\",\n \"0.502235\",\n \"0.5020305\",\n \"0.5012974\",\n \"0.50032985\",\n \"0.50007933\",\n \"0.49946392\",\n \"0.49877638\",\n \"0.49838987\",\n \"0.49720585\",\n \"0.49713564\",\n \"0.49677482\",\n \"0.49569097\",\n \"0.49555358\",\n \"0.4953368\",\n \"0.49497\",\n \"0.494905\",\n \"0.4948835\",\n \"0.4948297\",\n \"0.49456143\",\n \"0.4941451\",\n \"0.49366277\",\n \"0.49349245\",\n \"0.49300212\",\n \"0.4917645\",\n \"0.4913021\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7986895"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79789,"cells":{"query":{"kind":"string","value":"Refresh or disable DHCP for a network depending on the current state of the network."},"document":{"kind":"string","value":"def refresh_dhcp_helper(self, network_id):\n old_network = self.cache.get_network_by_id(network_id)\n if not old_network:\n # DHCP current not running for network.\n return self.enable_dhcp_helper(network_id)\n\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n old_cidrs = set(s.cidr for s in old_network.subnets if s.enable_dhcp)\n new_cidrs = set(s.cidr for s in network.subnets if s.enable_dhcp)\n\n if new_cidrs and old_cidrs == new_cidrs:\n self.call_driver('reload_allocations', network)\n self.cache.put(network)\n elif new_cidrs:\n if self.call_driver('restart', network):\n self.cache.put(network)\n else:\n self.disable_dhcp_helper(network.id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def disable_dhcp_helper(self, network_id):\n network = self.cache.get_network_by_id(network_id)\n if network:\n if self.call_driver('disable', network):\n self.cache.remove(network)","def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n active_networks = set(self.plugin_rpc.get_active_networks())\n for deleted_id in known_networks - active_networks:\n self.disable_dhcp_helper(deleted_id)\n\n for network_id in active_networks:\n self.refresh_dhcp_helper(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Unable to sync network state.'))","def enable_dhcp_helper(self, network_id):\n try:\n network = self.plugin_rpc.get_network_info(network_id)\n except:\n self.needs_resync = True\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\n return\n\n if not network.admin_state_up:\n return\n\n for subnet in network.subnets:\n if subnet.enable_dhcp:\n if self.call_driver('enable', network):\n self.cache.put(network)\n break","async def async_turn_off(self):\n path = \"/ip/firewall/nat\"\n param = \".id\"\n value = None\n for uid in self._ctrl.data[\"nat\"]:\n if (\n self._ctrl.data[\"nat\"][uid][\"name\"]\n == f\"{self._data['protocol']}:{self._data['dst-port']}\"\n ):\n value = self._ctrl.data[\"nat\"][uid][\".id\"]\n\n mod_param = \"disabled\"\n mod_value = True\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n await self._ctrl.async_update()","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def reconfigure(self):\n log.debug('Reconfiguring and restarting the DHCP daemon...')\n\n # Don't set the daemon running status here, but let the status\n # check take care of that.\n\n p = Properties(self.storage, CONFIG_SECTION)\n p.addCallback(self.changed).\\\n addCallback(lambda trigger: p.load()).\\\n addCallback(self.emit_config, p).\\\n addCallback(self.restart_daemon).\\\n addErrback(self.restart_error)","def _disable_wifi_ap(self):\n call(['systemctl', 'disable', 'hostapd', ])\n call(['systemctl', 'disable', 'dnsmasq', ])\n\n context = self._get_ap_context()\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)","def dhcp_agent_network_remove(self, dhcp_net_info):\n self.turn_on_dhcp_check()","async def async_turn_off(self):\n path = \"/interface\"\n param = \"default-name\"\n if \"-\" in self._data[\"port-mac-address\"]:\n param = \"name\"\n value = self._data[param]\n mod_param = \"disabled\"\n mod_value = True\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n\n if self._data[\"poe-out\"] == \"auto-on\":\n path = \"/interface/ethernet\"\n self._ctrl.set_value(path, param, value, \"poe-out\", \"off\")\n\n await self._ctrl.async_update()","def reset_network(self, instance):\n LOG.debug(\"reset_network\")\n return","def renew_dhcp_lease(self):\n\t\tresponse = os.system(\"/sbin/dhclient -r;/sbin/dhclient\")\n\t\tif response != 0:\n\t\t\tprint \"Network restart failed. DHCP Lease failed.\"","def network_create_end(self, payload):\n network_id = payload['network']['id']\n self.enable_dhcp_helper(network_id)","def update_network(self, context, net_id, network):\n\n LOG.debug(_(\"QuantumRestProxyV2.update_network() called\"))\n\n # Validate Args\n if network[\"network\"].get(\"admin_state_up\"):\n if network[\"network\"][\"admin_state_up\"] is False:\n LOG.warning(_(\"Network with admin_state_up=False are not yet \"\n \"supported by this plugin. Ignoring setting for \"\n \"network %s\", net_name))\n\n # update DB\n orig_net = super(QuantumRestProxyV2, self).get_network(context, net_id)\n tenant_id = orig_net[\"tenant_id\"]\n new_net = super(QuantumRestProxyV2, self).update_network(\n context, net_id, network)\n\n # update network on network controller\n if new_net[\"name\"] != orig_net[\"name\"]:\n try:\n resource = NETWORKS_PATH % (tenant_id, net_id)\n data = {\n \"network\": new_net,\n }\n ret = self.servers.put(resource, data)\n if not self.servers.action_success(ret):\n raise RemoteRestError(ret[2])\n except RemoteRestError as e:\n LOG.error(_(\"QuantumRestProxyV2: Unable to update remote \"\n \"network: %s\"), e.message)\n # reset network to original state\n super(QuantumRestProxyV2, self).update_network(\n context, id, orig_net)\n raise\n\n # return updated network\n return new_net","def apply_network_settings(**settings):\n if 'require_reboot' not in settings:\n settings['require_reboot'] = False\n if 'apply_hostname' not in settings:\n settings['apply_hostname'] = False\n\n hostname_res = True\n if settings['apply_hostname'] in _CONFIG_TRUE:\n if 'hostname' in settings:\n hostname_res = __salt__['network.mod_hostname'](settings['hostname'])\n else:\n log.warning(\n 'The network state sls is trying to apply hostname '\n 'changes but no hostname is defined.'\n )\n hostname_res = False\n\n res = True\n if settings['require_reboot'] in _CONFIG_TRUE:\n log.warning(\n 'The network state sls is requiring a reboot of the system to '\n 'properly apply network configuration.'\n )\n res = True\n else:\n res = __salt__['cmd.run']('/etc/netstart restart')\n\n return hostname_res and res","async def async_set_wifi_led_off(self):\n return","def subnet_update_end(self, payload):\n network_id = payload['subnet']['network_id']\n self.refresh_dhcp_helper(network_id)","def wifi_off(self):\n self._clear_read_buffer()\n self._write_cmd(\"PE00\")\n time.sleep(100e-3)","def dvs_update_network(self):\n self.show_step(1)\n self.env.revert_snapshot(\"dvs_vcenter_systest_setup\")\n\n cluster_id = self.fuel_web.get_last_created_cluster()\n\n self.show_step(2)\n os_ip = self.fuel_web.get_public_vip(cluster_id)\n os_conn = os_actions.OpenStackActions(\n os_ip, SERVTEST_USERNAME,\n SERVTEST_PASSWORD,\n SERVTEST_TENANT)\n\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\n\n net_1 = os_conn.create_network(\n network_name=self.net_data[0].keys()[0],\n tenant_id=tenant.id)['network']\n\n os_conn.create_subnet(\n subnet_name=net_1['name'],\n network_id=net_1['id'],\n cidr=self.net_data[0][self.net_data[0].keys()[0]],\n ip_version=4)\n\n assert_true(os_conn.get_network(net_1['name'])['id'] == net_1['id'])\n\n self.show_step(3)\n os_conn.neutron.update_network(net_1[\"id\"],\n {\"network\": {\"name\": 'net_2'}})\n\n assert_true(os_conn.get_network('net_2')['id'] == net_1['id'])\n\n self.show_step(4)\n default_net = os_conn.nova.networks.find(label=self.inter_net_name)\n os_conn.neutron.update_network(\n default_net.id, {\"network\": {\"name\": 'spring'}})\n\n assert_true(os_conn.get_network('spring')['id'] == default_net.id)","def elAddNetworkConfigurationWithDhcp(self, device):\n commandSection = self.sectionByName(\"command\")\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\n deviceMatch = re.match(r\"([^0-9]+)([0-9])\", device)\n if deviceMatch:\n # e.g. \"eth0\"\n devicePrefix = deviceMatch.group(1)\n deviceNumber = deviceMatch.group(2)\n deviceNumber = int(deviceNumber)\n for i in range(8, deviceNumber - 1, -1):\n deviceI = devicePrefix + str(i)\n deviceIPlus1 = devicePrefix + str(i + 1)\n # move up by one device each network configuration\n commandSection.string = re.sub(r\"(?m)^([ \\t]*network[ \\t]+.*--device[ \\t]*(?:=|[ \\t])[ \\t]*)\" + re.escape(deviceI) + r\"(.*)$\",\n r\"\\g<1>\" + deviceIPlus1 + r\"\\g<2>\",\n commandSection.string)\n # not --noipv6\n networkConfiguration = \"network --device=\" + device + \" --bootproto=dhcp --onboot=yes --activate\"\n if deviceMatch and deviceNumber == 0:\n # having configuration of eth0 first appears to be more conducive to overall success,\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\n # \"... in installer environment. Device of the first network command is activated if network is required,\n # e.g. in case of network installation ...\",\n commandSection.string = networkConfiguration + \"\\n\" \\\n + \"#\\n\" \\\n + commandSection.string\n else:\n commandSection.string = commandSection.string \\\n + \"#\\n\" \\\n + networkConfiguration + \"\\n\"","def _cycle_wifi(mode=None):\n call(['ifdown', settings.WIFI_INTERFACE])\n if mode is not None:\n call(['iwconfig', settings.WIFI_INTERFACE, 'mode', mode])\n call(['ifup', settings.WIFI_INTERFACE])","def remove_network(self, name_of_vm):\n try:\n # vmachine = self.get_vm_by_name(name_of_vm)\n vmachine = self.get_dc_object([vim.VirtualMachine], name_of_vm)\n network = None\n devices = vmachine.config.hardware.device\n networks = []\n for device in devices:\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\n networks.append(device)\n status = 'error'\n if not networks:\n log.info(\"INFO: No network adapters connected to the VM to remove\")\n status = 'success'\n else:\n for network in networks:\n name = network.deviceInfo.label\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\n nic_spec.device = network\n remove_nic = vim.vm.ConfigSpec()\n remove_nic.deviceChange = [nic_spec]\n task = WaitForTask(vmachine.ReconfigVM_Task(spec=remove_nic))\n\n if task == 'success':\n log.info(\"removed '{}' network adapter : {}\".format(name, name_of_vm))\n else:\n log.info(\"Could not '{}' Remove Network adapter: {}\".format(name, name_of_vm))\n status = 'success'\n return status\n except Exception as error:\n log.info(\"Error in 'remove_nic' keyword... {} \\n {}\".format(error, error.message))","def update_cmts_isc_dhcp_config(self, board_config):\n self.setup_dhcp_config(board_config)\n self.setup_dhcp6_config(board_config)\n raise NotImplementedError","def _get_net_dhcp_relay(self, context, net_id):\n pass","def is_configure_with_dhcp(self):\n\t\treturn bool(call_sdk_function('PrlVmDevNet_IsConfigureWithDhcp', self.handle))","def unconfigure_global_dual_active_recovery_reload_disable(device):\n # build a list of commands to send\n # Add stackwise-virtual as first element in the list\n # Enables dual-active recovery-reload\n command_list = ['stackwise-virtual']\n command_list.append(f'no dual-active recovery-reload-disable')\n try:\n output = device.configure(command_list)\n except SubCommandFailure:\n raise SubCommandFailure('Failed to Disable global stackwise-virtual dual-active recovery-reload')\n return output","def disable(ctx):\n config_db = ConfigDBConnector()\n config_db.connect()\n config_db.mod_entry(\"NAT_GLOBAL\", \"Values\", {\"admin_mode\": \"disabled\"})","def bring_down_nic(self, nics, hostname): \n if nics is None:\n return False\n\n pool = WorkerPool()\n\n try: \n #get the ip address of the interface\n for nic in nics:\n cmd = Command(name='get the ip of the interface', cmdStr=\"/sbin/ifconfig %s | grep \\'inet addr:\\' | cut -d: -f2 | awk \\'{ print $1}\\'\" % nic, ctxt=REMOTE, remoteHost=hostname)\n cmd.run(validateAfter=True)\n results = cmd.get_results()\n if results.rc != 0:\n raise Exception('Unable to map interface to ipaddress') \n\n self.nic_to_address_map[(nic, hostname)] = results.stdout.split()[0].strip()\n\n for nic in nics:\n tinctest.logger.info(\"Bringing down %s:%s ...\" % (hostname, nic)) \n cmd = Command(name='bring NIC down', cmdStr='sudo /sbin/ifdown %s' % nic, ctxt=REMOTE, remoteHost=hostname)\n pool.addCommand(cmd)\n\n pool.join()\n for cmd in pool.getCompletedItems():\n results = cmd.get_results()\n if results.rc != 0:\n return False\n finally:\n pool.haltWork()\n pool.joinWorkers()\n pool.join()\n\n return True","async def disable_protection(self) -> None:\n await self._request(\n \"dns_config\", method=\"POST\", json_data={\"protection_enabled\": False},\n )","async def async_turn_on(self):\n path = \"/ip/firewall/nat\"\n param = \".id\"\n value = None\n for uid in self._ctrl.data[\"nat\"]:\n if (\n self._ctrl.data[\"nat\"][uid][\"name\"]\n == f\"{self._data['protocol']}:{self._data['dst-port']}\"\n ):\n value = self._ctrl.data[\"nat\"][uid][\".id\"]\n\n mod_param = \"disabled\"\n mod_value = False\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n await self._ctrl.force_update()","def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\n modem.configuration[AUTO_LED].set_value(not auto_led)\n modem.configuration[DEADMAN].set_value(not deadman)","def leaveNetwork():\r\n cmd = \"zerotier-cli leave {}\".format(networkID)\r\n os.system(cmd)","def dhcp_renew(ifname):\n\n logging.debug('Renewing %s DHCP lease...', ifname)\n\n try:\n subprocess.call(['dhcpcd', '--rebind', ifname])\n except OSError, err:\n if err.errno != errno.ENOENT:\n raise err\n\n try:\n call_timeout(['dhclient', '-1', ifname], timeout=5)\n except OSError, err:\n if err.errno == errno.ENOENT:\n logging.critical('Neither dhcpcd nor dhclient were found.')\n else:\n raise err","def switch_network(self,type = None):\n network_type = self.appconfig(type,\"Settings\")\n self.logger.debug(\"Switch network to %s:%s.\" % (type,network_type))\n if self.enter_settings(u\"More…\"):\n if self.device(text=\"Mobile networks\").exists:\n self.device(text=\"Mobile networks\").click()\n if self.device(text=\"Preferred network mode\").wait.exists(timeout=self.timeout):\n self.device(text=\"Preferred network mode\").click()\n if self.device(resourceId=\"android:id/buttonPanel\").wait.exists(timeout=self.timeout):\n self.device(text=network_type).click()\n print self._is_connected(type)\n self.back_to_home()","def ethernet_off(self):\n if not self.healthy:\n self.health_check()\n if not self._ethernet_switch:\n raise errors.CapabilityNotReadyError(\n device_name=self._device_name,\n msg=\"Not set up for ethernet switching.\")\n self._ethernet_switch.switch_power.power_off(self.ethernet_port_number)","def down(self):\n self.update(admin_state='0')","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def update_network(self, dbnetwork, qipinfo):\n\n # We don't want to add the plenary to self.plenaries if we aren't going\n # to change anything\n plenary = Plenary.get_plenary(dbnetwork)\n updated = False\n\n if dbnetwork.name != qipinfo.name:\n self.logger.client_info(\"Setting network {0!s} name to {1}\"\n .format(dbnetwork, qipinfo.name))\n dbnetwork.name = qipinfo.name\n if dbnetwork.network_type != qipinfo.network_type:\n self.logger.client_info(\"Setting network {0!s} type to {1}\"\n .format(dbnetwork, qipinfo.network_type))\n dbnetwork.network_type = qipinfo.network_type\n if dbnetwork.location != qipinfo.location:\n self.logger.client_info(\"Setting network {0!s} location to {1:l}\"\n .format(dbnetwork, qipinfo.location))\n dbnetwork.location = qipinfo.location\n if dbnetwork.side != qipinfo.side:\n self.logger.client_info(\"Setting network {0!s} side to {1}\"\n .format(dbnetwork, qipinfo.side))\n dbnetwork.side = qipinfo.side\n if dbnetwork.network_compartment != qipinfo.compartment:\n self.logger.client_info(\"Setting network {0!s} compartment to {1!s}\"\n .format(dbnetwork, qipinfo.compartment))\n dbnetwork.network_compartment = qipinfo.compartment\n\n if dbnetwork in self.session.dirty:\n updated = True\n\n old_rtrs = set(dbnetwork.router_ips)\n new_rtrs = set(qipinfo.routers)\n\n del_routers = []\n for router in dbnetwork.routers:\n if router.ip in old_rtrs - new_rtrs:\n del_routers.append(router)\n\n for router in del_routers:\n self.logger.client_info(\"Removing router {0:s} from \"\n \"{1:l}\".format(router.ip, dbnetwork))\n for dns_rec in router.dns_records:\n if dns_rec.is_unused:\n delete_dns_record(dns_rec)\n dbnetwork.routers.remove(router)\n updated = True\n\n for ip in new_rtrs - old_rtrs:\n self.add_router(dbnetwork, ip)\n updated = True\n\n if updated:\n self.plenaries.append(plenary)\n\n # TODO: add support for updating router locations\n\n return dbnetwork.netmask == qipinfo.address.netmask","def network_settings():\n for host in online_hosts:\n with settings(warn_only=True):\n\n result1 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSDomainName <Domain Name>\")\n if result1.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSDomainName failed \")\n\n result2 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSServer1 \"+colo_dns[DEFAULT_COLO ][0])\n if result2.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSServer1 failed \")\n\n result3 = sudo(\"racadm5 -r \"+host+\" -u root -p \"+PASSWORD+\" config -g cfgLanNetworking -o cfgDNSServer2 \"+colo_dns[DEFAULT_COLO ][1])\n if result3.failed:\n logging.error(\"Host: [ \"+host+\" ] : \" + \"Configuration for DNSServer2 failed \")","def turn_off(self):\n self._state = False\n if(self._device['type'] == '_DT-PLUG' or self._device['type'] == '_THIMR'):\n self._send_cmd(self._device, 'cmd=ctrl&devices={[' + self._device[\"sid\"] + ']}&op={\"cmd\":5,\"op\":0 }', 5)\n if(self._device['type'] == '_REALY2' or self._device['type'] == '_REALY4'): \n self._send_cmd(self._device, 'cmd=ctrl&devices={[' + self._device[\"sid\"] + ']}&op={\"cmd\":5,\"'+ self._data_key +'\":0 }', 5)","def set_into_managed_mode(wifi_name):\n \n subprocess.run([\"ip\", \"link\", \"set\", wifi_name, \"down\"])\n subprocess.run([\"iwconfig\", wifi_name, \"mode\", \"managed\"])\n subprocess.run([\"ip\", \"link\", \"set\", wifi_name, \"up\"])\n subprocess.run([\"service\", \"NetworkManager\", \"start\"])","def maintainance(self, on_off, instance_type):\n print((\"enabling\" if on_off else \"disabling\") + \" Maintainer mode\")\n tries = 60\n while True:\n reply = self.send_request(\n instance_type,\n requests.put,\n \"/_admin/cluster/maintenance\",\n '\"on\"' if on_off else '\"off\"',\n )\n if len(reply) > 0:\n print(\"Reply: \" + str(reply[0].text))\n if reply[0].status_code == 200:\n return\n print(f\"Reply status code is {reply[0].status_code}. Sleeping for 3 s.\")\n time.sleep(3)\n tries -= 1\n else:\n print(\"Reply is empty. Sleeping for 3 s.\")\n time.sleep(3)\n tries -= 1\n if tries <= 0:\n action = \"enable\" if on_off else \"disable\"\n raise Exception(f\"Couldn't {action} maintainance mode!\")","def change_adp(self, network: str):\r\n self.ip = network\r\n self.adp = self.ipv4_adp[network]\r\n self.mac = self.ipv4_mac[network].replace('-', ':')\r\n # print(self.adp, self.ip, self.mac)\r","def disable_duplicate_address_detection(self, network):\n # For non-external networks, duplicate address detection isn't\n # necessary (and it sometimes results in race conditions for services\n # that attempt to bind to addresses before they're ready).\n\n if network.network_type != network.TYPE_EXTERNAL:\n real_ifname = self.generic_to_host(network.interface.ifname)\n try:\n utils.execute([\n 'sysctl', '-w', 'net.ipv6.conf.%s.accept_dad=0'\n % real_ifname\n ], self.root_helper)\n except RuntimeError:\n LOG.debug(\n 'Failed to disable v6 dad on %s' % real_ifname\n )","def is_configure_with_dhcp(self):\n\t\treturn bool(call_sdk_function('PrlSrvCfgNet_IsConfigureWithDhcp', self.handle))","def net_undefine(network, server, virt=\"Xen\"):\n\n cmd = \"virsh -c %s net-undefine %s 2>/dev/null\" % (virt2uri(virt), network)\n ret, out = run_remote(server, cmd)\n\n return ret","def update_network(**kwargs):\n\n ip_addr = kwargs.get('ip_addr')\n is_private = kwargs.get('is_private')\n name = kwargs.get('name')\n dns_names = kwargs.get('dns_names')\n is_scanning = kwargs.get('is_scanning', False)\n network_id = make_shortuuid(name)\n\n network = {\n 'dns_names': dns_names,\n 'ip_addr': ip_addr,\n 'is_private' : is_private,\n 'name': name,\n 'id': network_id,\n 'is_scanning': is_scanning,\n 'updated_count': 0\n\n }\n\n network_exists = r.table(\"networks\").insert([network], conflict=\"update\")\n\n return network_exists.run(conn)","def network_refresh(self, kwargs=None):\n scode, hosts = Rest.get('Host')\n filter = {}\n n = 1\n e = {}\n data = []\n for host in hosts:\n os.environ[\"DOCKER_HOST\"] = host['Ip'] + \":\" + str(host['Port'])\n filter['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n self.client = docker.from_env()\n try:\n networks = self.client.networks.list(**kwargs)\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n continue\n\n if len(networks) == 0:\n Console.info(\"No network exist\" + host['Ip'])\n continue\n\n for networkm in networks:\n network = networkm.__dict__['attrs']\n network['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n data.append(network)\n d = {}\n d['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n d['Id'] = network['Id']\n d['Name'] = network['Name']\n d['Containers'] = network['Containers']\n e[n] = d\n n = n + 1\n r = Rest.delete('Network', filter)\n r = Rest.post('Network', data)\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))","async def async_turn_off(self, **kwargs):\n try:\n if await self._api.set_relay_state(self._dev_id, \"off\"):\n self._is_on = False\n self.async_write_ha_state()\n except Smile.PlugwiseError:\n _LOGGER.error(\"Error while communicating to device\")","def clean_rebind_test(**kwargs):\n if 'verify_traffic' not in kwargs:\n kwargs['verify_traffic'] = False\n prepare_subscriber_traffic(**kwargs)\n device_id = kwargs.get('device_id', bbe.get_devices(device_tags='dut', id_only=True)[0])\n switch_id = kwargs.get('switch_id', 'r1')\n switch_handle = t.get_handle(switch_id)\n switch_access_intf = bbe.get_interfaces(switch_id, interfaces='access')\n status = True\n for iteration in range(1, int(kwargs.get('iteration', 1)) + 1):\n t.log(\"disable access ports in switch in iteration #{}\".format(iteration))\n port_command_list = []\n status = True\n for access in switch_access_intf:\n port_command_list.append(\"set interfaces {} disable\".format(access.interface_pic))\n switch_handle.config(command_list=port_command_list)\n switch_handle.commit()\n t.log(\"verify access ports in down state\")\n for access in switch_access_intf:\n resp = switch_handle.pyez('get_interface_information', level_extra='terse',\n interface_name=access.interface_pic).resp\n if resp.findtext('physical-interface/admin-status') == 'down' and resp.findtext(\n 'physical-interface/oper-status') == 'down':\n t.log(\"interface {} is in down state\".format(access.interface_pic))\n else:\n t.log('WARN', \"interface {} is in state {}\".format(access.interface_pic, resp))\n status = False\n\n if not status:\n for access in switch_access_intf:\n port_command_list.append(\"delete interfaces {} disable\".format(access.interface_pic))\n switch_handle.config(command_list=port_command_list)\n switch_handle.commit()\n raise Exception(\"some interfaces failed to be in down state after disable\")\n base_time = time.time()\n while time.time() - base_time < 1800:\n router_count = get_router_sub_summary(device_id)['client']\n tester_count = get_rt_subs_info()['rt_sessions_up']\n if router_count == 0 and tester_count == 0:\n duration = time.time() - base_time\n t.log(\"all subscribers cleared from tester and router after {}s in iteration #{}\".format(duration,\n iteration))\n break\n t.log(\"sleep 30s , waiting for clients cleared\")\n time.sleep(30)\n\n result = get_router_sub_summary(device_id)\n\n if result['client'] != 0 or 'terminated' in result or 'terminating' in result or 'init' in result:\n status = False\n t.log('WARN', 'some subscribers stuck in unexpected state in iteration #{}'.format(iteration))\n\n for access in switch_access_intf:\n port_command_list.append(\"delete interfaces {} disable\".format(access.interface_pic))\n switch_handle.config(command_list=port_command_list)\n switch_handle.commit()\n time.sleep(10)\n t.log(\"verify access ports in up state in iteration {}\".format(iteration))\n for access in switch_access_intf:\n resp = switch_handle.pyez('get_interface_information', level_extra='terse',\n interface_name=access.interface_pic).resp\n if resp.findtext('physical-interface/admin-status') == 'up' and resp.findtext(\n 'physical-interface/oper-status') == 'up':\n t.log(\"interface {} is in up state\".format(access.interface_pic))\n else:\n t.log('WARN', \"interface {} is in state {}\".format(access.interface_pic, resp))\n status = False\n\n if not status:\n raise Exception(\"clean test failed\")\n ##set the rt subscriber state to stopped, since it is not teared down by actions\n t.log(\"login subscriber and verify traffic after restore the connection in iteration #{}\".format(iteration))\n prepare_subscriber_traffic(**kwargs)","def down_nodes(nodelist, reason):\n if isinstance(nodelist, list):\n nodelist = util.to_hostlist(nodelist)\n run(f\"{lkp.scontrol} update nodename={nodelist} state=down reason='{reason}'\")","def _update_dnsmasq(self, network_id):\n\n # Check whether we should really do the following processing.\n if self.suppress_dnsmasq_updates:\n LOG.debug(\"Don't update dnsmasq yet;\"\n \" must be processing a snapshot\")\n self.dirty_networks.add(network_id)\n return\n\n self.dnsmasq_updater.update_network(network_id)","def config_networking(\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\n ):\n\n global_ip = vim.vm.customization.GlobalIPSettings()\n adapter_map = vim.vm.customization.AdapterMapping()\n adapter_map.adapter = vim.vm.customization.IPSettings()\n adapter_map.macAddress = network_obj.macAddress\n if ip:\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\n adapter_map.adapter.ip.ipAddress = ip\n else:\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\n adapter_map.adapter.subnetMask = netmask\n adapter_map.adapter.gateway = gateway\n global_ip.dnsServerList = dns\n adapter_map.adapter.dnsDomain = domain\n ident = vim.vm.customization.LinuxPrep()\n ident.hostName = vim.vm.customization.FixedName()\n if guest_hostname:\n ident.hostName.name = guest_hostname\n else:\n ident.hostName.name = self.vm_obj.name\n custom_spec = vim.vm.customization.Specification()\n custom_spec.nicSettingMap = [adapter_map]\n custom_spec.identity = ident\n custom_spec.globalIPSettings = global_ip\n return self.vm_obj.Customize(spec=custom_spec)","def call_driver(self, action, network):\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n try:\n # the Driver expects something that is duck typed similar to\n # the base models.\n driver = self.dhcp_driver_cls(self.conf,\n network,\n self.conf.root_helper,\n self.device_manager,\n namespace)\n getattr(driver, action)()\n return True\n\n except Exception, e:\n self.needs_resync = True\n LOG.exception('Unable to %s dhcp.' % action)","def remove_network_adapter(self, network_obj):\n\n nic_spec = vim.vm.device.VirtualDeviceSpec()\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\n nic_spec.device = network_obj\n config_spec = vim.vm.ConfigSpec()\n config_spec.deviceChange = [nic_spec]\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)","def disabled(config):\n disable(config)\n reload_service('apache2')","def dhcp_used(self, dhcp_used):\n\n self._dhcp_used = dhcp_used","def IsLinkdown(nic,timeout):\n nic = nic.strip()\n current = time.time()\n timeout += current\n while current < timeout:\n data = os.popen(\"ipconfig\").read().split(\"Ethernet adapter\")\n for item in data:\n if item.count(nic) and item.count(\"isconnected\"): #Disconnected\n return 1\n time.sleep(0.5)\n current = time.time()\n return 0","def destroy(self, network, device_name):\n if self.conf.use_namespaces:\n namespace = NS_PREFIX + network.id\n else:\n namespace = None\n\n self.driver.unplug(device_name, namespace=namespace)\n\n self.plugin.release_dhcp_port(network.id,\n self.get_device_id(network))","def deconfigure(self):\n yield self._r2rm_client.until_synced(2)\n response = yield self._r2rm_client.req.force_deconfigure_board(self._icom_id)\n if not response.reply.reply_ok():\n self.log.error(\"Error on deconfigure request: {}\".format(\n response.reply.arguments[1]))\n raise EddRoach2ProductError(response.reply.arguments[1])\n self.teardown_sensors()\n self._firmware = None\n self._icom_id = None","def randomize_interface(self, interface):\n mac = self.get_rand_mac()\n command = \"ifconfig %s hw ether %s\" % (interface, mac)\n\n subprocess.call(\"ifconfig %s down\" % interface, shell=True)\n rc = subprocess.call(command, shell=True)\n subprocess.call(\"ifconfig %s up\" % interface, shell=True)\n\n return rc","def update_network(self, context, net_id, network):\n LOG.debug(_(\"NeutronRestProxyV2.update_network() called\"))\n\n self._warn_on_state_status(network['network'])\n\n session = context.session\n with session.begin(subtransactions=True):\n new_net = super(NeutronRestProxyV2, self).update_network(\n context, net_id, network)\n self._process_l3_update(context, new_net, network['network'])\n\n # update network on network controller\n self._send_update_network(new_net, context)\n return new_net","def _configure_ipsec_networking(self):\n with hide(*fab_output_hides):\n changed = upload_template_changed(\n '../config/tpl/sysctl.conf',\n '/etc/sysctl.conf',\n use_sudo=True,\n mode=0600,\n )\n if changed:\n sudo('sysctl -p /etc/sysctl.conf')\n\n return changed","def networkMode(self, networkMode):\n\n # Setting the network mode can take a bit of time, so give it 10 seconds\n # to finish\n response = self.at.sendCommand(f\"AT+CFUN={networkMode}\", timeout = 10)\n\n if not response:\n raise modem.AtError(response, \"Failed to set network mode\")","def setup_dhcp_config(self, board_config):\n raise NotImplementedError","def test_wifi_scanner_with_wifi_off(self):\n self.log.debug(\"Make sure wifi is off.\")\n wutils.wifi_toggle_state(self.dut, False)\n self.start_wifi_scanner_single_scan_expect_failure(\n self.default_scan_setting)\n self.log.debug(\"Turning wifi back on.\")\n wutils.wifi_toggle_state(self.dut, True)","def configure_global_dual_active_recovery_reload_disable(device):\n # build a list of commands to send\n # Add stackwise-virtual as first element in the list\n # Disables dual-active recovery-reload\n command_list = ['stackwise-virtual']\n command_list.append(f'dual-active recovery-reload-disable')\n try:\n output = device.configure(command_list)\n except SubCommandFailure:\n raise SubCommandFailure('Failed to Enable global stackwise-virtual dual-active recovery-reload')\n return output","def update_network_section(self):\n rconfig = configparser.RawConfigParser()\n rconfig.read(self.conf_file)\n if self.ext_net:\n if not rconfig.has_section('network'):\n rconfig.add_section('network')\n rconfig.set('network', 'public_network_id', self.ext_net.id)\n rconfig.set('network', 'floating_network_name', self.ext_net.name)\n rconfig.set('network-feature-enabled', 'floating_ips', True)\n else:\n if not rconfig.has_section('network-feature-enabled'):\n rconfig.add_section('network-feature-enabled')\n rconfig.set('network-feature-enabled', 'floating_ips', False)\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\n rconfig.write(config_file)","def validate_nic_down(self): \n\n pool = WorkerPool()\n\n try: \n for nic, hostname in self.nic_to_address_map:\n address = self.nic_to_address_map[(nic, hostname)]\n cmd = Ping('ping validation', address, ctxt=REMOTE, remoteHost='localhost')\n pool.addCommand(cmd)\n pool.join()\n\n for cmd in pool.getCompletedItems():\n results = cmd.get_results()\n if results.rc == 0:\n return False\n finally:\n pool.haltWork()\n pool.joinWorkers()\n pool.join()\n\n tinctest.logger.info(\"Successfully brought down nics ...\") \n return True","def disable_relays(self):\n #ensure clock low and data high\n self.e.clear_bit(7)\n self.e.set_bit(5)\n time.sleep(0.01)\n\n #pulse the clock line\n self.e.set_bit(7)\n time.sleep(0.01)\n self.e.clear_bit(7)\n\n #clear the data line\n self.e.clear_bit(5)","def set_dhcpserver_enabled(self, bEnabled):\n\t\tcall_sdk_function('PrlVirtNet_SetDHCPServerEnabled', self.handle, bEnabled)","def filter_update_network_attributes(network, context):\n try_del(network, ['id', 'status', 'subnets', 'tenant_id'])","def turn_off(self, **kwargs):\n request = requests.post(self._resource, data=\"0\", timeout=10)\n if (request.status_code == 200) or (request.status_code == 201):\n self._state = False\n else:\n _LOGGER.error(\"Can't turn off %s. Is resource/endpoint offline?\",\n self._resource)\n\n self.schedule_update_ha_state()","def _validate_change_when_exposing_in_dhcp(self):\n if self.pk and settings.DHCP_ENTRY_FORBID_CHANGE:\n from ralph.networks.models import IPAddress\n old_obj = self.__class__._default_manager.get(pk=self.pk)\n try:\n if old_obj.ipaddress.dhcp_expose:\n if old_obj.mac != self.mac:\n raise ValidationError(\n 'Cannot change MAC when exposing in DHCP'\n )\n except IPAddress.DoesNotExist:\n pass","def _force_off(self):\n self._interface.set('fw_wp_vref', self._fw_wp_vref)\n self._interface.set('fw_wp_en', 'on')\n self._interface.set('fw_wp', 'off')","def cycle(self, include_ethernet=False):\n if not self.healthy:\n self.health_check()\n self.off()\n time.sleep(2) # Small delay before calling power_on\n self.on()","def turn_off(self, **kwargs):\n _LOGGER.error(\"DALI TURN OFF\")\n self._state = False\n\n url = self.urlx + '/toggle'\n headers = {'x-ha-access': 'raspberry',\n 'content-type': 'application/json'}\n\n response = get(url, headers=headers)\n _LOGGER.error(response.text)\n\n json_data = json.loads(response.text)\n _LOGGER.error(json_data)\n\n state = json_data['state']\n\n self._dimmer = 0\n\n self._state = state == 'on'","def reset_old_config(self):\n stringa = \"tc qdisc del dev \" + self.__interface + \" root\"\n cmd = shlex.split(stringa)\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n try:\n o, e = proc.communicate(timeout=1)\n except subprocess.TimeoutExpired:\n proc.kill()\n raise RuntimeWarning(\"Old configuration not eliminated\")\n\n if e.decode('ascii') != \"\":\n raise RuntimeError(e.decode('ascii'))\n return proc.returncode","def _network_conf(conf_tuples=None, **kwargs):\n nic = kwargs.get(\"network_profile\", None)\n ret = []\n nic_opts = kwargs.get(\"nic_opts\", {})\n if nic_opts is None:\n # coming from elsewhere\n nic_opts = {}\n if not conf_tuples:\n conf_tuples = []\n old = _get_veths(conf_tuples)\n if not old:\n old = {}\n\n # if we have a profile name, get the profile and load the network settings\n # this will obviously by default look for a profile called \"eth0\"\n # or by what is defined in nic_opts\n # and complete each nic settings by sane defaults\n if nic and isinstance(nic, ((str,), dict)):\n nicp = get_network_profile(nic)\n else:\n nicp = {}\n if DEFAULT_NIC not in nicp:\n nicp[DEFAULT_NIC] = {}\n\n kwargs = copy.deepcopy(kwargs)\n gateway = kwargs.pop(\"gateway\", None)\n bridge = kwargs.get(\"bridge\", None)\n if nic_opts:\n for dev, args in nic_opts.items():\n ethx = nicp.setdefault(dev, {})\n try:\n ethx = salt.utils.dictupdate.update(ethx, args)\n except AttributeError:\n raise SaltInvocationError(\"Invalid nic_opts configuration\")\n ifs = [a for a in nicp]\n ifs += [a for a in old if a not in nicp]\n ifs.sort()\n gateway_set = False\n for dev in ifs:\n args = nicp.get(dev, {})\n opts = nic_opts.get(dev, {}) if nic_opts else {}\n old_if = old.get(dev, {})\n disable = opts.get(\"disable\", args.get(\"disable\", False))\n if disable:\n continue\n mac = opts.get(\n \"mac\", opts.get(\"hwaddr\", args.get(\"mac\", args.get(\"hwaddr\", \"\")))\n )\n type_ = opts.get(\"type\", args.get(\"type\", \"\"))\n flags = opts.get(\"flags\", args.get(\"flags\", \"\"))\n link = opts.get(\"link\", args.get(\"link\", \"\"))\n ipv4 = opts.get(\"ipv4\", args.get(\"ipv4\", \"\"))\n ipv6 = opts.get(\"ipv6\", args.get(\"ipv6\", \"\"))\n infos = salt.utils.odict.OrderedDict(\n [\n (\n \"lxc.network.type\",\n {\n \"test\": not type_,\n \"value\": type_,\n \"old\": old_if.get(\"lxc.network.type\"),\n \"default\": \"veth\",\n },\n ),\n (\n \"lxc.network.name\",\n {\"test\": False, \"value\": dev, \"old\": dev, \"default\": dev},\n ),\n (\n \"lxc.network.flags\",\n {\n \"test\": not flags,\n \"value\": flags,\n \"old\": old_if.get(\"lxc.network.flags\"),\n \"default\": \"up\",\n },\n ),\n (\n \"lxc.network.link\",\n {\n \"test\": not link,\n \"value\": link,\n \"old\": old_if.get(\"lxc.network.link\"),\n \"default\": search_lxc_bridge(),\n },\n ),\n (\n \"lxc.network.hwaddr\",\n {\n \"test\": not mac,\n \"value\": mac,\n \"old\": old_if.get(\"lxc.network.hwaddr\"),\n \"default\": salt.utils.network.gen_mac(),\n },\n ),\n (\n \"lxc.network.ipv4\",\n {\n \"test\": not ipv4,\n \"value\": ipv4,\n \"old\": old_if.get(\"lxc.network.ipv4\", \"\"),\n \"default\": None,\n },\n ),\n (\n \"lxc.network.ipv6\",\n {\n \"test\": not ipv6,\n \"value\": ipv6,\n \"old\": old_if.get(\"lxc.network.ipv6\", \"\"),\n \"default\": None,\n },\n ),\n ]\n )\n # for each parameter, if not explicitly set, the\n # config value present in the LXC configuration should\n # take precedence over the profile configuration\n for info in list(infos.keys()):\n bundle = infos[info]\n if bundle[\"test\"]:\n if bundle[\"old\"]:\n bundle[\"value\"] = bundle[\"old\"]\n elif bundle[\"default\"]:\n bundle[\"value\"] = bundle[\"default\"]\n for info, data in infos.items():\n if data[\"value\"]:\n ret.append({info: data[\"value\"]})\n for key, val in args.items():\n if key == \"link\" and bridge:\n val = bridge\n val = opts.get(key, val)\n if key in [\n \"type\",\n \"flags\",\n \"name\",\n \"gateway\",\n \"mac\",\n \"link\",\n \"ipv4\",\n \"ipv6\",\n ]:\n continue\n ret.append({f\"lxc.network.{key}\": val})\n # gateway (in automode) must be appended following network conf !\n if not gateway:\n gateway = args.get(\"gateway\", None)\n if gateway is not None and not gateway_set:\n ret.append({\"lxc.network.ipv4.gateway\": gateway})\n # only one network gateway ;)\n gateway_set = True\n # normally, this won't happen\n # set the gateway if specified even if we did\n # not managed the network underlying\n if gateway is not None and not gateway_set:\n ret.append({\"lxc.network.ipv4.gateway\": gateway})\n # only one network gateway ;)\n gateway_set = True\n\n new = _get_veths(ret)\n # verify that we did not loose the mac settings\n for iface in [a for a in new]:\n ndata = new[iface]\n nmac = ndata.get(\"lxc.network.hwaddr\", \"\")\n ntype = ndata.get(\"lxc.network.type\", \"\")\n omac, otype = \"\", \"\"\n if iface in old:\n odata = old[iface]\n omac = odata.get(\"lxc.network.hwaddr\", \"\")\n otype = odata.get(\"lxc.network.type\", \"\")\n # default for network type is setted here\n # attention not to change the network type\n # without a good and explicit reason to.\n if otype and not ntype:\n ntype = otype\n if not ntype:\n ntype = \"veth\"\n new[iface][\"lxc.network.type\"] = ntype\n if omac and not nmac:\n new[iface][\"lxc.network.hwaddr\"] = omac\n\n ret = []\n for val in new.values():\n for row in val:\n ret.append(salt.utils.odict.OrderedDict([(row, val[row])]))\n # on old versions of lxc, still support the gateway auto mode\n # if we didn't explicitly say no to\n # (lxc.network.ipv4.gateway: auto)\n if (\n Version(version()) <= Version(\"1.0.7\")\n and True not in [\"lxc.network.ipv4.gateway\" in a for a in ret]\n and True in [\"lxc.network.ipv4\" in a for a in ret]\n ):\n ret.append({\"lxc.network.ipv4.gateway\": \"auto\"})\n return ret","def set_disabled_switch(self, disabled):\n self.disabled = disabled","def set_dhcp_conn(nic):\n nic.EnableDHCP()\n # After static DNS servers are specified to start using Dynamic Host\n # Configuration Protocol (DHCP) instead of static DNS servers,\n # you can call the method without supplying \"in\" parameters.\n nic.SetDNSServerSearchOrder()","async def async_turn_off(self, **kwargs):\n try:\n state_off = await self._api.set_relay_state(\n self._dev_id, self._members, \"off\"\n )\n if state_off:\n self._is_on = False\n self.async_write_ha_state()\n except PlugwiseException:\n _LOGGER.error(\"Error while communicating to device\")","def unconfigure_lldp_reinit(device): \r\n try:\r\n device.configure('no lldp reinit')\r\n except SubCommandFailure as e:\r\n raise SubCommandFailure(\r\n \"Could not unconfigure LLDP reinit\"\r\n \"Error: {error}\".format(error=e)\r\n )","def set_configure_with_dhcp(self, bConfigureWithDhcp):\n\t\tcall_sdk_function('PrlVmDevNet_SetConfigureWithDhcp', self.handle, bConfigureWithDhcp)","async def async_turn_on(self):\n path = \"/interface\"\n param = \"default-name\"\n if \"-\" in self._data[\"port-mac-address\"]:\n param = \"name\"\n value = self._data[param]\n mod_param = \"disabled\"\n mod_value = False\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n\n if self._data[\"poe-out\"] == \"off\":\n path = \"/interface/ethernet\"\n self._ctrl.set_value(path, param, value, \"poe-out\", \"auto-on\")\n\n await self._ctrl.force_update()","def down(config):\n pass","def maintenance_down():\n install_apache_conf()\n reboot()","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def emulate_whole_network_disaster(self, delay_before_recover=5 * 60,\n wait_become_online=True):\n nodes = [node for node in self.env.d_env.get_nodes()\n if node.driver.node_active(node)]\n\n networks_interfaces = nodes[1].interfaces\n\n for interface in networks_interfaces:\n interface.network.block()\n\n time.sleep(delay_before_recover)\n\n for interface in networks_interfaces:\n interface.network.unblock()\n\n if wait_become_online:\n self.fuel_web.wait_nodes_get_online_state(nodes[1:])","def stop(self):\n logging.debug(\"Network.stop entered:\" + str(self.id))\n # print self.cloudnet\n # res = cn.delete(self.cloudnet)\n notify(\"Stopping network %s\" % self.name)\n # if not self.cloudnet:\n # \n # self.cloudnet = cn.find(id=\"52a24319-f58d-4795-a3bd-c22d87bb65ae\")\n if self.cloudnet:\n res = self.cloudnet.delete()\n else:\n res = True\n return res","def test_set_enabled(self):\n networktables_mock = unittest.mock.Mock()\n table_mock = unittest.mock.Mock()\n networktables_mock.getTable.return_value = table_mock\n\n network_instance = network.Network(networktables_mock, None, None)\n\n network_instance.set_enabled(True)\n table_mock.putBoolean.assert_called_with(\"/enabled\", True)\n\n network_instance.set_enabled(False)\n table_mock.putBoolean.assert_called_with(\"/enabled\", False)","def reboot(self, instance, network_info):\n LOG.debug(\"reboot\")\n\n bmm = db.bmm_get_by_instance_id(None, instance[\"id\"])\n PowerManager(bmm[\"ipmi_ip\"]).reboot()","def load_ethernet_aux(self, _type, _path, ethernet_data):\n\n support_ipv6 = True\n dhcpv4curr = dhcpv4conf = oem_dhcpv4curr = oem_dhcpv4conf = dict()\n dhcpv6curr = dhcpv6conf = oem_dhcpv6curr = oem_dhcpv6conf = dict()\n errors = []\n\n ident_eth = False\n if \"EthernetInterface\" in _type:\n for curr_sel in self.rdmc.app.select(\n _type.split(\".\")[0] + \".\",\n (\n self.rdmc.app.typepath.defs.hrefstring,\n self.rdmc.app.typepath.defs.managerpath + \"*\",\n ),\n path_refresh=True,\n ):\n if curr_sel.path == _path:\n ident_eth = True\n break\n # 'links/self/href' required when using iLO 4 (rest).\n elif \"EthernetNetworkInterface\" in _type:\n for curr_sel in self.rdmc.app.select(\n _type.split(\".\")[0] + \".\",\n (\n \"links/self/\" + self.rdmc.app.typepath.defs.hrefstring,\n self.rdmc.app.typepath.defs.managerpath + \"*\",\n ),\n path_refresh=True,\n ):\n if curr_sel.path == _path:\n ident_eth = True\n break\n else:\n raise Exception(\"Invalid type in management NIC load operation: '%s'\" % _type)\n\n if not ident_eth:\n raise InvalidPathError(\n \"Path: '%s' is invalid/not identified on this server.\\n\" % _path\n )\n\n ident_name = curr_sel.dict.get(\"Name\")\n ident_id = curr_sel.dict.get(\"Id\")\n # ENABLING ETHERNET INTERFACE SECTION\n try:\n # Enable the Interface if called for and not already enabled\n if ethernet_data.get(\"InterfaceEnabled\") and not curr_sel.dict.get(\n \"InterfaceEnabled\"\n ):\n self.rdmc.app.patch_handler(\n _path, {\"InterfaceEnabled\": True}, silent=True\n )\n self.rdmc.ui.printer(\"NIC Interface Enabled.\\n\")\n # Disable the Interface if called for and not disabled already\n # No need to do anything else, just return\n elif not ethernet_data.get(\"InterfaceEnabled\") and not curr_sel.dict.get(\n \"InterfaceEnabled\"\n ):\n self.rdmc.app.patch_handler(\n _path, {\"InterfaceEnabled\": False}, silent=True\n )\n self.rdmc.ui.warn(\n \"NIC Interface Disabled. All additional configurations \" \"omitted.\"\n )\n return\n except (KeyError, NameError, TypeError, AttributeError):\n # check OEM for NICEnabled instead\n if (\n not curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"NICEnabled\"]\n and ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"NICEnabled\"]\n ):\n self.rdmc.app.patch_handler(\n _path,\n {\"Oem\": {self.rdmc.app.typepath.defs.oemhp: {\"NICEnabled\": True}}},\n silent=True,\n )\n self.rdmc.ui.printer(\"NIC Interface Enabled.\\n\")\n elif (\n not curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"NICEnabled\"]\n and not ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"NICEnabled\"\n ]\n ):\n self.rdmc.app.patch_handler(\n _path,\n {\"Oem\": {self.rdmc.app.typepath.defs.oemhp: {\"NICEnabled\": False}}},\n silent=True,\n )\n self.rdmc.ui.printer(\"NIC Interface Disabled.\\n\")\n return\n # except IloResponseError should just be raised and captured by decorator. No point in\n # performing any other operations if the interface can not be set.\n\n # END ENABLING ETHERNET INTEFACE SECTION\n # ---------------------------------------\n # DETERMINE DHCPv4 and DHCPv6 States and associated flags\n\n if \"NICSupportsIPv6\" in list(\n curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp].keys()\n ):\n support_ipv6 = curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"NICSupportsIPv6\"\n ]\n\n # obtain DHCPv4 Config and OEM\n try:\n if \"DHCPv4\" in list(curr_sel.dict.keys()) and \"DHCPv4\" in list(\n ethernet_data.keys()\n ):\n dhcpv4curr = copy.deepcopy(curr_sel.dict[\"DHCPv4\"])\n dhcpv4conf = copy.deepcopy(ethernet_data[\"DHCPv4\"])\n except (KeyError, NameError, TypeError, AttributeError):\n errors.append(\"Unable to find Redfish DHCPv4 Settings.\\n\")\n finally:\n try:\n oem_dhcpv4curr = copy.deepcopy(\n curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv4\"]\n )\n oem_dhcpv4conf = copy.deepcopy(\n ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv4\"]\n )\n ipv4curr = copy.deepcopy(\n curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"IPv4\"]\n )\n ipv4conf = copy.deepcopy(\n ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"IPv4\"]\n )\n except (KeyError, NameError):\n errors.append(\"Unable to find OEM Keys for DHCPv4 or IPv4\")\n\n try:\n if support_ipv6:\n if \"DHCPv6\" in list(curr_sel.dict.keys()) and \"DHCPv6\" in list(\n ethernet_data.keys()\n ):\n dhcpv6curr = copy.deepcopy(curr_sel.dict[\"DHCPv6\"])\n dhcpv6conf = copy.deepcopy(ethernet_data[\"DHCPv6\"])\n else:\n self.rdmc.ui.warn(\"NIC Does not support IPv6.\")\n except (KeyError, NameError, TypeError, AttributeError):\n errors.append(\"Unable to find Redfish DHCPv6 Settings.\\n\")\n finally:\n try:\n oem_dhcpv4curr = copy.deepcopy(\n curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv6\"]\n )\n oem_dhcpv6conf = copy.deepcopy(\n ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv6\"]\n )\n ipv6curr = copy.deepcopy(\n curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"IPv6\"]\n )\n ipv6conf = copy.deepcopy(\n ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"IPv6\"]\n )\n except (KeyError, NameError):\n errors.append(\"Unable to find OEM Keys for DHCPv6 or IPv6\")\n\n try:\n # if DHCP Enable request but not currently enabled\n if (\n dhcpv4conf.get(\"DHCPEnabled\")\n and not curr_sel.dict[\"DHCPv4\"][\"DHCPEnabled\"]\n ):\n self.rdmc.app.patch_handler(\n _path, {\"DHCPv4\": {\"DHCPEnabled\": True}}, silent=True\n )\n self.rdmc.ui.printer(\"DHCP Enabled.\\n\")\n # if DHCP Disable request but currently enabled\n elif not dhcpv4conf[\"DHCPEnabled\"] and curr_sel.dict[\"DHCPv4\"][\"DHCPEnabled\"]:\n self.rdmc.app.patch_handler(\n _path, {\"DHCPv4\": {\"DHCPEnabled\": False}}, silent=True\n )\n dhcpv4conf[\"UseDNSServers\"] = False\n dhcpv4conf[\"UseNTPServers\"] = False\n dhcpv4conf[\"UseGateway\"] = False\n dhcpv4conf[\"UseDomainName\"] = False\n self.rdmc.ui.printer(\"DHCP Disabled.\\n\")\n except (KeyError, NameError, TypeError, AttributeError):\n # try with OEM\n try:\n if (\n oem_dhcpv4conf.get(\"Enabled\")\n and not curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"DHCPv4\"\n ][\"Enabled\"]\n ):\n self.rdmc.app.patch_handler(\n _path,\n {\n \"Oem\": {\n self.rdmc.app.typepath.defs.oemhp: {\n \"DHCPv4\": {\"DHCPEnabled\": True}\n }\n }\n },\n silent=True,\n )\n self.rdmc.ui.printer(\"DHCP Enabled.\\n\")\n if \"IPv4Addresses\" in ethernet_data:\n del ethernet_data[\"IPv4Addresses\"]\n elif (\n not oem_dhcpv4conf.get(\"Enabled\")\n and curr_sel.dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv4\"][\n \"Enabled\"\n ]\n ):\n oem_dhcpv4conf[\"UseDNSServers\"] = False\n oem_dhcpv4conf[\"UseNTPServers\"] = False\n oem_dhcpv4conf[\"UseGateway\"] = False\n oem_dhcpv4conf[\"UseDomainName\"] = False\n self.rdmc.ui.printer(\"DHCP Disabled.\\n\")\n except (KeyError, NameError) as exp:\n errors.append(\n \"Failure in parsing or removing data in OEM DHCPv4: %s.\\n\" % exp\n )\n\n try:\n # if the ClientIDType is custom and we are missing the ClientID then this property can\n # not be set.\n if \"ClientIdType\" in list(dhcpv4conf.keys()):\n if dhcpv4conf[\"ClientIdType\"] == \"Custom\" and \"ClientID\" not in list(\n dhcpv4conf.keys()\n ):\n del ethernet_data[\"DHCPv4\"][\"ClientIdType\"]\n elif \"ClientIdType\" in list(oem_dhcpv4conf.keys()):\n if oem_dhcpv4conf[\"ClientIdType\"] == \"Custom\" and \"ClientID\" not in list(\n oem_dhcpv4conf.keys()\n ):\n del ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\"DHCPv4\"][\n \"ClientIdType\"\n ]\n except (KeyError, NameError, TypeError, AttributeError):\n try:\n if \"ClientIdType\" in list(oem_dhcpv4conf.keys()):\n if oem_dhcpv4conf[\n \"ClientIdType\"\n ] == \"Custom\" and \"ClientID\" not in list(oem_dhcpv4conf.keys()):\n del ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"DHCPv4\"\n ][\"ClientIdType\"]\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n # special considerations go here for things that need to stay despite diffdict\n # EX: IPv4 addresses (aka bug). Changing only one property within the\n # IPv4StaticAddresses or IPv4Addresses causes an issue during load. Must include IP,\n # subnet mask and gateway (they can not be patched individually).\n # spec_dict = {'Oem': {self.rdmc.app.typepath.defs.oemhp: {}}}\n spec_dict = dict()\n if \"IPv4Addresses\" in ethernet_data:\n spec_dict[\"IPv4Addresses\"] = copy.deepcopy(ethernet_data[\"IPv4Addresses\"])\n try:\n if \"IPv4Addresses\" in ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp]:\n spec_dict[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"IPv4Addresses\"\n ] = copy.deepcopy(\n ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"IPv4StaticAddresses\"\n ]\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n\n # diff and overwrite the original payload\n ethernet_data = diffdict(ethernet_data, curr_sel.dict)\n ethernet_data.update(spec_dict)\n\n # verify dependencies on those flags which are to be applied are eliminated\n try:\n # delete Domain name and FQDN if UseDomainName for DHCPv4 or DHCPv6\n # is present. can wait to apply at the end\n if dhcpv4conf.get(\"UseDomainName\"): # or dhcpv6conf['UseDomainName']:\n if (\n \"DomainName\"\n in ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp]\n ):\n del ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"DomainName\"\n ]\n if \"FQDN\" in ethernet_data:\n del ethernet_data[\"FQDN\"]\n except (KeyError, NameError, TypeError, AttributeError):\n # try again with OEM\n try:\n if oem_dhcpv4conf.get(\"UseDomainName\") or oem_dhcpv6conf.get(\n \"UseDomainName\"\n ):\n if (\n \"DomainName\"\n in ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp]\n ):\n del ethernet_data[\"Oem\"][self.rdmc.app.typepath.defs.oemhp][\n \"DomainName\"\n ]\n if \"FQDN\" in ethernet_data:\n del ethernet_data[\"FQDN\"]\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n try:\n # delete DHCP4 DNSServers from IPV4 dict if UseDNSServers Enabled\n # can wait to apply at the end\n if dhcpv4conf.get(\"UseDNSServers\"): # and ethernet_data.get('NameServers'):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"NameServers\"]\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n finally:\n try:\n if oem_dhcpv4conf.get(\"UseDNSServers\"):\n # del_sections('DNSServers', ethernet_data)\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"DNSServers\"]\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n try:\n if dhcpv4conf.get(\"UseWINSServers\"):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"WINServers\"]\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n finally:\n try:\n if oem_dhcpv4conf.get(\"UseWINSServers\"):\n json_traversal_delete_empty(\n data=ethernet_data,\n remove_list=[\"WINServers\", \"WINSRegistration\"],\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n try:\n if dhcpv4conf.get(\"UseStaticRoutes\"):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"StaticRoutes\"]\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n finally:\n try:\n if oem_dhcpv4conf.get(\"UseStaticRoutes\"):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"StaticRoutes\"]\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n try:\n # if using DHCPv4, remove static addresses\n if dhcpv4conf.get(\"DHCPEnabled\"):\n json_traversal_delete_empty(\n data=ethernet_data,\n remove_list=[\"IPv4Addresses\", \"IPv4StaticAddresses\"],\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n finally:\n try:\n if oem_dhcpv4conf.get(\"Enabled\"):\n json_traversal_delete_empty(\n data=ethernet_data,\n remove_list=[\"IPv4Addresses\", \"IPv4StaticAddresses\"],\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n try:\n # if not using DHCPv6, remove static addresses from payload\n if dhcpv6conf.get(\"OperatingMode\") == \"Disabled\":\n json_traversal_delete_empty(\n data=ethernet_data,\n remove_list=[\"IPv6Addresses\", \"IPv6StaticAddresses\"],\n )\n except (KeyError, NameError, TypeError, AttributeError):\n pass\n finally:\n try:\n if not oem_dhcpv6conf.get(\"StatefulModeEnabled\"):\n json_traversal_delete_empty(\n data=ethernet_data,\n remove_list=[\"IPv6Addresses\", \"IPv6StaticAddresses\"],\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n flags = dict()\n if dhcpv4conf:\n flags[\"DHCPv4\"] = dhcpv4conf\n if dhcpv6conf:\n flags[\"DHCPv6\"] = dhcpv6conf\n if oem_dhcpv4conf:\n flags[\"Oem\"] = {self.rdmc.app.typepath.defs.oemhp: {\"DHCPv4\": oem_dhcpv4conf}}\n if oem_dhcpv6conf:\n flags[\"Oem\"] = {self.rdmc.app.typepath.defs.oemhp: {\"DHCPv6\": oem_dhcpv6conf}}\n\n # verify dependencies on those flags which are to be applied are eliminated\n\n try:\n self.rdmc.app.patch_handler(_path, flags, silent=True)\n except IloResponseError as excp:\n errors.append(\n \"iLO Responded with the following errors setting DHCP: %s.\\n\" % excp\n )\n\n try:\n if \"AutoNeg\" not in list(ethernet_data.keys()):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"FullDuplex\", \"SpeedMbps\"]\n )\n\n # if Full Duplex exists, check if FullDuplexing enabled. If so,\n # remove Speed setting.\n elif \"FullDuplex\" in list(ethernet_data.keys()):\n json_traversal_delete_empty(\n data=ethernet_data, remove_list=[\"FullDuplex\", \"SpeedMbps\"]\n )\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n try:\n if \"FrameSize\" in list(ethernet_data.keys()):\n json_traversal_delete_empty(data=ethernet_data, remove_list=[\"FrameSize\"])\n except (KeyError, NameError) as exp:\n errors.append(\"Unable to remove property %s.\\n\" % exp)\n\n self.patch_eth(_path, ethernet_data, errors)\n\n if errors and \"Virtual\" not in ident_name:\n raise RdmcError(\n \"Ethernet configuration errors were found collectively on adapter: \"\n \"'%s, %s'\\ntype: %s\\nerrors: %s\" % (ident_name, ident_id, _type, errors)\n )","def update_networks(self, agent, force_hard=False):\n\n if self.update_type == \"soft\" and not force_hard:\n self._soft_update(agent.actor, agent.actor_target)\n self._soft_update(agent.critic, agent.critic_target)\n elif self.t_step % self.C == 0 or force_hard:\n self._hard_update(agent.actor, agent.actor_target)\n self._hard_update(agent.critic, agent.critic_target)","def on_the_network_page_click_on_setting_on_the_global_configuration_card(driver):\n assert wait_on_element(driver, 7, '//h1[contains(.,\"Network\")]')\n assert wait_on_element(driver, 5, '//button[@ix-auto=\"button__globalSettings\"]', 'clickable')\n driver.find_element_by_xpath('//button[@ix-auto=\"button__globalSettings\"]').click()","def _post_task_update_advertise_address():\n default_network_interface = None\n\n with open(KUBE_APISERVER_CONFIG) as f:\n lines = f.read()\n m = re.search(REGEXPR_ADVERTISE_ADDRESS, lines)\n if m:\n default_network_interface = m.group(1)\n LOG.debug(' default_network_interface = %s', default_network_interface)\n\n if advertise_address and default_network_interface \\\n and advertise_address != default_network_interface:\n cmd = [\"sed\", \"-i\", \"/oidc-issuer-url/! s/{}/{}/g\".format(default_network_interface, advertise_address),\n KUBE_APISERVER_CONFIG]\n _ = _exec_cmd(cmd)","def setOff(self, command):\r\n self.setDriver('ST', 0)","def reconfiguration_scalability(self):\n\n self.check_run('reconfiguration_scalability')\n self.show_step(1, initialize=True)\n self.env.revert_snapshot(\"reconfigure_nova_ephemeral_disk\")\n\n self.show_step(2)\n cluster_id = self.fuel_web.get_last_created_cluster()\n config = utils.get_config_template('nova_disk')\n structured_config_nova = get_structured_config_dict(config)\n config = utils.get_config_template('keystone')\n structured_config_keystone = get_structured_config_dict(config)\n self.fuel_web.client.upload_configuration(config,\n cluster_id,\n role='controller')\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['controller'])\n\n self.show_step(3)\n task = self.fuel_web.client.apply_configuration(cluster_id,\n role='controller')\n self.fuel_web.assert_task_success(task, timeout=900, interval=5)\n\n self.show_step(4)\n self.check_config_on_remote(controllers, structured_config_keystone)\n\n self.show_step(5)\n os_conn = os_actions.OpenStackActions(\n self.fuel_web.get_public_vip(cluster_id))\n time_expiration = config[\n 'keystone_config']['token/expiration']['value']\n self.check_token_expiration(os_conn, time_expiration)\n\n self.show_step(6)\n bs_nodes = [x for x in self.env.d_env.get_nodes()\n if x.name == 'slave-05' or x.name == 'slave-06']\n self.env.bootstrap_nodes(bs_nodes)\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-05': ['compute', 'cinder']})\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-06': ['controller']})\n\n self.show_step(7)\n self.fuel_web.verify_network(cluster_id)\n\n self.show_step(8)\n self.fuel_web.deploy_cluster_wait(cluster_id)\n\n self.show_step(9)\n self.fuel_web.run_ostf(cluster_id=cluster_id)\n\n self.show_step(10)\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['controller'])\n computes = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\n cluster_id, ['compute'])\n target_controller = [x for x in controllers\n if 'slave-06' in x['name']]\n target_compute = [x for x in computes\n if 'slave-05' in x['name']]\n self.check_config_on_remote(target_controller,\n structured_config_keystone)\n\n self.show_step(11)\n self.check_config_on_remote(target_compute, structured_config_nova)\n\n self.show_step(12)\n self.show_step(13)\n self.show_step(14)\n self.show_step(15)\n self.show_step(16)\n\n os_conn = os_actions.OpenStackActions(\n self.fuel_web.get_public_vip(cluster_id))\n hypervisor_name = target_compute[0]['fqdn']\n self.check_nova_ephemeral_disk(os_conn, cluster_id,\n hypervisor_name=hypervisor_name)\n\n self.show_step(17)\n self.check_token_expiration(os_conn, time_expiration)\n\n self.env.make_snapshot(\"reconfiguration_scalability\", is_make=True)","def unconfigure_stackwise_virtual_dual_active_interfaces(device, dad_links):\n # build a list of commands to send\n command_list = []\n output = ''\n for interface in dad_links:\n command_list.append(f'interface {interface}')\n command_list.append(f'no stackwise-virtual dual-active-detection')\n try:\n output = device.configure(command_list)\n except SubCommandFailure:\n raise SubCommandFailure('Failed to unconfigure stackwise-virtual dual-active-detection interfaces')\n return output"],"string":"[\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def disable_dhcp_helper(self, network_id):\\n network = self.cache.get_network_by_id(network_id)\\n if network:\\n if self.call_driver('disable', network):\\n self.cache.remove(network)\",\n \"def sync_state(self):\\n LOG.info(_('Synchronizing state'))\\n known_networks = set(self.cache.get_network_ids())\\n\\n try:\\n active_networks = set(self.plugin_rpc.get_active_networks())\\n for deleted_id in known_networks - active_networks:\\n self.disable_dhcp_helper(deleted_id)\\n\\n for network_id in active_networks:\\n self.refresh_dhcp_helper(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Unable to sync network state.'))\",\n \"def enable_dhcp_helper(self, network_id):\\n try:\\n network = self.plugin_rpc.get_network_info(network_id)\\n except:\\n self.needs_resync = True\\n LOG.exception(_('Network %s RPC info call failed.') % network_id)\\n return\\n\\n if not network.admin_state_up:\\n return\\n\\n for subnet in network.subnets:\\n if subnet.enable_dhcp:\\n if self.call_driver('enable', network):\\n self.cache.put(network)\\n break\",\n \"async def async_turn_off(self):\\n path = \\\"/ip/firewall/nat\\\"\\n param = \\\".id\\\"\\n value = None\\n for uid in self._ctrl.data[\\\"nat\\\"]:\\n if (\\n self._ctrl.data[\\\"nat\\\"][uid][\\\"name\\\"]\\n == f\\\"{self._data['protocol']}:{self._data['dst-port']}\\\"\\n ):\\n value = self._ctrl.data[\\\"nat\\\"][uid][\\\".id\\\"]\\n\\n mod_param = \\\"disabled\\\"\\n mod_value = True\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n await self._ctrl.async_update()\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def reconfigure(self):\\n log.debug('Reconfiguring and restarting the DHCP daemon...')\\n\\n # Don't set the daemon running status here, but let the status\\n # check take care of that.\\n\\n p = Properties(self.storage, CONFIG_SECTION)\\n p.addCallback(self.changed).\\\\\\n addCallback(lambda trigger: p.load()).\\\\\\n addCallback(self.emit_config, p).\\\\\\n addCallback(self.restart_daemon).\\\\\\n addErrback(self.restart_error)\",\n \"def _disable_wifi_ap(self):\\n call(['systemctl', 'disable', 'hostapd', ])\\n call(['systemctl', 'disable', 'dnsmasq', ])\\n\\n context = self._get_ap_context()\\n self._write_system_template('/etc/network/interfaces', 'interfaces.conf', context)\\n self._write_system_template('/etc/dhcpcd.conf', 'dhcpcd.conf', context)\",\n \"def dhcp_agent_network_remove(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"async def async_turn_off(self):\\n path = \\\"/interface\\\"\\n param = \\\"default-name\\\"\\n if \\\"-\\\" in self._data[\\\"port-mac-address\\\"]:\\n param = \\\"name\\\"\\n value = self._data[param]\\n mod_param = \\\"disabled\\\"\\n mod_value = True\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n\\n if self._data[\\\"poe-out\\\"] == \\\"auto-on\\\":\\n path = \\\"/interface/ethernet\\\"\\n self._ctrl.set_value(path, param, value, \\\"poe-out\\\", \\\"off\\\")\\n\\n await self._ctrl.async_update()\",\n \"def reset_network(self, instance):\\n LOG.debug(\\\"reset_network\\\")\\n return\",\n \"def renew_dhcp_lease(self):\\n\\t\\tresponse = os.system(\\\"/sbin/dhclient -r;/sbin/dhclient\\\")\\n\\t\\tif response != 0:\\n\\t\\t\\tprint \\\"Network restart failed. DHCP Lease failed.\\\"\",\n \"def network_create_end(self, payload):\\n network_id = payload['network']['id']\\n self.enable_dhcp_helper(network_id)\",\n \"def update_network(self, context, net_id, network):\\n\\n LOG.debug(_(\\\"QuantumRestProxyV2.update_network() called\\\"))\\n\\n # Validate Args\\n if network[\\\"network\\\"].get(\\\"admin_state_up\\\"):\\n if network[\\\"network\\\"][\\\"admin_state_up\\\"] is False:\\n LOG.warning(_(\\\"Network with admin_state_up=False are not yet \\\"\\n \\\"supported by this plugin. Ignoring setting for \\\"\\n \\\"network %s\\\", net_name))\\n\\n # update DB\\n orig_net = super(QuantumRestProxyV2, self).get_network(context, net_id)\\n tenant_id = orig_net[\\\"tenant_id\\\"]\\n new_net = super(QuantumRestProxyV2, self).update_network(\\n context, net_id, network)\\n\\n # update network on network controller\\n if new_net[\\\"name\\\"] != orig_net[\\\"name\\\"]:\\n try:\\n resource = NETWORKS_PATH % (tenant_id, net_id)\\n data = {\\n \\\"network\\\": new_net,\\n }\\n ret = self.servers.put(resource, data)\\n if not self.servers.action_success(ret):\\n raise RemoteRestError(ret[2])\\n except RemoteRestError as e:\\n LOG.error(_(\\\"QuantumRestProxyV2: Unable to update remote \\\"\\n \\\"network: %s\\\"), e.message)\\n # reset network to original state\\n super(QuantumRestProxyV2, self).update_network(\\n context, id, orig_net)\\n raise\\n\\n # return updated network\\n return new_net\",\n \"def apply_network_settings(**settings):\\n if 'require_reboot' not in settings:\\n settings['require_reboot'] = False\\n if 'apply_hostname' not in settings:\\n settings['apply_hostname'] = False\\n\\n hostname_res = True\\n if settings['apply_hostname'] in _CONFIG_TRUE:\\n if 'hostname' in settings:\\n hostname_res = __salt__['network.mod_hostname'](settings['hostname'])\\n else:\\n log.warning(\\n 'The network state sls is trying to apply hostname '\\n 'changes but no hostname is defined.'\\n )\\n hostname_res = False\\n\\n res = True\\n if settings['require_reboot'] in _CONFIG_TRUE:\\n log.warning(\\n 'The network state sls is requiring a reboot of the system to '\\n 'properly apply network configuration.'\\n )\\n res = True\\n else:\\n res = __salt__['cmd.run']('/etc/netstart restart')\\n\\n return hostname_res and res\",\n \"async def async_set_wifi_led_off(self):\\n return\",\n \"def subnet_update_end(self, payload):\\n network_id = payload['subnet']['network_id']\\n self.refresh_dhcp_helper(network_id)\",\n \"def wifi_off(self):\\n self._clear_read_buffer()\\n self._write_cmd(\\\"PE00\\\")\\n time.sleep(100e-3)\",\n \"def dvs_update_network(self):\\n self.show_step(1)\\n self.env.revert_snapshot(\\\"dvs_vcenter_systest_setup\\\")\\n\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n\\n self.show_step(2)\\n os_ip = self.fuel_web.get_public_vip(cluster_id)\\n os_conn = os_actions.OpenStackActions(\\n os_ip, SERVTEST_USERNAME,\\n SERVTEST_PASSWORD,\\n SERVTEST_TENANT)\\n\\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\\n\\n net_1 = os_conn.create_network(\\n network_name=self.net_data[0].keys()[0],\\n tenant_id=tenant.id)['network']\\n\\n os_conn.create_subnet(\\n subnet_name=net_1['name'],\\n network_id=net_1['id'],\\n cidr=self.net_data[0][self.net_data[0].keys()[0]],\\n ip_version=4)\\n\\n assert_true(os_conn.get_network(net_1['name'])['id'] == net_1['id'])\\n\\n self.show_step(3)\\n os_conn.neutron.update_network(net_1[\\\"id\\\"],\\n {\\\"network\\\": {\\\"name\\\": 'net_2'}})\\n\\n assert_true(os_conn.get_network('net_2')['id'] == net_1['id'])\\n\\n self.show_step(4)\\n default_net = os_conn.nova.networks.find(label=self.inter_net_name)\\n os_conn.neutron.update_network(\\n default_net.id, {\\\"network\\\": {\\\"name\\\": 'spring'}})\\n\\n assert_true(os_conn.get_network('spring')['id'] == default_net.id)\",\n \"def elAddNetworkConfigurationWithDhcp(self, device):\\n commandSection = self.sectionByName(\\\"command\\\")\\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\\n deviceMatch = re.match(r\\\"([^0-9]+)([0-9])\\\", device)\\n if deviceMatch:\\n # e.g. \\\"eth0\\\"\\n devicePrefix = deviceMatch.group(1)\\n deviceNumber = deviceMatch.group(2)\\n deviceNumber = int(deviceNumber)\\n for i in range(8, deviceNumber - 1, -1):\\n deviceI = devicePrefix + str(i)\\n deviceIPlus1 = devicePrefix + str(i + 1)\\n # move up by one device each network configuration\\n commandSection.string = re.sub(r\\\"(?m)^([ \\\\t]*network[ \\\\t]+.*--device[ \\\\t]*(?:=|[ \\\\t])[ \\\\t]*)\\\" + re.escape(deviceI) + r\\\"(.*)$\\\",\\n r\\\"\\\\g<1>\\\" + deviceIPlus1 + r\\\"\\\\g<2>\\\",\\n commandSection.string)\\n # not --noipv6\\n networkConfiguration = \\\"network --device=\\\" + device + \\\" --bootproto=dhcp --onboot=yes --activate\\\"\\n if deviceMatch and deviceNumber == 0:\\n # having configuration of eth0 first appears to be more conducive to overall success,\\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\\n # \\\"... in installer environment. Device of the first network command is activated if network is required,\\n # e.g. in case of network installation ...\\\",\\n commandSection.string = networkConfiguration + \\\"\\\\n\\\" \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + commandSection.string\\n else:\\n commandSection.string = commandSection.string \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + networkConfiguration + \\\"\\\\n\\\"\",\n \"def _cycle_wifi(mode=None):\\n call(['ifdown', settings.WIFI_INTERFACE])\\n if mode is not None:\\n call(['iwconfig', settings.WIFI_INTERFACE, 'mode', mode])\\n call(['ifup', settings.WIFI_INTERFACE])\",\n \"def remove_network(self, name_of_vm):\\n try:\\n # vmachine = self.get_vm_by_name(name_of_vm)\\n vmachine = self.get_dc_object([vim.VirtualMachine], name_of_vm)\\n network = None\\n devices = vmachine.config.hardware.device\\n networks = []\\n for device in devices:\\n if isinstance(device, vim.vm.device.VirtualEthernetCard):\\n networks.append(device)\\n status = 'error'\\n if not networks:\\n log.info(\\\"INFO: No network adapters connected to the VM to remove\\\")\\n status = 'success'\\n else:\\n for network in networks:\\n name = network.deviceInfo.label\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\\n nic_spec.device = network\\n remove_nic = vim.vm.ConfigSpec()\\n remove_nic.deviceChange = [nic_spec]\\n task = WaitForTask(vmachine.ReconfigVM_Task(spec=remove_nic))\\n\\n if task == 'success':\\n log.info(\\\"removed '{}' network adapter : {}\\\".format(name, name_of_vm))\\n else:\\n log.info(\\\"Could not '{}' Remove Network adapter: {}\\\".format(name, name_of_vm))\\n status = 'success'\\n return status\\n except Exception as error:\\n log.info(\\\"Error in 'remove_nic' keyword... {} \\\\n {}\\\".format(error, error.message))\",\n \"def update_cmts_isc_dhcp_config(self, board_config):\\n self.setup_dhcp_config(board_config)\\n self.setup_dhcp6_config(board_config)\\n raise NotImplementedError\",\n \"def _get_net_dhcp_relay(self, context, net_id):\\n pass\",\n \"def is_configure_with_dhcp(self):\\n\\t\\treturn bool(call_sdk_function('PrlVmDevNet_IsConfigureWithDhcp', self.handle))\",\n \"def unconfigure_global_dual_active_recovery_reload_disable(device):\\n # build a list of commands to send\\n # Add stackwise-virtual as first element in the list\\n # Enables dual-active recovery-reload\\n command_list = ['stackwise-virtual']\\n command_list.append(f'no dual-active recovery-reload-disable')\\n try:\\n output = device.configure(command_list)\\n except SubCommandFailure:\\n raise SubCommandFailure('Failed to Disable global stackwise-virtual dual-active recovery-reload')\\n return output\",\n \"def disable(ctx):\\n config_db = ConfigDBConnector()\\n config_db.connect()\\n config_db.mod_entry(\\\"NAT_GLOBAL\\\", \\\"Values\\\", {\\\"admin_mode\\\": \\\"disabled\\\"})\",\n \"def bring_down_nic(self, nics, hostname): \\n if nics is None:\\n return False\\n\\n pool = WorkerPool()\\n\\n try: \\n #get the ip address of the interface\\n for nic in nics:\\n cmd = Command(name='get the ip of the interface', cmdStr=\\\"/sbin/ifconfig %s | grep \\\\'inet addr:\\\\' | cut -d: -f2 | awk \\\\'{ print $1}\\\\'\\\" % nic, ctxt=REMOTE, remoteHost=hostname)\\n cmd.run(validateAfter=True)\\n results = cmd.get_results()\\n if results.rc != 0:\\n raise Exception('Unable to map interface to ipaddress') \\n\\n self.nic_to_address_map[(nic, hostname)] = results.stdout.split()[0].strip()\\n\\n for nic in nics:\\n tinctest.logger.info(\\\"Bringing down %s:%s ...\\\" % (hostname, nic)) \\n cmd = Command(name='bring NIC down', cmdStr='sudo /sbin/ifdown %s' % nic, ctxt=REMOTE, remoteHost=hostname)\\n pool.addCommand(cmd)\\n\\n pool.join()\\n for cmd in pool.getCompletedItems():\\n results = cmd.get_results()\\n if results.rc != 0:\\n return False\\n finally:\\n pool.haltWork()\\n pool.joinWorkers()\\n pool.join()\\n\\n return True\",\n \"async def disable_protection(self) -> None:\\n await self._request(\\n \\\"dns_config\\\", method=\\\"POST\\\", json_data={\\\"protection_enabled\\\": False},\\n )\",\n \"async def async_turn_on(self):\\n path = \\\"/ip/firewall/nat\\\"\\n param = \\\".id\\\"\\n value = None\\n for uid in self._ctrl.data[\\\"nat\\\"]:\\n if (\\n self._ctrl.data[\\\"nat\\\"][uid][\\\"name\\\"]\\n == f\\\"{self._data['protocol']}:{self._data['dst-port']}\\\"\\n ):\\n value = self._ctrl.data[\\\"nat\\\"][uid][\\\".id\\\"]\\n\\n mod_param = \\\"disabled\\\"\\n mod_value = False\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n await self._ctrl.force_update()\",\n \"def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\\n modem.configuration[AUTO_LED].set_value(not auto_led)\\n modem.configuration[DEADMAN].set_value(not deadman)\",\n \"def leaveNetwork():\\r\\n cmd = \\\"zerotier-cli leave {}\\\".format(networkID)\\r\\n os.system(cmd)\",\n \"def dhcp_renew(ifname):\\n\\n logging.debug('Renewing %s DHCP lease...', ifname)\\n\\n try:\\n subprocess.call(['dhcpcd', '--rebind', ifname])\\n except OSError, err:\\n if err.errno != errno.ENOENT:\\n raise err\\n\\n try:\\n call_timeout(['dhclient', '-1', ifname], timeout=5)\\n except OSError, err:\\n if err.errno == errno.ENOENT:\\n logging.critical('Neither dhcpcd nor dhclient were found.')\\n else:\\n raise err\",\n \"def switch_network(self,type = None):\\n network_type = self.appconfig(type,\\\"Settings\\\")\\n self.logger.debug(\\\"Switch network to %s:%s.\\\" % (type,network_type))\\n if self.enter_settings(u\\\"More…\\\"):\\n if self.device(text=\\\"Mobile networks\\\").exists:\\n self.device(text=\\\"Mobile networks\\\").click()\\n if self.device(text=\\\"Preferred network mode\\\").wait.exists(timeout=self.timeout):\\n self.device(text=\\\"Preferred network mode\\\").click()\\n if self.device(resourceId=\\\"android:id/buttonPanel\\\").wait.exists(timeout=self.timeout):\\n self.device(text=network_type).click()\\n print self._is_connected(type)\\n self.back_to_home()\",\n \"def ethernet_off(self):\\n if not self.healthy:\\n self.health_check()\\n if not self._ethernet_switch:\\n raise errors.CapabilityNotReadyError(\\n device_name=self._device_name,\\n msg=\\\"Not set up for ethernet switching.\\\")\\n self._ethernet_switch.switch_power.power_off(self.ethernet_port_number)\",\n \"def down(self):\\n self.update(admin_state='0')\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def update_network(self, dbnetwork, qipinfo):\\n\\n # We don't want to add the plenary to self.plenaries if we aren't going\\n # to change anything\\n plenary = Plenary.get_plenary(dbnetwork)\\n updated = False\\n\\n if dbnetwork.name != qipinfo.name:\\n self.logger.client_info(\\\"Setting network {0!s} name to {1}\\\"\\n .format(dbnetwork, qipinfo.name))\\n dbnetwork.name = qipinfo.name\\n if dbnetwork.network_type != qipinfo.network_type:\\n self.logger.client_info(\\\"Setting network {0!s} type to {1}\\\"\\n .format(dbnetwork, qipinfo.network_type))\\n dbnetwork.network_type = qipinfo.network_type\\n if dbnetwork.location != qipinfo.location:\\n self.logger.client_info(\\\"Setting network {0!s} location to {1:l}\\\"\\n .format(dbnetwork, qipinfo.location))\\n dbnetwork.location = qipinfo.location\\n if dbnetwork.side != qipinfo.side:\\n self.logger.client_info(\\\"Setting network {0!s} side to {1}\\\"\\n .format(dbnetwork, qipinfo.side))\\n dbnetwork.side = qipinfo.side\\n if dbnetwork.network_compartment != qipinfo.compartment:\\n self.logger.client_info(\\\"Setting network {0!s} compartment to {1!s}\\\"\\n .format(dbnetwork, qipinfo.compartment))\\n dbnetwork.network_compartment = qipinfo.compartment\\n\\n if dbnetwork in self.session.dirty:\\n updated = True\\n\\n old_rtrs = set(dbnetwork.router_ips)\\n new_rtrs = set(qipinfo.routers)\\n\\n del_routers = []\\n for router in dbnetwork.routers:\\n if router.ip in old_rtrs - new_rtrs:\\n del_routers.append(router)\\n\\n for router in del_routers:\\n self.logger.client_info(\\\"Removing router {0:s} from \\\"\\n \\\"{1:l}\\\".format(router.ip, dbnetwork))\\n for dns_rec in router.dns_records:\\n if dns_rec.is_unused:\\n delete_dns_record(dns_rec)\\n dbnetwork.routers.remove(router)\\n updated = True\\n\\n for ip in new_rtrs - old_rtrs:\\n self.add_router(dbnetwork, ip)\\n updated = True\\n\\n if updated:\\n self.plenaries.append(plenary)\\n\\n # TODO: add support for updating router locations\\n\\n return dbnetwork.netmask == qipinfo.address.netmask\",\n \"def network_settings():\\n for host in online_hosts:\\n with settings(warn_only=True):\\n\\n result1 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSDomainName <Domain Name>\\\")\\n if result1.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSDomainName failed \\\")\\n\\n result2 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSServer1 \\\"+colo_dns[DEFAULT_COLO ][0])\\n if result2.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSServer1 failed \\\")\\n\\n result3 = sudo(\\\"racadm5 -r \\\"+host+\\\" -u root -p \\\"+PASSWORD+\\\" config -g cfgLanNetworking -o cfgDNSServer2 \\\"+colo_dns[DEFAULT_COLO ][1])\\n if result3.failed:\\n logging.error(\\\"Host: [ \\\"+host+\\\" ] : \\\" + \\\"Configuration for DNSServer2 failed \\\")\",\n \"def turn_off(self):\\n self._state = False\\n if(self._device['type'] == '_DT-PLUG' or self._device['type'] == '_THIMR'):\\n self._send_cmd(self._device, 'cmd=ctrl&devices={[' + self._device[\\\"sid\\\"] + ']}&op={\\\"cmd\\\":5,\\\"op\\\":0 }', 5)\\n if(self._device['type'] == '_REALY2' or self._device['type'] == '_REALY4'): \\n self._send_cmd(self._device, 'cmd=ctrl&devices={[' + self._device[\\\"sid\\\"] + ']}&op={\\\"cmd\\\":5,\\\"'+ self._data_key +'\\\":0 }', 5)\",\n \"def set_into_managed_mode(wifi_name):\\n \\n subprocess.run([\\\"ip\\\", \\\"link\\\", \\\"set\\\", wifi_name, \\\"down\\\"])\\n subprocess.run([\\\"iwconfig\\\", wifi_name, \\\"mode\\\", \\\"managed\\\"])\\n subprocess.run([\\\"ip\\\", \\\"link\\\", \\\"set\\\", wifi_name, \\\"up\\\"])\\n subprocess.run([\\\"service\\\", \\\"NetworkManager\\\", \\\"start\\\"])\",\n \"def maintainance(self, on_off, instance_type):\\n print((\\\"enabling\\\" if on_off else \\\"disabling\\\") + \\\" Maintainer mode\\\")\\n tries = 60\\n while True:\\n reply = self.send_request(\\n instance_type,\\n requests.put,\\n \\\"/_admin/cluster/maintenance\\\",\\n '\\\"on\\\"' if on_off else '\\\"off\\\"',\\n )\\n if len(reply) > 0:\\n print(\\\"Reply: \\\" + str(reply[0].text))\\n if reply[0].status_code == 200:\\n return\\n print(f\\\"Reply status code is {reply[0].status_code}. Sleeping for 3 s.\\\")\\n time.sleep(3)\\n tries -= 1\\n else:\\n print(\\\"Reply is empty. Sleeping for 3 s.\\\")\\n time.sleep(3)\\n tries -= 1\\n if tries <= 0:\\n action = \\\"enable\\\" if on_off else \\\"disable\\\"\\n raise Exception(f\\\"Couldn't {action} maintainance mode!\\\")\",\n \"def change_adp(self, network: str):\\r\\n self.ip = network\\r\\n self.adp = self.ipv4_adp[network]\\r\\n self.mac = self.ipv4_mac[network].replace('-', ':')\\r\\n # print(self.adp, self.ip, self.mac)\\r\",\n \"def disable_duplicate_address_detection(self, network):\\n # For non-external networks, duplicate address detection isn't\\n # necessary (and it sometimes results in race conditions for services\\n # that attempt to bind to addresses before they're ready).\\n\\n if network.network_type != network.TYPE_EXTERNAL:\\n real_ifname = self.generic_to_host(network.interface.ifname)\\n try:\\n utils.execute([\\n 'sysctl', '-w', 'net.ipv6.conf.%s.accept_dad=0'\\n % real_ifname\\n ], self.root_helper)\\n except RuntimeError:\\n LOG.debug(\\n 'Failed to disable v6 dad on %s' % real_ifname\\n )\",\n \"def is_configure_with_dhcp(self):\\n\\t\\treturn bool(call_sdk_function('PrlSrvCfgNet_IsConfigureWithDhcp', self.handle))\",\n \"def net_undefine(network, server, virt=\\\"Xen\\\"):\\n\\n cmd = \\\"virsh -c %s net-undefine %s 2>/dev/null\\\" % (virt2uri(virt), network)\\n ret, out = run_remote(server, cmd)\\n\\n return ret\",\n \"def update_network(**kwargs):\\n\\n ip_addr = kwargs.get('ip_addr')\\n is_private = kwargs.get('is_private')\\n name = kwargs.get('name')\\n dns_names = kwargs.get('dns_names')\\n is_scanning = kwargs.get('is_scanning', False)\\n network_id = make_shortuuid(name)\\n\\n network = {\\n 'dns_names': dns_names,\\n 'ip_addr': ip_addr,\\n 'is_private' : is_private,\\n 'name': name,\\n 'id': network_id,\\n 'is_scanning': is_scanning,\\n 'updated_count': 0\\n\\n }\\n\\n network_exists = r.table(\\\"networks\\\").insert([network], conflict=\\\"update\\\")\\n\\n return network_exists.run(conn)\",\n \"def network_refresh(self, kwargs=None):\\n scode, hosts = Rest.get('Host')\\n filter = {}\\n n = 1\\n e = {}\\n data = []\\n for host in hosts:\\n os.environ[\\\"DOCKER_HOST\\\"] = host['Ip'] + \\\":\\\" + str(host['Port'])\\n filter['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n self.client = docker.from_env()\\n try:\\n networks = self.client.networks.list(**kwargs)\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n continue\\n\\n if len(networks) == 0:\\n Console.info(\\\"No network exist\\\" + host['Ip'])\\n continue\\n\\n for networkm in networks:\\n network = networkm.__dict__['attrs']\\n network['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n data.append(network)\\n d = {}\\n d['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n d['Id'] = network['Id']\\n d['Name'] = network['Name']\\n d['Containers'] = network['Containers']\\n e[n] = d\\n n = n + 1\\n r = Rest.delete('Network', filter)\\n r = Rest.post('Network', data)\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))\",\n \"async def async_turn_off(self, **kwargs):\\n try:\\n if await self._api.set_relay_state(self._dev_id, \\\"off\\\"):\\n self._is_on = False\\n self.async_write_ha_state()\\n except Smile.PlugwiseError:\\n _LOGGER.error(\\\"Error while communicating to device\\\")\",\n \"def clean_rebind_test(**kwargs):\\n if 'verify_traffic' not in kwargs:\\n kwargs['verify_traffic'] = False\\n prepare_subscriber_traffic(**kwargs)\\n device_id = kwargs.get('device_id', bbe.get_devices(device_tags='dut', id_only=True)[0])\\n switch_id = kwargs.get('switch_id', 'r1')\\n switch_handle = t.get_handle(switch_id)\\n switch_access_intf = bbe.get_interfaces(switch_id, interfaces='access')\\n status = True\\n for iteration in range(1, int(kwargs.get('iteration', 1)) + 1):\\n t.log(\\\"disable access ports in switch in iteration #{}\\\".format(iteration))\\n port_command_list = []\\n status = True\\n for access in switch_access_intf:\\n port_command_list.append(\\\"set interfaces {} disable\\\".format(access.interface_pic))\\n switch_handle.config(command_list=port_command_list)\\n switch_handle.commit()\\n t.log(\\\"verify access ports in down state\\\")\\n for access in switch_access_intf:\\n resp = switch_handle.pyez('get_interface_information', level_extra='terse',\\n interface_name=access.interface_pic).resp\\n if resp.findtext('physical-interface/admin-status') == 'down' and resp.findtext(\\n 'physical-interface/oper-status') == 'down':\\n t.log(\\\"interface {} is in down state\\\".format(access.interface_pic))\\n else:\\n t.log('WARN', \\\"interface {} is in state {}\\\".format(access.interface_pic, resp))\\n status = False\\n\\n if not status:\\n for access in switch_access_intf:\\n port_command_list.append(\\\"delete interfaces {} disable\\\".format(access.interface_pic))\\n switch_handle.config(command_list=port_command_list)\\n switch_handle.commit()\\n raise Exception(\\\"some interfaces failed to be in down state after disable\\\")\\n base_time = time.time()\\n while time.time() - base_time < 1800:\\n router_count = get_router_sub_summary(device_id)['client']\\n tester_count = get_rt_subs_info()['rt_sessions_up']\\n if router_count == 0 and tester_count == 0:\\n duration = time.time() - base_time\\n t.log(\\\"all subscribers cleared from tester and router after {}s in iteration #{}\\\".format(duration,\\n iteration))\\n break\\n t.log(\\\"sleep 30s , waiting for clients cleared\\\")\\n time.sleep(30)\\n\\n result = get_router_sub_summary(device_id)\\n\\n if result['client'] != 0 or 'terminated' in result or 'terminating' in result or 'init' in result:\\n status = False\\n t.log('WARN', 'some subscribers stuck in unexpected state in iteration #{}'.format(iteration))\\n\\n for access in switch_access_intf:\\n port_command_list.append(\\\"delete interfaces {} disable\\\".format(access.interface_pic))\\n switch_handle.config(command_list=port_command_list)\\n switch_handle.commit()\\n time.sleep(10)\\n t.log(\\\"verify access ports in up state in iteration {}\\\".format(iteration))\\n for access in switch_access_intf:\\n resp = switch_handle.pyez('get_interface_information', level_extra='terse',\\n interface_name=access.interface_pic).resp\\n if resp.findtext('physical-interface/admin-status') == 'up' and resp.findtext(\\n 'physical-interface/oper-status') == 'up':\\n t.log(\\\"interface {} is in up state\\\".format(access.interface_pic))\\n else:\\n t.log('WARN', \\\"interface {} is in state {}\\\".format(access.interface_pic, resp))\\n status = False\\n\\n if not status:\\n raise Exception(\\\"clean test failed\\\")\\n ##set the rt subscriber state to stopped, since it is not teared down by actions\\n t.log(\\\"login subscriber and verify traffic after restore the connection in iteration #{}\\\".format(iteration))\\n prepare_subscriber_traffic(**kwargs)\",\n \"def down_nodes(nodelist, reason):\\n if isinstance(nodelist, list):\\n nodelist = util.to_hostlist(nodelist)\\n run(f\\\"{lkp.scontrol} update nodename={nodelist} state=down reason='{reason}'\\\")\",\n \"def _update_dnsmasq(self, network_id):\\n\\n # Check whether we should really do the following processing.\\n if self.suppress_dnsmasq_updates:\\n LOG.debug(\\\"Don't update dnsmasq yet;\\\"\\n \\\" must be processing a snapshot\\\")\\n self.dirty_networks.add(network_id)\\n return\\n\\n self.dnsmasq_updater.update_network(network_id)\",\n \"def config_networking(\\n self, network_obj, ip, netmask, gateway, domain, dns, guest_hostname\\n ):\\n\\n global_ip = vim.vm.customization.GlobalIPSettings()\\n adapter_map = vim.vm.customization.AdapterMapping()\\n adapter_map.adapter = vim.vm.customization.IPSettings()\\n adapter_map.macAddress = network_obj.macAddress\\n if ip:\\n adapter_map.adapter.ip = vim.vm.customization.FixedIp()\\n adapter_map.adapter.ip.ipAddress = ip\\n else:\\n adapter_map.adapter.ip = vim.vm.customization.DhcpIpGenerator()\\n adapter_map.adapter.subnetMask = netmask\\n adapter_map.adapter.gateway = gateway\\n global_ip.dnsServerList = dns\\n adapter_map.adapter.dnsDomain = domain\\n ident = vim.vm.customization.LinuxPrep()\\n ident.hostName = vim.vm.customization.FixedName()\\n if guest_hostname:\\n ident.hostName.name = guest_hostname\\n else:\\n ident.hostName.name = self.vm_obj.name\\n custom_spec = vim.vm.customization.Specification()\\n custom_spec.nicSettingMap = [adapter_map]\\n custom_spec.identity = ident\\n custom_spec.globalIPSettings = global_ip\\n return self.vm_obj.Customize(spec=custom_spec)\",\n \"def call_driver(self, action, network):\\n if self.conf.use_namespaces:\\n namespace = NS_PREFIX + network.id\\n else:\\n namespace = None\\n try:\\n # the Driver expects something that is duck typed similar to\\n # the base models.\\n driver = self.dhcp_driver_cls(self.conf,\\n network,\\n self.conf.root_helper,\\n self.device_manager,\\n namespace)\\n getattr(driver, action)()\\n return True\\n\\n except Exception, e:\\n self.needs_resync = True\\n LOG.exception('Unable to %s dhcp.' % action)\",\n \"def remove_network_adapter(self, network_obj):\\n\\n nic_spec = vim.vm.device.VirtualDeviceSpec()\\n nic_spec.operation = vim.vm.device.VirtualDeviceSpec.Operation.remove\\n nic_spec.device = network_obj\\n config_spec = vim.vm.ConfigSpec()\\n config_spec.deviceChange = [nic_spec]\\n return self.vm_obj.ReconfigVM_Task(spec=config_spec)\",\n \"def disabled(config):\\n disable(config)\\n reload_service('apache2')\",\n \"def dhcp_used(self, dhcp_used):\\n\\n self._dhcp_used = dhcp_used\",\n \"def IsLinkdown(nic,timeout):\\n nic = nic.strip()\\n current = time.time()\\n timeout += current\\n while current < timeout:\\n data = os.popen(\\\"ipconfig\\\").read().split(\\\"Ethernet adapter\\\")\\n for item in data:\\n if item.count(nic) and item.count(\\\"isconnected\\\"): #Disconnected\\n return 1\\n time.sleep(0.5)\\n current = time.time()\\n return 0\",\n \"def destroy(self, network, device_name):\\n if self.conf.use_namespaces:\\n namespace = NS_PREFIX + network.id\\n else:\\n namespace = None\\n\\n self.driver.unplug(device_name, namespace=namespace)\\n\\n self.plugin.release_dhcp_port(network.id,\\n self.get_device_id(network))\",\n \"def deconfigure(self):\\n yield self._r2rm_client.until_synced(2)\\n response = yield self._r2rm_client.req.force_deconfigure_board(self._icom_id)\\n if not response.reply.reply_ok():\\n self.log.error(\\\"Error on deconfigure request: {}\\\".format(\\n response.reply.arguments[1]))\\n raise EddRoach2ProductError(response.reply.arguments[1])\\n self.teardown_sensors()\\n self._firmware = None\\n self._icom_id = None\",\n \"def randomize_interface(self, interface):\\n mac = self.get_rand_mac()\\n command = \\\"ifconfig %s hw ether %s\\\" % (interface, mac)\\n\\n subprocess.call(\\\"ifconfig %s down\\\" % interface, shell=True)\\n rc = subprocess.call(command, shell=True)\\n subprocess.call(\\\"ifconfig %s up\\\" % interface, shell=True)\\n\\n return rc\",\n \"def update_network(self, context, net_id, network):\\n LOG.debug(_(\\\"NeutronRestProxyV2.update_network() called\\\"))\\n\\n self._warn_on_state_status(network['network'])\\n\\n session = context.session\\n with session.begin(subtransactions=True):\\n new_net = super(NeutronRestProxyV2, self).update_network(\\n context, net_id, network)\\n self._process_l3_update(context, new_net, network['network'])\\n\\n # update network on network controller\\n self._send_update_network(new_net, context)\\n return new_net\",\n \"def _configure_ipsec_networking(self):\\n with hide(*fab_output_hides):\\n changed = upload_template_changed(\\n '../config/tpl/sysctl.conf',\\n '/etc/sysctl.conf',\\n use_sudo=True,\\n mode=0600,\\n )\\n if changed:\\n sudo('sysctl -p /etc/sysctl.conf')\\n\\n return changed\",\n \"def networkMode(self, networkMode):\\n\\n # Setting the network mode can take a bit of time, so give it 10 seconds\\n # to finish\\n response = self.at.sendCommand(f\\\"AT+CFUN={networkMode}\\\", timeout = 10)\\n\\n if not response:\\n raise modem.AtError(response, \\\"Failed to set network mode\\\")\",\n \"def setup_dhcp_config(self, board_config):\\n raise NotImplementedError\",\n \"def test_wifi_scanner_with_wifi_off(self):\\n self.log.debug(\\\"Make sure wifi is off.\\\")\\n wutils.wifi_toggle_state(self.dut, False)\\n self.start_wifi_scanner_single_scan_expect_failure(\\n self.default_scan_setting)\\n self.log.debug(\\\"Turning wifi back on.\\\")\\n wutils.wifi_toggle_state(self.dut, True)\",\n \"def configure_global_dual_active_recovery_reload_disable(device):\\n # build a list of commands to send\\n # Add stackwise-virtual as first element in the list\\n # Disables dual-active recovery-reload\\n command_list = ['stackwise-virtual']\\n command_list.append(f'dual-active recovery-reload-disable')\\n try:\\n output = device.configure(command_list)\\n except SubCommandFailure:\\n raise SubCommandFailure('Failed to Enable global stackwise-virtual dual-active recovery-reload')\\n return output\",\n \"def update_network_section(self):\\n rconfig = configparser.RawConfigParser()\\n rconfig.read(self.conf_file)\\n if self.ext_net:\\n if not rconfig.has_section('network'):\\n rconfig.add_section('network')\\n rconfig.set('network', 'public_network_id', self.ext_net.id)\\n rconfig.set('network', 'floating_network_name', self.ext_net.name)\\n rconfig.set('network-feature-enabled', 'floating_ips', True)\\n else:\\n if not rconfig.has_section('network-feature-enabled'):\\n rconfig.add_section('network-feature-enabled')\\n rconfig.set('network-feature-enabled', 'floating_ips', False)\\n with open(self.conf_file, 'w', encoding='utf-8') as config_file:\\n rconfig.write(config_file)\",\n \"def validate_nic_down(self): \\n\\n pool = WorkerPool()\\n\\n try: \\n for nic, hostname in self.nic_to_address_map:\\n address = self.nic_to_address_map[(nic, hostname)]\\n cmd = Ping('ping validation', address, ctxt=REMOTE, remoteHost='localhost')\\n pool.addCommand(cmd)\\n pool.join()\\n\\n for cmd in pool.getCompletedItems():\\n results = cmd.get_results()\\n if results.rc == 0:\\n return False\\n finally:\\n pool.haltWork()\\n pool.joinWorkers()\\n pool.join()\\n\\n tinctest.logger.info(\\\"Successfully brought down nics ...\\\") \\n return True\",\n \"def disable_relays(self):\\n #ensure clock low and data high\\n self.e.clear_bit(7)\\n self.e.set_bit(5)\\n time.sleep(0.01)\\n\\n #pulse the clock line\\n self.e.set_bit(7)\\n time.sleep(0.01)\\n self.e.clear_bit(7)\\n\\n #clear the data line\\n self.e.clear_bit(5)\",\n \"def set_dhcpserver_enabled(self, bEnabled):\\n\\t\\tcall_sdk_function('PrlVirtNet_SetDHCPServerEnabled', self.handle, bEnabled)\",\n \"def filter_update_network_attributes(network, context):\\n try_del(network, ['id', 'status', 'subnets', 'tenant_id'])\",\n \"def turn_off(self, **kwargs):\\n request = requests.post(self._resource, data=\\\"0\\\", timeout=10)\\n if (request.status_code == 200) or (request.status_code == 201):\\n self._state = False\\n else:\\n _LOGGER.error(\\\"Can't turn off %s. Is resource/endpoint offline?\\\",\\n self._resource)\\n\\n self.schedule_update_ha_state()\",\n \"def _validate_change_when_exposing_in_dhcp(self):\\n if self.pk and settings.DHCP_ENTRY_FORBID_CHANGE:\\n from ralph.networks.models import IPAddress\\n old_obj = self.__class__._default_manager.get(pk=self.pk)\\n try:\\n if old_obj.ipaddress.dhcp_expose:\\n if old_obj.mac != self.mac:\\n raise ValidationError(\\n 'Cannot change MAC when exposing in DHCP'\\n )\\n except IPAddress.DoesNotExist:\\n pass\",\n \"def _force_off(self):\\n self._interface.set('fw_wp_vref', self._fw_wp_vref)\\n self._interface.set('fw_wp_en', 'on')\\n self._interface.set('fw_wp', 'off')\",\n \"def cycle(self, include_ethernet=False):\\n if not self.healthy:\\n self.health_check()\\n self.off()\\n time.sleep(2) # Small delay before calling power_on\\n self.on()\",\n \"def turn_off(self, **kwargs):\\n _LOGGER.error(\\\"DALI TURN OFF\\\")\\n self._state = False\\n\\n url = self.urlx + '/toggle'\\n headers = {'x-ha-access': 'raspberry',\\n 'content-type': 'application/json'}\\n\\n response = get(url, headers=headers)\\n _LOGGER.error(response.text)\\n\\n json_data = json.loads(response.text)\\n _LOGGER.error(json_data)\\n\\n state = json_data['state']\\n\\n self._dimmer = 0\\n\\n self._state = state == 'on'\",\n \"def reset_old_config(self):\\n stringa = \\\"tc qdisc del dev \\\" + self.__interface + \\\" root\\\"\\n cmd = shlex.split(stringa)\\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n try:\\n o, e = proc.communicate(timeout=1)\\n except subprocess.TimeoutExpired:\\n proc.kill()\\n raise RuntimeWarning(\\\"Old configuration not eliminated\\\")\\n\\n if e.decode('ascii') != \\\"\\\":\\n raise RuntimeError(e.decode('ascii'))\\n return proc.returncode\",\n \"def _network_conf(conf_tuples=None, **kwargs):\\n nic = kwargs.get(\\\"network_profile\\\", None)\\n ret = []\\n nic_opts = kwargs.get(\\\"nic_opts\\\", {})\\n if nic_opts is None:\\n # coming from elsewhere\\n nic_opts = {}\\n if not conf_tuples:\\n conf_tuples = []\\n old = _get_veths(conf_tuples)\\n if not old:\\n old = {}\\n\\n # if we have a profile name, get the profile and load the network settings\\n # this will obviously by default look for a profile called \\\"eth0\\\"\\n # or by what is defined in nic_opts\\n # and complete each nic settings by sane defaults\\n if nic and isinstance(nic, ((str,), dict)):\\n nicp = get_network_profile(nic)\\n else:\\n nicp = {}\\n if DEFAULT_NIC not in nicp:\\n nicp[DEFAULT_NIC] = {}\\n\\n kwargs = copy.deepcopy(kwargs)\\n gateway = kwargs.pop(\\\"gateway\\\", None)\\n bridge = kwargs.get(\\\"bridge\\\", None)\\n if nic_opts:\\n for dev, args in nic_opts.items():\\n ethx = nicp.setdefault(dev, {})\\n try:\\n ethx = salt.utils.dictupdate.update(ethx, args)\\n except AttributeError:\\n raise SaltInvocationError(\\\"Invalid nic_opts configuration\\\")\\n ifs = [a for a in nicp]\\n ifs += [a for a in old if a not in nicp]\\n ifs.sort()\\n gateway_set = False\\n for dev in ifs:\\n args = nicp.get(dev, {})\\n opts = nic_opts.get(dev, {}) if nic_opts else {}\\n old_if = old.get(dev, {})\\n disable = opts.get(\\\"disable\\\", args.get(\\\"disable\\\", False))\\n if disable:\\n continue\\n mac = opts.get(\\n \\\"mac\\\", opts.get(\\\"hwaddr\\\", args.get(\\\"mac\\\", args.get(\\\"hwaddr\\\", \\\"\\\")))\\n )\\n type_ = opts.get(\\\"type\\\", args.get(\\\"type\\\", \\\"\\\"))\\n flags = opts.get(\\\"flags\\\", args.get(\\\"flags\\\", \\\"\\\"))\\n link = opts.get(\\\"link\\\", args.get(\\\"link\\\", \\\"\\\"))\\n ipv4 = opts.get(\\\"ipv4\\\", args.get(\\\"ipv4\\\", \\\"\\\"))\\n ipv6 = opts.get(\\\"ipv6\\\", args.get(\\\"ipv6\\\", \\\"\\\"))\\n infos = salt.utils.odict.OrderedDict(\\n [\\n (\\n \\\"lxc.network.type\\\",\\n {\\n \\\"test\\\": not type_,\\n \\\"value\\\": type_,\\n \\\"old\\\": old_if.get(\\\"lxc.network.type\\\"),\\n \\\"default\\\": \\\"veth\\\",\\n },\\n ),\\n (\\n \\\"lxc.network.name\\\",\\n {\\\"test\\\": False, \\\"value\\\": dev, \\\"old\\\": dev, \\\"default\\\": dev},\\n ),\\n (\\n \\\"lxc.network.flags\\\",\\n {\\n \\\"test\\\": not flags,\\n \\\"value\\\": flags,\\n \\\"old\\\": old_if.get(\\\"lxc.network.flags\\\"),\\n \\\"default\\\": \\\"up\\\",\\n },\\n ),\\n (\\n \\\"lxc.network.link\\\",\\n {\\n \\\"test\\\": not link,\\n \\\"value\\\": link,\\n \\\"old\\\": old_if.get(\\\"lxc.network.link\\\"),\\n \\\"default\\\": search_lxc_bridge(),\\n },\\n ),\\n (\\n \\\"lxc.network.hwaddr\\\",\\n {\\n \\\"test\\\": not mac,\\n \\\"value\\\": mac,\\n \\\"old\\\": old_if.get(\\\"lxc.network.hwaddr\\\"),\\n \\\"default\\\": salt.utils.network.gen_mac(),\\n },\\n ),\\n (\\n \\\"lxc.network.ipv4\\\",\\n {\\n \\\"test\\\": not ipv4,\\n \\\"value\\\": ipv4,\\n \\\"old\\\": old_if.get(\\\"lxc.network.ipv4\\\", \\\"\\\"),\\n \\\"default\\\": None,\\n },\\n ),\\n (\\n \\\"lxc.network.ipv6\\\",\\n {\\n \\\"test\\\": not ipv6,\\n \\\"value\\\": ipv6,\\n \\\"old\\\": old_if.get(\\\"lxc.network.ipv6\\\", \\\"\\\"),\\n \\\"default\\\": None,\\n },\\n ),\\n ]\\n )\\n # for each parameter, if not explicitly set, the\\n # config value present in the LXC configuration should\\n # take precedence over the profile configuration\\n for info in list(infos.keys()):\\n bundle = infos[info]\\n if bundle[\\\"test\\\"]:\\n if bundle[\\\"old\\\"]:\\n bundle[\\\"value\\\"] = bundle[\\\"old\\\"]\\n elif bundle[\\\"default\\\"]:\\n bundle[\\\"value\\\"] = bundle[\\\"default\\\"]\\n for info, data in infos.items():\\n if data[\\\"value\\\"]:\\n ret.append({info: data[\\\"value\\\"]})\\n for key, val in args.items():\\n if key == \\\"link\\\" and bridge:\\n val = bridge\\n val = opts.get(key, val)\\n if key in [\\n \\\"type\\\",\\n \\\"flags\\\",\\n \\\"name\\\",\\n \\\"gateway\\\",\\n \\\"mac\\\",\\n \\\"link\\\",\\n \\\"ipv4\\\",\\n \\\"ipv6\\\",\\n ]:\\n continue\\n ret.append({f\\\"lxc.network.{key}\\\": val})\\n # gateway (in automode) must be appended following network conf !\\n if not gateway:\\n gateway = args.get(\\\"gateway\\\", None)\\n if gateway is not None and not gateway_set:\\n ret.append({\\\"lxc.network.ipv4.gateway\\\": gateway})\\n # only one network gateway ;)\\n gateway_set = True\\n # normally, this won't happen\\n # set the gateway if specified even if we did\\n # not managed the network underlying\\n if gateway is not None and not gateway_set:\\n ret.append({\\\"lxc.network.ipv4.gateway\\\": gateway})\\n # only one network gateway ;)\\n gateway_set = True\\n\\n new = _get_veths(ret)\\n # verify that we did not loose the mac settings\\n for iface in [a for a in new]:\\n ndata = new[iface]\\n nmac = ndata.get(\\\"lxc.network.hwaddr\\\", \\\"\\\")\\n ntype = ndata.get(\\\"lxc.network.type\\\", \\\"\\\")\\n omac, otype = \\\"\\\", \\\"\\\"\\n if iface in old:\\n odata = old[iface]\\n omac = odata.get(\\\"lxc.network.hwaddr\\\", \\\"\\\")\\n otype = odata.get(\\\"lxc.network.type\\\", \\\"\\\")\\n # default for network type is setted here\\n # attention not to change the network type\\n # without a good and explicit reason to.\\n if otype and not ntype:\\n ntype = otype\\n if not ntype:\\n ntype = \\\"veth\\\"\\n new[iface][\\\"lxc.network.type\\\"] = ntype\\n if omac and not nmac:\\n new[iface][\\\"lxc.network.hwaddr\\\"] = omac\\n\\n ret = []\\n for val in new.values():\\n for row in val:\\n ret.append(salt.utils.odict.OrderedDict([(row, val[row])]))\\n # on old versions of lxc, still support the gateway auto mode\\n # if we didn't explicitly say no to\\n # (lxc.network.ipv4.gateway: auto)\\n if (\\n Version(version()) <= Version(\\\"1.0.7\\\")\\n and True not in [\\\"lxc.network.ipv4.gateway\\\" in a for a in ret]\\n and True in [\\\"lxc.network.ipv4\\\" in a for a in ret]\\n ):\\n ret.append({\\\"lxc.network.ipv4.gateway\\\": \\\"auto\\\"})\\n return ret\",\n \"def set_disabled_switch(self, disabled):\\n self.disabled = disabled\",\n \"def set_dhcp_conn(nic):\\n nic.EnableDHCP()\\n # After static DNS servers are specified to start using Dynamic Host\\n # Configuration Protocol (DHCP) instead of static DNS servers,\\n # you can call the method without supplying \\\"in\\\" parameters.\\n nic.SetDNSServerSearchOrder()\",\n \"async def async_turn_off(self, **kwargs):\\n try:\\n state_off = await self._api.set_relay_state(\\n self._dev_id, self._members, \\\"off\\\"\\n )\\n if state_off:\\n self._is_on = False\\n self.async_write_ha_state()\\n except PlugwiseException:\\n _LOGGER.error(\\\"Error while communicating to device\\\")\",\n \"def unconfigure_lldp_reinit(device): \\r\\n try:\\r\\n device.configure('no lldp reinit')\\r\\n except SubCommandFailure as e:\\r\\n raise SubCommandFailure(\\r\\n \\\"Could not unconfigure LLDP reinit\\\"\\r\\n \\\"Error: {error}\\\".format(error=e)\\r\\n )\",\n \"def set_configure_with_dhcp(self, bConfigureWithDhcp):\\n\\t\\tcall_sdk_function('PrlVmDevNet_SetConfigureWithDhcp', self.handle, bConfigureWithDhcp)\",\n \"async def async_turn_on(self):\\n path = \\\"/interface\\\"\\n param = \\\"default-name\\\"\\n if \\\"-\\\" in self._data[\\\"port-mac-address\\\"]:\\n param = \\\"name\\\"\\n value = self._data[param]\\n mod_param = \\\"disabled\\\"\\n mod_value = False\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n\\n if self._data[\\\"poe-out\\\"] == \\\"off\\\":\\n path = \\\"/interface/ethernet\\\"\\n self._ctrl.set_value(path, param, value, \\\"poe-out\\\", \\\"auto-on\\\")\\n\\n await self._ctrl.force_update()\",\n \"def down(config):\\n pass\",\n \"def maintenance_down():\\n install_apache_conf()\\n reboot()\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def dhcp_agent_network_add(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def emulate_whole_network_disaster(self, delay_before_recover=5 * 60,\\n wait_become_online=True):\\n nodes = [node for node in self.env.d_env.get_nodes()\\n if node.driver.node_active(node)]\\n\\n networks_interfaces = nodes[1].interfaces\\n\\n for interface in networks_interfaces:\\n interface.network.block()\\n\\n time.sleep(delay_before_recover)\\n\\n for interface in networks_interfaces:\\n interface.network.unblock()\\n\\n if wait_become_online:\\n self.fuel_web.wait_nodes_get_online_state(nodes[1:])\",\n \"def stop(self):\\n logging.debug(\\\"Network.stop entered:\\\" + str(self.id))\\n # print self.cloudnet\\n # res = cn.delete(self.cloudnet)\\n notify(\\\"Stopping network %s\\\" % self.name)\\n # if not self.cloudnet:\\n # \\n # self.cloudnet = cn.find(id=\\\"52a24319-f58d-4795-a3bd-c22d87bb65ae\\\")\\n if self.cloudnet:\\n res = self.cloudnet.delete()\\n else:\\n res = True\\n return res\",\n \"def test_set_enabled(self):\\n networktables_mock = unittest.mock.Mock()\\n table_mock = unittest.mock.Mock()\\n networktables_mock.getTable.return_value = table_mock\\n\\n network_instance = network.Network(networktables_mock, None, None)\\n\\n network_instance.set_enabled(True)\\n table_mock.putBoolean.assert_called_with(\\\"/enabled\\\", True)\\n\\n network_instance.set_enabled(False)\\n table_mock.putBoolean.assert_called_with(\\\"/enabled\\\", False)\",\n \"def reboot(self, instance, network_info):\\n LOG.debug(\\\"reboot\\\")\\n\\n bmm = db.bmm_get_by_instance_id(None, instance[\\\"id\\\"])\\n PowerManager(bmm[\\\"ipmi_ip\\\"]).reboot()\",\n \"def load_ethernet_aux(self, _type, _path, ethernet_data):\\n\\n support_ipv6 = True\\n dhcpv4curr = dhcpv4conf = oem_dhcpv4curr = oem_dhcpv4conf = dict()\\n dhcpv6curr = dhcpv6conf = oem_dhcpv6curr = oem_dhcpv6conf = dict()\\n errors = []\\n\\n ident_eth = False\\n if \\\"EthernetInterface\\\" in _type:\\n for curr_sel in self.rdmc.app.select(\\n _type.split(\\\".\\\")[0] + \\\".\\\",\\n (\\n self.rdmc.app.typepath.defs.hrefstring,\\n self.rdmc.app.typepath.defs.managerpath + \\\"*\\\",\\n ),\\n path_refresh=True,\\n ):\\n if curr_sel.path == _path:\\n ident_eth = True\\n break\\n # 'links/self/href' required when using iLO 4 (rest).\\n elif \\\"EthernetNetworkInterface\\\" in _type:\\n for curr_sel in self.rdmc.app.select(\\n _type.split(\\\".\\\")[0] + \\\".\\\",\\n (\\n \\\"links/self/\\\" + self.rdmc.app.typepath.defs.hrefstring,\\n self.rdmc.app.typepath.defs.managerpath + \\\"*\\\",\\n ),\\n path_refresh=True,\\n ):\\n if curr_sel.path == _path:\\n ident_eth = True\\n break\\n else:\\n raise Exception(\\\"Invalid type in management NIC load operation: '%s'\\\" % _type)\\n\\n if not ident_eth:\\n raise InvalidPathError(\\n \\\"Path: '%s' is invalid/not identified on this server.\\\\n\\\" % _path\\n )\\n\\n ident_name = curr_sel.dict.get(\\\"Name\\\")\\n ident_id = curr_sel.dict.get(\\\"Id\\\")\\n # ENABLING ETHERNET INTERFACE SECTION\\n try:\\n # Enable the Interface if called for and not already enabled\\n if ethernet_data.get(\\\"InterfaceEnabled\\\") and not curr_sel.dict.get(\\n \\\"InterfaceEnabled\\\"\\n ):\\n self.rdmc.app.patch_handler(\\n _path, {\\\"InterfaceEnabled\\\": True}, silent=True\\n )\\n self.rdmc.ui.printer(\\\"NIC Interface Enabled.\\\\n\\\")\\n # Disable the Interface if called for and not disabled already\\n # No need to do anything else, just return\\n elif not ethernet_data.get(\\\"InterfaceEnabled\\\") and not curr_sel.dict.get(\\n \\\"InterfaceEnabled\\\"\\n ):\\n self.rdmc.app.patch_handler(\\n _path, {\\\"InterfaceEnabled\\\": False}, silent=True\\n )\\n self.rdmc.ui.warn(\\n \\\"NIC Interface Disabled. All additional configurations \\\" \\\"omitted.\\\"\\n )\\n return\\n except (KeyError, NameError, TypeError, AttributeError):\\n # check OEM for NICEnabled instead\\n if (\\n not curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"NICEnabled\\\"]\\n and ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"NICEnabled\\\"]\\n ):\\n self.rdmc.app.patch_handler(\\n _path,\\n {\\\"Oem\\\": {self.rdmc.app.typepath.defs.oemhp: {\\\"NICEnabled\\\": True}}},\\n silent=True,\\n )\\n self.rdmc.ui.printer(\\\"NIC Interface Enabled.\\\\n\\\")\\n elif (\\n not curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"NICEnabled\\\"]\\n and not ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"NICEnabled\\\"\\n ]\\n ):\\n self.rdmc.app.patch_handler(\\n _path,\\n {\\\"Oem\\\": {self.rdmc.app.typepath.defs.oemhp: {\\\"NICEnabled\\\": False}}},\\n silent=True,\\n )\\n self.rdmc.ui.printer(\\\"NIC Interface Disabled.\\\\n\\\")\\n return\\n # except IloResponseError should just be raised and captured by decorator. No point in\\n # performing any other operations if the interface can not be set.\\n\\n # END ENABLING ETHERNET INTEFACE SECTION\\n # ---------------------------------------\\n # DETERMINE DHCPv4 and DHCPv6 States and associated flags\\n\\n if \\\"NICSupportsIPv6\\\" in list(\\n curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp].keys()\\n ):\\n support_ipv6 = curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"NICSupportsIPv6\\\"\\n ]\\n\\n # obtain DHCPv4 Config and OEM\\n try:\\n if \\\"DHCPv4\\\" in list(curr_sel.dict.keys()) and \\\"DHCPv4\\\" in list(\\n ethernet_data.keys()\\n ):\\n dhcpv4curr = copy.deepcopy(curr_sel.dict[\\\"DHCPv4\\\"])\\n dhcpv4conf = copy.deepcopy(ethernet_data[\\\"DHCPv4\\\"])\\n except (KeyError, NameError, TypeError, AttributeError):\\n errors.append(\\\"Unable to find Redfish DHCPv4 Settings.\\\\n\\\")\\n finally:\\n try:\\n oem_dhcpv4curr = copy.deepcopy(\\n curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv4\\\"]\\n )\\n oem_dhcpv4conf = copy.deepcopy(\\n ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv4\\\"]\\n )\\n ipv4curr = copy.deepcopy(\\n curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"IPv4\\\"]\\n )\\n ipv4conf = copy.deepcopy(\\n ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"IPv4\\\"]\\n )\\n except (KeyError, NameError):\\n errors.append(\\\"Unable to find OEM Keys for DHCPv4 or IPv4\\\")\\n\\n try:\\n if support_ipv6:\\n if \\\"DHCPv6\\\" in list(curr_sel.dict.keys()) and \\\"DHCPv6\\\" in list(\\n ethernet_data.keys()\\n ):\\n dhcpv6curr = copy.deepcopy(curr_sel.dict[\\\"DHCPv6\\\"])\\n dhcpv6conf = copy.deepcopy(ethernet_data[\\\"DHCPv6\\\"])\\n else:\\n self.rdmc.ui.warn(\\\"NIC Does not support IPv6.\\\")\\n except (KeyError, NameError, TypeError, AttributeError):\\n errors.append(\\\"Unable to find Redfish DHCPv6 Settings.\\\\n\\\")\\n finally:\\n try:\\n oem_dhcpv4curr = copy.deepcopy(\\n curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv6\\\"]\\n )\\n oem_dhcpv6conf = copy.deepcopy(\\n ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv6\\\"]\\n )\\n ipv6curr = copy.deepcopy(\\n curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"IPv6\\\"]\\n )\\n ipv6conf = copy.deepcopy(\\n ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"IPv6\\\"]\\n )\\n except (KeyError, NameError):\\n errors.append(\\\"Unable to find OEM Keys for DHCPv6 or IPv6\\\")\\n\\n try:\\n # if DHCP Enable request but not currently enabled\\n if (\\n dhcpv4conf.get(\\\"DHCPEnabled\\\")\\n and not curr_sel.dict[\\\"DHCPv4\\\"][\\\"DHCPEnabled\\\"]\\n ):\\n self.rdmc.app.patch_handler(\\n _path, {\\\"DHCPv4\\\": {\\\"DHCPEnabled\\\": True}}, silent=True\\n )\\n self.rdmc.ui.printer(\\\"DHCP Enabled.\\\\n\\\")\\n # if DHCP Disable request but currently enabled\\n elif not dhcpv4conf[\\\"DHCPEnabled\\\"] and curr_sel.dict[\\\"DHCPv4\\\"][\\\"DHCPEnabled\\\"]:\\n self.rdmc.app.patch_handler(\\n _path, {\\\"DHCPv4\\\": {\\\"DHCPEnabled\\\": False}}, silent=True\\n )\\n dhcpv4conf[\\\"UseDNSServers\\\"] = False\\n dhcpv4conf[\\\"UseNTPServers\\\"] = False\\n dhcpv4conf[\\\"UseGateway\\\"] = False\\n dhcpv4conf[\\\"UseDomainName\\\"] = False\\n self.rdmc.ui.printer(\\\"DHCP Disabled.\\\\n\\\")\\n except (KeyError, NameError, TypeError, AttributeError):\\n # try with OEM\\n try:\\n if (\\n oem_dhcpv4conf.get(\\\"Enabled\\\")\\n and not curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"DHCPv4\\\"\\n ][\\\"Enabled\\\"]\\n ):\\n self.rdmc.app.patch_handler(\\n _path,\\n {\\n \\\"Oem\\\": {\\n self.rdmc.app.typepath.defs.oemhp: {\\n \\\"DHCPv4\\\": {\\\"DHCPEnabled\\\": True}\\n }\\n }\\n },\\n silent=True,\\n )\\n self.rdmc.ui.printer(\\\"DHCP Enabled.\\\\n\\\")\\n if \\\"IPv4Addresses\\\" in ethernet_data:\\n del ethernet_data[\\\"IPv4Addresses\\\"]\\n elif (\\n not oem_dhcpv4conf.get(\\\"Enabled\\\")\\n and curr_sel.dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv4\\\"][\\n \\\"Enabled\\\"\\n ]\\n ):\\n oem_dhcpv4conf[\\\"UseDNSServers\\\"] = False\\n oem_dhcpv4conf[\\\"UseNTPServers\\\"] = False\\n oem_dhcpv4conf[\\\"UseGateway\\\"] = False\\n oem_dhcpv4conf[\\\"UseDomainName\\\"] = False\\n self.rdmc.ui.printer(\\\"DHCP Disabled.\\\\n\\\")\\n except (KeyError, NameError) as exp:\\n errors.append(\\n \\\"Failure in parsing or removing data in OEM DHCPv4: %s.\\\\n\\\" % exp\\n )\\n\\n try:\\n # if the ClientIDType is custom and we are missing the ClientID then this property can\\n # not be set.\\n if \\\"ClientIdType\\\" in list(dhcpv4conf.keys()):\\n if dhcpv4conf[\\\"ClientIdType\\\"] == \\\"Custom\\\" and \\\"ClientID\\\" not in list(\\n dhcpv4conf.keys()\\n ):\\n del ethernet_data[\\\"DHCPv4\\\"][\\\"ClientIdType\\\"]\\n elif \\\"ClientIdType\\\" in list(oem_dhcpv4conf.keys()):\\n if oem_dhcpv4conf[\\\"ClientIdType\\\"] == \\\"Custom\\\" and \\\"ClientID\\\" not in list(\\n oem_dhcpv4conf.keys()\\n ):\\n del ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\\"DHCPv4\\\"][\\n \\\"ClientIdType\\\"\\n ]\\n except (KeyError, NameError, TypeError, AttributeError):\\n try:\\n if \\\"ClientIdType\\\" in list(oem_dhcpv4conf.keys()):\\n if oem_dhcpv4conf[\\n \\\"ClientIdType\\\"\\n ] == \\\"Custom\\\" and \\\"ClientID\\\" not in list(oem_dhcpv4conf.keys()):\\n del ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"DHCPv4\\\"\\n ][\\\"ClientIdType\\\"]\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n # special considerations go here for things that need to stay despite diffdict\\n # EX: IPv4 addresses (aka bug). Changing only one property within the\\n # IPv4StaticAddresses or IPv4Addresses causes an issue during load. Must include IP,\\n # subnet mask and gateway (they can not be patched individually).\\n # spec_dict = {'Oem': {self.rdmc.app.typepath.defs.oemhp: {}}}\\n spec_dict = dict()\\n if \\\"IPv4Addresses\\\" in ethernet_data:\\n spec_dict[\\\"IPv4Addresses\\\"] = copy.deepcopy(ethernet_data[\\\"IPv4Addresses\\\"])\\n try:\\n if \\\"IPv4Addresses\\\" in ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp]:\\n spec_dict[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"IPv4Addresses\\\"\\n ] = copy.deepcopy(\\n ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"IPv4StaticAddresses\\\"\\n ]\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n\\n # diff and overwrite the original payload\\n ethernet_data = diffdict(ethernet_data, curr_sel.dict)\\n ethernet_data.update(spec_dict)\\n\\n # verify dependencies on those flags which are to be applied are eliminated\\n try:\\n # delete Domain name and FQDN if UseDomainName for DHCPv4 or DHCPv6\\n # is present. can wait to apply at the end\\n if dhcpv4conf.get(\\\"UseDomainName\\\"): # or dhcpv6conf['UseDomainName']:\\n if (\\n \\\"DomainName\\\"\\n in ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp]\\n ):\\n del ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"DomainName\\\"\\n ]\\n if \\\"FQDN\\\" in ethernet_data:\\n del ethernet_data[\\\"FQDN\\\"]\\n except (KeyError, NameError, TypeError, AttributeError):\\n # try again with OEM\\n try:\\n if oem_dhcpv4conf.get(\\\"UseDomainName\\\") or oem_dhcpv6conf.get(\\n \\\"UseDomainName\\\"\\n ):\\n if (\\n \\\"DomainName\\\"\\n in ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp]\\n ):\\n del ethernet_data[\\\"Oem\\\"][self.rdmc.app.typepath.defs.oemhp][\\n \\\"DomainName\\\"\\n ]\\n if \\\"FQDN\\\" in ethernet_data:\\n del ethernet_data[\\\"FQDN\\\"]\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n try:\\n # delete DHCP4 DNSServers from IPV4 dict if UseDNSServers Enabled\\n # can wait to apply at the end\\n if dhcpv4conf.get(\\\"UseDNSServers\\\"): # and ethernet_data.get('NameServers'):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"NameServers\\\"]\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n finally:\\n try:\\n if oem_dhcpv4conf.get(\\\"UseDNSServers\\\"):\\n # del_sections('DNSServers', ethernet_data)\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"DNSServers\\\"]\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n try:\\n if dhcpv4conf.get(\\\"UseWINSServers\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"WINServers\\\"]\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n finally:\\n try:\\n if oem_dhcpv4conf.get(\\\"UseWINSServers\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data,\\n remove_list=[\\\"WINServers\\\", \\\"WINSRegistration\\\"],\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n try:\\n if dhcpv4conf.get(\\\"UseStaticRoutes\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"StaticRoutes\\\"]\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n finally:\\n try:\\n if oem_dhcpv4conf.get(\\\"UseStaticRoutes\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"StaticRoutes\\\"]\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n try:\\n # if using DHCPv4, remove static addresses\\n if dhcpv4conf.get(\\\"DHCPEnabled\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data,\\n remove_list=[\\\"IPv4Addresses\\\", \\\"IPv4StaticAddresses\\\"],\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n finally:\\n try:\\n if oem_dhcpv4conf.get(\\\"Enabled\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data,\\n remove_list=[\\\"IPv4Addresses\\\", \\\"IPv4StaticAddresses\\\"],\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n try:\\n # if not using DHCPv6, remove static addresses from payload\\n if dhcpv6conf.get(\\\"OperatingMode\\\") == \\\"Disabled\\\":\\n json_traversal_delete_empty(\\n data=ethernet_data,\\n remove_list=[\\\"IPv6Addresses\\\", \\\"IPv6StaticAddresses\\\"],\\n )\\n except (KeyError, NameError, TypeError, AttributeError):\\n pass\\n finally:\\n try:\\n if not oem_dhcpv6conf.get(\\\"StatefulModeEnabled\\\"):\\n json_traversal_delete_empty(\\n data=ethernet_data,\\n remove_list=[\\\"IPv6Addresses\\\", \\\"IPv6StaticAddresses\\\"],\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n flags = dict()\\n if dhcpv4conf:\\n flags[\\\"DHCPv4\\\"] = dhcpv4conf\\n if dhcpv6conf:\\n flags[\\\"DHCPv6\\\"] = dhcpv6conf\\n if oem_dhcpv4conf:\\n flags[\\\"Oem\\\"] = {self.rdmc.app.typepath.defs.oemhp: {\\\"DHCPv4\\\": oem_dhcpv4conf}}\\n if oem_dhcpv6conf:\\n flags[\\\"Oem\\\"] = {self.rdmc.app.typepath.defs.oemhp: {\\\"DHCPv6\\\": oem_dhcpv6conf}}\\n\\n # verify dependencies on those flags which are to be applied are eliminated\\n\\n try:\\n self.rdmc.app.patch_handler(_path, flags, silent=True)\\n except IloResponseError as excp:\\n errors.append(\\n \\\"iLO Responded with the following errors setting DHCP: %s.\\\\n\\\" % excp\\n )\\n\\n try:\\n if \\\"AutoNeg\\\" not in list(ethernet_data.keys()):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"FullDuplex\\\", \\\"SpeedMbps\\\"]\\n )\\n\\n # if Full Duplex exists, check if FullDuplexing enabled. If so,\\n # remove Speed setting.\\n elif \\\"FullDuplex\\\" in list(ethernet_data.keys()):\\n json_traversal_delete_empty(\\n data=ethernet_data, remove_list=[\\\"FullDuplex\\\", \\\"SpeedMbps\\\"]\\n )\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n try:\\n if \\\"FrameSize\\\" in list(ethernet_data.keys()):\\n json_traversal_delete_empty(data=ethernet_data, remove_list=[\\\"FrameSize\\\"])\\n except (KeyError, NameError) as exp:\\n errors.append(\\\"Unable to remove property %s.\\\\n\\\" % exp)\\n\\n self.patch_eth(_path, ethernet_data, errors)\\n\\n if errors and \\\"Virtual\\\" not in ident_name:\\n raise RdmcError(\\n \\\"Ethernet configuration errors were found collectively on adapter: \\\"\\n \\\"'%s, %s'\\\\ntype: %s\\\\nerrors: %s\\\" % (ident_name, ident_id, _type, errors)\\n )\",\n \"def update_networks(self, agent, force_hard=False):\\n\\n if self.update_type == \\\"soft\\\" and not force_hard:\\n self._soft_update(agent.actor, agent.actor_target)\\n self._soft_update(agent.critic, agent.critic_target)\\n elif self.t_step % self.C == 0 or force_hard:\\n self._hard_update(agent.actor, agent.actor_target)\\n self._hard_update(agent.critic, agent.critic_target)\",\n \"def on_the_network_page_click_on_setting_on_the_global_configuration_card(driver):\\n assert wait_on_element(driver, 7, '//h1[contains(.,\\\"Network\\\")]')\\n assert wait_on_element(driver, 5, '//button[@ix-auto=\\\"button__globalSettings\\\"]', 'clickable')\\n driver.find_element_by_xpath('//button[@ix-auto=\\\"button__globalSettings\\\"]').click()\",\n \"def _post_task_update_advertise_address():\\n default_network_interface = None\\n\\n with open(KUBE_APISERVER_CONFIG) as f:\\n lines = f.read()\\n m = re.search(REGEXPR_ADVERTISE_ADDRESS, lines)\\n if m:\\n default_network_interface = m.group(1)\\n LOG.debug(' default_network_interface = %s', default_network_interface)\\n\\n if advertise_address and default_network_interface \\\\\\n and advertise_address != default_network_interface:\\n cmd = [\\\"sed\\\", \\\"-i\\\", \\\"/oidc-issuer-url/! s/{}/{}/g\\\".format(default_network_interface, advertise_address),\\n KUBE_APISERVER_CONFIG]\\n _ = _exec_cmd(cmd)\",\n \"def setOff(self, command):\\r\\n self.setDriver('ST', 0)\",\n \"def reconfiguration_scalability(self):\\n\\n self.check_run('reconfiguration_scalability')\\n self.show_step(1, initialize=True)\\n self.env.revert_snapshot(\\\"reconfigure_nova_ephemeral_disk\\\")\\n\\n self.show_step(2)\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n config = utils.get_config_template('nova_disk')\\n structured_config_nova = get_structured_config_dict(config)\\n config = utils.get_config_template('keystone')\\n structured_config_keystone = get_structured_config_dict(config)\\n self.fuel_web.client.upload_configuration(config,\\n cluster_id,\\n role='controller')\\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['controller'])\\n\\n self.show_step(3)\\n task = self.fuel_web.client.apply_configuration(cluster_id,\\n role='controller')\\n self.fuel_web.assert_task_success(task, timeout=900, interval=5)\\n\\n self.show_step(4)\\n self.check_config_on_remote(controllers, structured_config_keystone)\\n\\n self.show_step(5)\\n os_conn = os_actions.OpenStackActions(\\n self.fuel_web.get_public_vip(cluster_id))\\n time_expiration = config[\\n 'keystone_config']['token/expiration']['value']\\n self.check_token_expiration(os_conn, time_expiration)\\n\\n self.show_step(6)\\n bs_nodes = [x for x in self.env.d_env.get_nodes()\\n if x.name == 'slave-05' or x.name == 'slave-06']\\n self.env.bootstrap_nodes(bs_nodes)\\n self.fuel_web.update_nodes(\\n cluster_id,\\n {'slave-05': ['compute', 'cinder']})\\n self.fuel_web.update_nodes(\\n cluster_id,\\n {'slave-06': ['controller']})\\n\\n self.show_step(7)\\n self.fuel_web.verify_network(cluster_id)\\n\\n self.show_step(8)\\n self.fuel_web.deploy_cluster_wait(cluster_id)\\n\\n self.show_step(9)\\n self.fuel_web.run_ostf(cluster_id=cluster_id)\\n\\n self.show_step(10)\\n controllers = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['controller'])\\n computes = self.fuel_web.get_nailgun_cluster_nodes_by_roles(\\n cluster_id, ['compute'])\\n target_controller = [x for x in controllers\\n if 'slave-06' in x['name']]\\n target_compute = [x for x in computes\\n if 'slave-05' in x['name']]\\n self.check_config_on_remote(target_controller,\\n structured_config_keystone)\\n\\n self.show_step(11)\\n self.check_config_on_remote(target_compute, structured_config_nova)\\n\\n self.show_step(12)\\n self.show_step(13)\\n self.show_step(14)\\n self.show_step(15)\\n self.show_step(16)\\n\\n os_conn = os_actions.OpenStackActions(\\n self.fuel_web.get_public_vip(cluster_id))\\n hypervisor_name = target_compute[0]['fqdn']\\n self.check_nova_ephemeral_disk(os_conn, cluster_id,\\n hypervisor_name=hypervisor_name)\\n\\n self.show_step(17)\\n self.check_token_expiration(os_conn, time_expiration)\\n\\n self.env.make_snapshot(\\\"reconfiguration_scalability\\\", is_make=True)\",\n \"def unconfigure_stackwise_virtual_dual_active_interfaces(device, dad_links):\\n # build a list of commands to send\\n command_list = []\\n output = ''\\n for interface in dad_links:\\n command_list.append(f'interface {interface}')\\n command_list.append(f'no stackwise-virtual dual-active-detection')\\n try:\\n output = device.configure(command_list)\\n except SubCommandFailure:\\n raise SubCommandFailure('Failed to unconfigure stackwise-virtual dual-active-detection interfaces')\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list 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event."},"document":{"kind":"string","value":"def network_create_end(self, payload):\n network_id = payload['network']['id']\n self.enable_dhcp_helper(network_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def visit_graph_end(self, network: Network):\n pass","def onMessageEnd(self):","def network_create_event(self, network_info):\n net = network_info['network']\n net_id = net['id']\n net_name = net.get('name')\n network_db_elem = self.get_network(net_id)\n # Check if the source of network creation is FW and if yes, skip\n # this event.\n # Check if there's a way to read the DB from service class\n # TODO(padkrish)\n if self.fw_api.is_network_source_fw(network_db_elem, net_name):\n LOG.info(_LI(\"Service network %s, returning\"), net_name)\n return\n self.network[net_id] = {}\n self.network[net_id].update(net)\n\n net_name = net.get('name')\n tenant_id = net.get('tenant_id')\n\n # Extract segmentation_id from the network name\n net_ext_name = self.cfg.dcnm.dcnm_net_ext\n nobj = re.search(net_ext_name, net_name)\n try:\n seg_id = int((net_name[nobj.start(0) + len(net_ext_name) - 1:]\n if nobj else None))\n except (IndexError, TypeError, ValueError):\n seg_id = None\n\n # Check if network is already created.\n query_net = self.get_network_by_segid(seg_id) if seg_id else None\n if query_net:\n # The network is already created no need to process the event.\n if query_net.source.lower() == 'dcnm':\n # DCNM created the network. Only update network id in database.\n prev_id = query_net.network_id\n params = dict(columns=dict(network_id=net_id))\n self.update_network(prev_id, **params)\n\n # Update the network cache.\n prev_info = self.network.pop(prev_id)\n prev_info['id'] = net_id\n self.network[net_id] = prev_info\n\n # Update the network name. After extracting the segmentation_id\n # no need to keep it in the name. Removing it and update\n # the network.\n updated_net_name = (\n net_name[:nobj.start(0) + len(net_ext_name) - 1])\n try:\n body = {'network': {'name': updated_net_name, }}\n dcnm_net = self.neutronclient.update_network(\n net_id, body=body).get('network')\n LOG.debug('Updated network %(network)s', dcnm_net)\n except Exception as exc:\n LOG.exception(_LE('Failed to update network '\n '%(network)s. Reason %(err)s.'),\n {'network': updated_net_name,\n 'err': str(exc)})\n return\n\n LOG.info(_LI('network_create_event: network %(name)s was created '\n 'by %(source)s. Ignoring processing the event.'),\n {'name': net_name, 'source': 'dcnm'})\n return\n\n # Check if project (i.e. tenant) exist.\n tenant_name = self.get_project_name(tenant_id)\n if not tenant_name:\n LOG.error(_LE('Failed to create network %(name)s. Project '\n '%(tenant_id)s does not exist.'),\n {'name': net_name, 'tenant_id': tenant_id})\n return\n\n pseg_id = self.network[net_id].get('provider:segmentation_id')\n seg_id = self._get_segmentation_id(net_id, pseg_id, 'openstack')\n self.network[net_id]['segmentation_id'] = seg_id\n try:\n cfgp, fwd_mod = self.dcnm_client.get_config_profile_for_network(\n net.get('name'))\n self.network[net_id]['config_profile'] = cfgp\n self.network[net_id]['fwd_mod'] = fwd_mod\n self.add_network_db(net_id, self.network[net_id],\n 'openstack',\n constants.RESULT_SUCCESS)\n LOG.debug('network_create_event: network=%s', self.network)\n except dexc.DfaClientRequestFailed:\n # Fail to get config profile from DCNM.\n # Save the network info with failure result and send the request\n # to DCNM later.\n self.add_network_db(net_id, self.network[net_id], 'openstack',\n constants.CREATE_FAIL)\n LOG.error(_LE('Failed to create network=%s.'), self.network)","def notify_end(self, status, objective):\n pass # pragma: no cover","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def endMessage(self):","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def notify_finish_event(self):\n self.notify(self._finish_event_type())","def on_connection_closed(self):","def dcnm_network_create_event(self, network_info):\n\n # 1. Add network info to database before sending request to\n # neutron to create the network.\n # Check if network is already created.\n pre_seg_id = network_info.get('segmentation_id')\n pre_project_name = network_info.get('project_name')\n pre_partition_name = network_info.get('partition_name')\n if not pre_seg_id or not pre_partition_name or not pre_project_name:\n LOG.error(_LE('Invalid network event: %s'), network_info)\n return\n\n # Check if partition name is the one that openstack created.\n if pre_partition_name != self.cfg.dcnm.default_partition_name:\n LOG.error(_LE('Failed to create network. Partition %(part)s is '\n 'not %(os_part)s which is created by openstack.'),\n {'part': pre_partition_name,\n 'os_part': self.cfg.dcnm.default_partition_name})\n return\n\n query_net = self.get_network_by_segid(pre_seg_id)\n if query_net:\n # The network is already created no need to process the event.\n LOG.info(_LI('dcnm_network_create_event: network %(name)s was '\n 'created. Ignoring processing the event.'),\n {'name': query_net.name})\n return\n\n dcnm_net_info = self.dcnm_client.get_network(pre_project_name,\n pre_seg_id)\n if not dcnm_net_info:\n LOG.info(_LI('No network details for %(org)s and %(segid)s'),\n {'org': pre_project_name, 'segid': pre_seg_id})\n return\n\n net_id = utils.get_uuid()\n pseg_id = dcnm_net_info.get('segmentId')\n seg_id = self._get_segmentation_id(net_id, pseg_id, 'DCNM')\n cfgp = dcnm_net_info.get('profileName')\n net_name = dcnm_net_info.get('networkName')\n fwd_mod = self.dcnm_client.config_profile_fwding_mode_get(cfgp)\n tenant_name = dcnm_net_info.get('organizationName')\n tenant_id = self.get_project_id(tenant_name)\n\n # Get the subnet details.\n subnet = dcnm_net_info.get('dhcpScope')\n if not subnet:\n # The dhcpScope is not provided. Calculating the cidr based on\n # gateway ip and netmask.\n gw_addr = dcnm_net_info.get('gateway')\n net_mask = dcnm_net_info.get('netmaskLength')\n cidr = utils.make_cidr(gw_addr, net_mask)\n if not cidr:\n LOG.error(_LE('Failed to create network: '\n 'cidr is None for %(gw)s %(mask)s'),\n {'gw': gw_addr, 'mask': net_mask})\n return\n subnet = dict(gateway=gw_addr, subnet=cidr)\n\n # Check if parameters are provided.\n if not (net_name and tenant_id and seg_id and subnet):\n LOG.error(_LE('Invalid value: network %(name)s tenant_id '\n '%(tenant_id)s segmentation_id %(seg_id)s '\n 'subnet %(subnet)s.'), {'name': net_name,\n 'tenant_id': tenant_id,\n 'seg_id': seg_id,\n 'subnet': subnet})\n return\n\n # Update network cache and add the network to the database.\n net_ext_name = self.cfg.dcnm.dcnm_net_ext\n self.network[net_id] = dict(segmentation_id=seg_id,\n config_profile=cfgp,\n fwd_mod=fwd_mod,\n tenant_id=tenant_id,\n name=net_name + net_ext_name,\n id=net_id,\n source='DCNM')\n self.add_network_db(net_id, self.network[net_id], 'DCNM',\n constants.RESULT_SUCCESS)\n\n # 2. Send network create request to neutron\n try:\n # With create_network (called below), the same request comes as\n # notification and it will be processed in the\n # create_network_event. The request should not be processed as it\n # is already processed here.\n # The only way to decide whether it is for a new network or not is\n # the segmentation_id (DCNM does not have uuid for network) which\n # is unique. For that reason it is needed to send segmentation_id\n # when creating network in openstack.\n # Moreover, we are using network_type=local and for that reason\n # provider:segmentation_id cannot be added as parameter when\n # creating network. One solution is to embed segmentation_id in the\n # network name. Then, when processing the notification, if the\n # request is from DCNM, the segmentation_id will be extracted from\n # network name. With that create_network_event can decide to\n # process or deny an event.\n updated_net_name = net_name + net_ext_name + str(seg_id)\n body = {'network': {'name': updated_net_name,\n 'tenant_id': tenant_id,\n 'admin_state_up': True}}\n dcnm_net = self.neutronclient.create_network(\n body=body).get('network')\n net_id = dcnm_net.get('id')\n\n except Exception as exc:\n # Failed to create network, do clean up.\n # Remove the entry from database and local cache.\n del self.network[net_id]\n self.delete_network_db(net_id)\n LOG.exception(_LE('dcnm_network_create_event: Failed to create '\n '%(network)s. Reason %(err)s.'),\n {'network': body, 'err': str(exc)})\n return\n\n LOG.debug('dcnm_network_create_event: Created network %(network)s', (\n body))\n\n # 3. Send subnet create request to neutron.\n pool = subnet.get('ipRange')\n allocation_pools = []\n if pool:\n allocation_pools = [{'start': s, 'end': e} for s, e in\n [p.split('-') for p in pool.split(',')]]\n\n try:\n body = {'subnet': {'cidr': subnet.get('subnet'),\n 'gateway_ip': subnet.get('gateway'),\n 'ip_version': 4,\n 'network_id': net_id,\n 'tenant_id': tenant_id,\n 'enable_dhcp': not self.dcnm_dhcp,\n 'allocation_pools': allocation_pools, }}\n if not self.dcnm_dhcp:\n body.get('subnet').pop('allocation_pools')\n # Send requenst to create subnet in neutron.\n LOG.debug('Creating subnet %(subnet)s for DCNM request.', body)\n dcnm_subnet = self.neutronclient.create_subnet(\n body=body).get('subnet')\n subnet_id = dcnm_subnet.get('id')\n # Update subnet cache.\n self.subnet[subnet_id] = {}\n self.subnet[subnet_id].update(body.get('subnet'))\n except Exception as exc:\n # Failed to create network, do clean up if necessary.\n LOG.exception(_LE('Failed to create subnet %(subnet)s for DCNM '\n 'request. Error %(err)s'),\n {'subnet': body['subnet'], 'err': str(exc)})\n\n LOG.debug('dcnm_network_create_event: Created subnet %(subnet)s', (\n body))","def create_network_postcommit(self, context):\n if self.rpc_handler is None:\n return\n network = self._get_network_info(context._network)\n for _, _network in network.items():\n network_type = _network.get('network_type', '')\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\n return\n if network is not None:\n try:\n self.rpc_handler.create_network(network)\n except:\n pass","def handle_finish(self, dataset):\n\n logger.info('End of scheduling block message received.')","def registration_ended(self):\n pass","def on_close(self):\n # remove callback so it doesn't get called in the future\n self._notifier.remove_callback(self.process_notification)","def visit_initializer_end(self, network: Network):\n pass","def on_end(self, ctx):\n pass","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","def registration_ended(self):\n self._write_image('end')","def END(self):\n log.debug(\"All packets successfully transmitted!\")","async def end_object(self):","def on_chat_end(self, request, trigger_context):\n raise NotImplementedError","def process_IN_CREATE(self, event):","def tcp_end(self, flow: mitmproxy.tcp.TCPFlow):","def process_create_q(self):\n while not self.create_req.empty():\n [new_name, new_type, mvt] = self.create_req.get()\n self.create_new_peer(new_type, new_name, mvt)\n self.create_req.task_done()","async def on_disconnect(self) -> None:","def finished(self, reply):\n pass","def on_finish(self):\n pass","def on_leave(self):\n\n self.check_connection.cancel()","def _handle_service_end(node: int, system: System, params: Params,\n records: Records):\n time_now = system.time\n server = system.get_server(node)\n queue = system.get_queue(node)\n\n # Extract the packet from the server and mark it as served:\n packet = server.pop()\n packet.service_finished[node] = time_now\n packet.was_served[node] = True\n\n # If this node is the target, then packet is delivered.\n # Otherwise, pass it to the next node.\n if packet.target == node:\n packet.was_delivered = True\n packet.delivery_time = time_now\n elif node < params.num_stations - 1:\n _process_packet(node + 1, packet, system, params, records)\n else:\n raise RuntimeError(\"shouldn't arrive here: packet reached network\"\n \"end, and this is not its destination!!\")\n\n # Start serving next packet, if exists:\n if (packet := queue.pop()) is not None:\n server.serve(packet)\n packet.service_started[node] = time_now\n system.schedule(Event.SERVICE_END, params.services[node](), node)\n\n # Record new system size:\n records.get_system_size(node).add(time_now, system.get_size(node))","def onfinish():","def end(self):\n ...","def onEnd(self, agent):\n\n pass","def RequestHandler_finish(self):\n if self.request._oboe_finish_ev and self.request._oboe_ctx and self.request._oboe_ctx.is_valid():\n ev = self.request._oboe_finish_ev\n ctx = self.request._oboe_ctx\n if hasattr(self, 'get_status'): # recent Tornado\n ev.add_info(\"Status\", self.get_status())\n elif hasattr(self, '_status_code'): # older Tornado\n ev.add_info(\"Status\", self._status_code)\n\n ev.add_edge(oboe.Context.get_default())\n ctx.report(ev)\n\n # clear the stored oboe event/metadata from the request object\n self.request._oboe_ctx = None\n self.request._oboe_finish_ev = None","async def on_end(self, game_result: Result):","def _onEnd(self, name, completed):\n logging.debug(\"onEnd...\")","async def notify_disconnected(self):\n payload = {\n \"uuid\": self.uuid,\n \"addr\": self.addr,\n \"port\": self.port,\n }\n msg = {\n \"event\": \"connection/disconnected\",\n \"secret\": WS_SECRET,\n \"payload\": payload,\n }\n\n asyncio.create_task(messages_to_game.put(Message(\"IO\", message=json.dumps(msg, sort_keys=True, indent=4))))","def connectionLost(self, reason):\n self.finished.callback(self.display)","def on_terminate(self):\n pass","def end(self):\n pass","def end(self):\n pass","def end(self):\n pass","def handle_record_end():\n LOG.info(\"End Recording...\")\n context = {'client_name': 'mycroft_listener',\n 'source': 'audio',\n 'destination': [\"skills\"]}\n bus.emit(Message('recognizer_loop:record_end', context=context))","def keepAliveReceived(self):","def on_event_finished(self, event):","def end(self, won, reason):\n pass\n # replace with your end logic","def on_connection_close(self):\n self._stat.http_stream_close += 1\n self._logger.info(\"HTTP EventStream %s closed\", self)\n self.finish()\n asyncio.async(self._nc_handler.close(), loop=self._asyncio_loop)","def end(self):\n winners = mafia.str_player_list(self.game.winners())\n logging.info(\"Game over! Winners: %s\" % winners)\n\n subject = \"%s: The End\" % self.name\n body = \"Game over!\\n\\nCongratulations to %s for a well \" \\\n \"(or poorly; I can't tell) played game!\" % winners\n self.send_message(mafia.events.PUBLIC, subject, body)","def on_disconnect(self, raw_msg, server, port, **kwargs):","def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def on_connection_close(self, *args, **kwargs):\n print \"CONNECTION CLOSED!!!!\"\n self.p.terminate()\n tornado.web.RequestHandler.on_connection_close(self, *args, **kwargs)","def on_finish(self):","def sync_end(self):","def end_message(self):\r\n if self.cur_message is not None:\r\n self.cur_message.end_time = datetime.now()\r\n self.wait_message()","async def end(self, roles, dialogs):\n self.ended = True","def handler(self):\n\t\tself.exitClient()","def on_exit(self, userdata):\n pass","def onMessageFrameEnd(self):","def end(self) -> None:\n return","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def handle_close(self):\n LOGGER.debug(\"server closed\")\n self.clear()\n data = {}\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\n self.close()\n\n # close all and restart\n asyncore.close_all()","def api_end_game(self):\n pass","def end(self) -> None:","def post_virtual_network_create(self, resource_dict):\n pass","def postProcess(self, message):\n self.conn.close()","def onleaveerror(self, event):\n #print(self.config)\n #try:\n # self.mqttclient.publish(self.config['MQTT']['TOPICBASE'] + '/fsm/state', e.src + '->' + e.dst)\n # self.mqttclient.disconnect()\n # self.network.disconnect()\n #except:\n # pass\n print('onleaveerror; event: %s, %s->%s' % (event.event, event.src, event.dst))","def endElement(self, name):\n if name == 'message':\n self.inMessageContent = False","def _end(self):\n\n self.logger.msg1(\"Done\")","def on_epoch_end(self):\n pass","def create_new_event(self):\n pass","def notifyEDEN2disconnected(self):\n self.notifyGUItask(\"EDEN_DISCONNECTED\")","def process_employee_exit(self):\n if self.is_employee_serving():\n self._end_date.append(datetime.now().isoformat())\n\n print(f\"Successfully processed exit for employee {self.name} on\" \\\n f\"{self._end_date[-1]}\\nWe wish {self.name} for future endeavours\")\n return\n raise RejoiningException(\"Employee not in service. Cannot process exit.\")","def handleClose(self):\n logging.info(\"%s %s\", self.address, \"closed\")\n self.logbook.clients_disconnected_count += 1","def signal_creation_event(bb_object):\n global EVENTS\n EVENTS.append( (creation_event, bb_object.data) )","def end(self):\n self._log.debug('%s: doing ..', __class__.__name__)\n self._log.debug('%s: done.', __class__.__name__)","def dcnm_network_delete_event(self, network_info):\n seg_id = network_info.get('segmentation_id')\n if not seg_id:\n LOG.error(_LE('Failed to delete network. Invalid network '\n 'info %s.'), network_info)\n query_net = self.get_network_by_segid(seg_id)\n if not query_net:\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\n 'does not exist.'), {'segid': seg_id})\n return\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\n LOG.info(_LI(\"Service network %s, returning\"), query_net.name)\n return\n # Send network delete request to neutron\n try:\n del_net = self.network.pop(query_net.network_id)\n self.neutronclient.delete_network(query_net.network_id)\n self.delete_network_db(query_net.network_id)\n except Exception as exc:\n # Failed to delete network.\n # Put back the entry to the local cache???\n self.network[query_net.network_id] = del_net\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\n '%(network)s. Reason %(err)s.'),\n {'network': query_net.name, 'err': str(exc)})","def leave(ctx, network):\n return _leave(ctx.obj['client'], network)","def on_session_ended():\n #print(\"on_session_ended\")","def on_session_ended():\n #print(\"on_session_ended\")","def connectionLost(self, reason):\r\n _Protocol.remote_destroy(self)","def edgeDragEnd(self, item):\n self.mode = MODE_NOOP\n # View::edgeDragEnd ~ End dragging edge\n self.drag_edge.remove()\n self.drag_edge = None\n\n if type(item) is GraphicsSocket:\n if item.socket != self.drag_start_socket:\n if item.socket != self.drag_start_socket:\n # if we released dragging on a socket (other then the beginning socket)\n\n # we wanna keep all the edges comming from target socket\n if not item.socket.is_multi_edges:\n item.socket.removeAllEdges()\n\n # we wanna keep all the edges comming from start socket\n if not self.drag_start_socket.is_multi_edges:\n self.drag_start_socket.removeAllEdges()\n\n new_edge = Edge(self.grScene.scene, self.drag_start_socket, item.socket, edge_type=EDGE_TYPE_BEZIER)\n #\"View::edgeDragEnd ~ created new edge:\", new_edge, \"connecting\",new_edge.start_socket, \"<-->\", new_edge.end_socket)\n # history\n self.grScene.scene.history.storeHistory(\"Created new edge by dragging\")\n return True\n\n # View::edgeDragEnd ~ everything done\n return False","def onDone(self):\n pass","def add_end_listener(self, listener):\n self.end_listeners.append(listener)","def end(self):\n\t\t\t#kill bgui objects\n\t\t\tself.notificationSystem.activeNote = None\n\t\t\tself._remove_widget(self.frame)","def on_finish(self):\n return self._on_finish","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def connectionLost(self, reason):\n self.factory._r_on_connection_lost(self)","def notify_new(self, **kwargs):\n return self.notify(\"notify_new\", **kwargs)","def run(self, network_create_args=None):\n network = self.neutron.create_network(**(network_create_args or {}))\n self.neutron.delete_network(network[\"id\"])","def sendEnd(self):\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))","def handle_close(self):\r\n self._connection_state = STATE_DISCONNECTED\r\n super(http_evented, self).handle_close()\r\n self._fail_all_pending_event_handlers()\r\n call_if_not_none_and_callable(self._onClose)","def server_close(self):\n\t\tpass","def on_data(self, event):\n if not self.quitting:\n self.the_server.process_ready_socks([event.socket_ID])","def leave_notify_event(self, widget, event):\n self.logger.debug(\"leaving widget...\")\n return self.make_callback('leave')","def close(self):\n if self.is_connected:\n def _wrapper_callback(proto):\n proto.send_close()\n return proto\n\n self.factory.on_ready(_wrapper_callback)","def on_client_exit(self, game) -> None:\n pass","def outConnectEvent(self):\r\n pass","def on_epoch_end(self):\n for callback in self.callbacks:\n callback.on_epoch_end(self, self.get_model())","def onShutdown(self, connection:MQTTConnection) -> None:","def after_send(self):"],"string":"[\n \"def visit_graph_end(self, network: Network):\\n pass\",\n \"def onMessageEnd(self):\",\n \"def network_create_event(self, network_info):\\n net = network_info['network']\\n net_id = net['id']\\n net_name = net.get('name')\\n network_db_elem = self.get_network(net_id)\\n # Check if the source of network creation is FW and if yes, skip\\n # this event.\\n # Check if there's a way to read the DB from service class\\n # TODO(padkrish)\\n if self.fw_api.is_network_source_fw(network_db_elem, net_name):\\n LOG.info(_LI(\\\"Service network %s, returning\\\"), net_name)\\n return\\n self.network[net_id] = {}\\n self.network[net_id].update(net)\\n\\n net_name = net.get('name')\\n tenant_id = net.get('tenant_id')\\n\\n # Extract segmentation_id from the network name\\n net_ext_name = self.cfg.dcnm.dcnm_net_ext\\n nobj = re.search(net_ext_name, net_name)\\n try:\\n seg_id = int((net_name[nobj.start(0) + len(net_ext_name) - 1:]\\n if nobj else None))\\n except (IndexError, TypeError, ValueError):\\n seg_id = None\\n\\n # Check if network is already created.\\n query_net = self.get_network_by_segid(seg_id) if seg_id else None\\n if query_net:\\n # The network is already created no need to process the event.\\n if query_net.source.lower() == 'dcnm':\\n # DCNM created the network. Only update network id in database.\\n prev_id = query_net.network_id\\n params = dict(columns=dict(network_id=net_id))\\n self.update_network(prev_id, **params)\\n\\n # Update the network cache.\\n prev_info = self.network.pop(prev_id)\\n prev_info['id'] = net_id\\n self.network[net_id] = prev_info\\n\\n # Update the network name. After extracting the segmentation_id\\n # no need to keep it in the name. Removing it and update\\n # the network.\\n updated_net_name = (\\n net_name[:nobj.start(0) + len(net_ext_name) - 1])\\n try:\\n body = {'network': {'name': updated_net_name, }}\\n dcnm_net = self.neutronclient.update_network(\\n net_id, body=body).get('network')\\n LOG.debug('Updated network %(network)s', dcnm_net)\\n except Exception as exc:\\n LOG.exception(_LE('Failed to update network '\\n '%(network)s. Reason %(err)s.'),\\n {'network': updated_net_name,\\n 'err': str(exc)})\\n return\\n\\n LOG.info(_LI('network_create_event: network %(name)s was created '\\n 'by %(source)s. Ignoring processing the event.'),\\n {'name': net_name, 'source': 'dcnm'})\\n return\\n\\n # Check if project (i.e. tenant) exist.\\n tenant_name = self.get_project_name(tenant_id)\\n if not tenant_name:\\n LOG.error(_LE('Failed to create network %(name)s. Project '\\n '%(tenant_id)s does not exist.'),\\n {'name': net_name, 'tenant_id': tenant_id})\\n return\\n\\n pseg_id = self.network[net_id].get('provider:segmentation_id')\\n seg_id = self._get_segmentation_id(net_id, pseg_id, 'openstack')\\n self.network[net_id]['segmentation_id'] = seg_id\\n try:\\n cfgp, fwd_mod = self.dcnm_client.get_config_profile_for_network(\\n net.get('name'))\\n self.network[net_id]['config_profile'] = cfgp\\n self.network[net_id]['fwd_mod'] = fwd_mod\\n self.add_network_db(net_id, self.network[net_id],\\n 'openstack',\\n constants.RESULT_SUCCESS)\\n LOG.debug('network_create_event: network=%s', self.network)\\n except dexc.DfaClientRequestFailed:\\n # Fail to get config profile from DCNM.\\n # Save the network info with failure result and send the request\\n # to DCNM later.\\n self.add_network_db(net_id, self.network[net_id], 'openstack',\\n constants.CREATE_FAIL)\\n LOG.error(_LE('Failed to create network=%s.'), self.network)\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def endMessage(self):\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def notify_finish_event(self):\\n self.notify(self._finish_event_type())\",\n \"def on_connection_closed(self):\",\n \"def dcnm_network_create_event(self, network_info):\\n\\n # 1. Add network info to database before sending request to\\n # neutron to create the network.\\n # Check if network is already created.\\n pre_seg_id = network_info.get('segmentation_id')\\n pre_project_name = network_info.get('project_name')\\n pre_partition_name = network_info.get('partition_name')\\n if not pre_seg_id or not pre_partition_name or not pre_project_name:\\n LOG.error(_LE('Invalid network event: %s'), network_info)\\n return\\n\\n # Check if partition name is the one that openstack created.\\n if pre_partition_name != self.cfg.dcnm.default_partition_name:\\n LOG.error(_LE('Failed to create network. Partition %(part)s is '\\n 'not %(os_part)s which is created by openstack.'),\\n {'part': pre_partition_name,\\n 'os_part': self.cfg.dcnm.default_partition_name})\\n return\\n\\n query_net = self.get_network_by_segid(pre_seg_id)\\n if query_net:\\n # The network is already created no need to process the event.\\n LOG.info(_LI('dcnm_network_create_event: network %(name)s was '\\n 'created. Ignoring processing the event.'),\\n {'name': query_net.name})\\n return\\n\\n dcnm_net_info = self.dcnm_client.get_network(pre_project_name,\\n pre_seg_id)\\n if not dcnm_net_info:\\n LOG.info(_LI('No network details for %(org)s and %(segid)s'),\\n {'org': pre_project_name, 'segid': pre_seg_id})\\n return\\n\\n net_id = utils.get_uuid()\\n pseg_id = dcnm_net_info.get('segmentId')\\n seg_id = self._get_segmentation_id(net_id, pseg_id, 'DCNM')\\n cfgp = dcnm_net_info.get('profileName')\\n net_name = dcnm_net_info.get('networkName')\\n fwd_mod = self.dcnm_client.config_profile_fwding_mode_get(cfgp)\\n tenant_name = dcnm_net_info.get('organizationName')\\n tenant_id = self.get_project_id(tenant_name)\\n\\n # Get the subnet details.\\n subnet = dcnm_net_info.get('dhcpScope')\\n if not subnet:\\n # The dhcpScope is not provided. Calculating the cidr based on\\n # gateway ip and netmask.\\n gw_addr = dcnm_net_info.get('gateway')\\n net_mask = dcnm_net_info.get('netmaskLength')\\n cidr = utils.make_cidr(gw_addr, net_mask)\\n if not cidr:\\n LOG.error(_LE('Failed to create network: '\\n 'cidr is None for %(gw)s %(mask)s'),\\n {'gw': gw_addr, 'mask': net_mask})\\n return\\n subnet = dict(gateway=gw_addr, subnet=cidr)\\n\\n # Check if parameters are provided.\\n if not (net_name and tenant_id and seg_id and subnet):\\n LOG.error(_LE('Invalid value: network %(name)s tenant_id '\\n '%(tenant_id)s segmentation_id %(seg_id)s '\\n 'subnet %(subnet)s.'), {'name': net_name,\\n 'tenant_id': tenant_id,\\n 'seg_id': seg_id,\\n 'subnet': subnet})\\n return\\n\\n # Update network cache and add the network to the database.\\n net_ext_name = self.cfg.dcnm.dcnm_net_ext\\n self.network[net_id] = dict(segmentation_id=seg_id,\\n config_profile=cfgp,\\n fwd_mod=fwd_mod,\\n tenant_id=tenant_id,\\n name=net_name + net_ext_name,\\n id=net_id,\\n source='DCNM')\\n self.add_network_db(net_id, self.network[net_id], 'DCNM',\\n constants.RESULT_SUCCESS)\\n\\n # 2. Send network create request to neutron\\n try:\\n # With create_network (called below), the same request comes as\\n # notification and it will be processed in the\\n # create_network_event. The request should not be processed as it\\n # is already processed here.\\n # The only way to decide whether it is for a new network or not is\\n # the segmentation_id (DCNM does not have uuid for network) which\\n # is unique. For that reason it is needed to send segmentation_id\\n # when creating network in openstack.\\n # Moreover, we are using network_type=local and for that reason\\n # provider:segmentation_id cannot be added as parameter when\\n # creating network. One solution is to embed segmentation_id in the\\n # network name. Then, when processing the notification, if the\\n # request is from DCNM, the segmentation_id will be extracted from\\n # network name. With that create_network_event can decide to\\n # process or deny an event.\\n updated_net_name = net_name + net_ext_name + str(seg_id)\\n body = {'network': {'name': updated_net_name,\\n 'tenant_id': tenant_id,\\n 'admin_state_up': True}}\\n dcnm_net = self.neutronclient.create_network(\\n body=body).get('network')\\n net_id = dcnm_net.get('id')\\n\\n except Exception as exc:\\n # Failed to create network, do clean up.\\n # Remove the entry from database and local cache.\\n del self.network[net_id]\\n self.delete_network_db(net_id)\\n LOG.exception(_LE('dcnm_network_create_event: Failed to create '\\n '%(network)s. Reason %(err)s.'),\\n {'network': body, 'err': str(exc)})\\n return\\n\\n LOG.debug('dcnm_network_create_event: Created network %(network)s', (\\n body))\\n\\n # 3. Send subnet create request to neutron.\\n pool = subnet.get('ipRange')\\n allocation_pools = []\\n if pool:\\n allocation_pools = [{'start': s, 'end': e} for s, e in\\n [p.split('-') for p in pool.split(',')]]\\n\\n try:\\n body = {'subnet': {'cidr': subnet.get('subnet'),\\n 'gateway_ip': subnet.get('gateway'),\\n 'ip_version': 4,\\n 'network_id': net_id,\\n 'tenant_id': tenant_id,\\n 'enable_dhcp': not self.dcnm_dhcp,\\n 'allocation_pools': allocation_pools, }}\\n if not self.dcnm_dhcp:\\n body.get('subnet').pop('allocation_pools')\\n # Send requenst to create subnet in neutron.\\n LOG.debug('Creating subnet %(subnet)s for DCNM request.', body)\\n dcnm_subnet = self.neutronclient.create_subnet(\\n body=body).get('subnet')\\n subnet_id = dcnm_subnet.get('id')\\n # Update subnet cache.\\n self.subnet[subnet_id] = {}\\n self.subnet[subnet_id].update(body.get('subnet'))\\n except Exception as exc:\\n # Failed to create network, do clean up if necessary.\\n LOG.exception(_LE('Failed to create subnet %(subnet)s for DCNM '\\n 'request. Error %(err)s'),\\n {'subnet': body['subnet'], 'err': str(exc)})\\n\\n LOG.debug('dcnm_network_create_event: Created subnet %(subnet)s', (\\n body))\",\n \"def create_network_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n network = self._get_network_info(context._network)\\n for _, _network in network.items():\\n network_type = _network.get('network_type', '')\\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\\n return\\n if network is not None:\\n try:\\n self.rpc_handler.create_network(network)\\n except:\\n pass\",\n \"def handle_finish(self, dataset):\\n\\n logger.info('End of scheduling block message received.')\",\n \"def registration_ended(self):\\n pass\",\n \"def on_close(self):\\n # remove callback so it doesn't get called in the future\\n self._notifier.remove_callback(self.process_notification)\",\n \"def visit_initializer_end(self, network: Network):\\n pass\",\n \"def on_end(self, ctx):\\n pass\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"def registration_ended(self):\\n self._write_image('end')\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"async def end_object(self):\",\n \"def on_chat_end(self, request, trigger_context):\\n raise NotImplementedError\",\n \"def process_IN_CREATE(self, event):\",\n \"def tcp_end(self, flow: mitmproxy.tcp.TCPFlow):\",\n \"def process_create_q(self):\\n while not self.create_req.empty():\\n [new_name, new_type, mvt] = self.create_req.get()\\n self.create_new_peer(new_type, new_name, mvt)\\n self.create_req.task_done()\",\n \"async def on_disconnect(self) -> None:\",\n \"def finished(self, reply):\\n pass\",\n \"def on_finish(self):\\n pass\",\n \"def on_leave(self):\\n\\n self.check_connection.cancel()\",\n \"def _handle_service_end(node: int, system: System, params: Params,\\n records: Records):\\n time_now = system.time\\n server = system.get_server(node)\\n queue = system.get_queue(node)\\n\\n # Extract the packet from the server and mark it as served:\\n packet = server.pop()\\n packet.service_finished[node] = time_now\\n packet.was_served[node] = True\\n\\n # If this node is the target, then packet is delivered.\\n # Otherwise, pass it to the next node.\\n if packet.target == node:\\n packet.was_delivered = True\\n packet.delivery_time = time_now\\n elif node < params.num_stations - 1:\\n _process_packet(node + 1, packet, system, params, records)\\n else:\\n raise RuntimeError(\\\"shouldn't arrive here: packet reached network\\\"\\n \\\"end, and this is not its destination!!\\\")\\n\\n # Start serving next packet, if exists:\\n if (packet := queue.pop()) is not None:\\n server.serve(packet)\\n packet.service_started[node] = time_now\\n system.schedule(Event.SERVICE_END, params.services[node](), node)\\n\\n # Record new system size:\\n records.get_system_size(node).add(time_now, system.get_size(node))\",\n \"def onfinish():\",\n \"def end(self):\\n ...\",\n \"def onEnd(self, agent):\\n\\n pass\",\n \"def RequestHandler_finish(self):\\n if self.request._oboe_finish_ev and self.request._oboe_ctx and self.request._oboe_ctx.is_valid():\\n ev = self.request._oboe_finish_ev\\n ctx = self.request._oboe_ctx\\n if hasattr(self, 'get_status'): # recent Tornado\\n ev.add_info(\\\"Status\\\", self.get_status())\\n elif hasattr(self, '_status_code'): # older Tornado\\n ev.add_info(\\\"Status\\\", self._status_code)\\n\\n ev.add_edge(oboe.Context.get_default())\\n ctx.report(ev)\\n\\n # clear the stored oboe event/metadata from the request object\\n self.request._oboe_ctx = None\\n self.request._oboe_finish_ev = None\",\n \"async def on_end(self, game_result: Result):\",\n \"def _onEnd(self, name, completed):\\n logging.debug(\\\"onEnd...\\\")\",\n \"async def notify_disconnected(self):\\n payload = {\\n \\\"uuid\\\": self.uuid,\\n \\\"addr\\\": self.addr,\\n \\\"port\\\": self.port,\\n }\\n msg = {\\n \\\"event\\\": \\\"connection/disconnected\\\",\\n \\\"secret\\\": WS_SECRET,\\n \\\"payload\\\": payload,\\n }\\n\\n asyncio.create_task(messages_to_game.put(Message(\\\"IO\\\", message=json.dumps(msg, sort_keys=True, indent=4))))\",\n \"def connectionLost(self, reason):\\n self.finished.callback(self.display)\",\n \"def on_terminate(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def handle_record_end():\\n LOG.info(\\\"End Recording...\\\")\\n context = {'client_name': 'mycroft_listener',\\n 'source': 'audio',\\n 'destination': [\\\"skills\\\"]}\\n bus.emit(Message('recognizer_loop:record_end', context=context))\",\n \"def keepAliveReceived(self):\",\n \"def on_event_finished(self, event):\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"def on_connection_close(self):\\n self._stat.http_stream_close += 1\\n self._logger.info(\\\"HTTP EventStream %s closed\\\", self)\\n self.finish()\\n asyncio.async(self._nc_handler.close(), loop=self._asyncio_loop)\",\n \"def end(self):\\n winners = mafia.str_player_list(self.game.winners())\\n logging.info(\\\"Game over! Winners: %s\\\" % winners)\\n\\n subject = \\\"%s: The End\\\" % self.name\\n body = \\\"Game over!\\\\n\\\\nCongratulations to %s for a well \\\" \\\\\\n \\\"(or poorly; I can't tell) played game!\\\" % winners\\n self.send_message(mafia.events.PUBLIC, subject, body)\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def on_connection_close(self, *args, **kwargs):\\n print \\\"CONNECTION CLOSED!!!!\\\"\\n self.p.terminate()\\n tornado.web.RequestHandler.on_connection_close(self, *args, **kwargs)\",\n \"def on_finish(self):\",\n \"def sync_end(self):\",\n \"def end_message(self):\\r\\n if self.cur_message is not None:\\r\\n self.cur_message.end_time = datetime.now()\\r\\n self.wait_message()\",\n \"async def end(self, roles, dialogs):\\n self.ended = True\",\n \"def handler(self):\\n\\t\\tself.exitClient()\",\n \"def on_exit(self, userdata):\\n pass\",\n \"def onMessageFrameEnd(self):\",\n \"def end(self) -> None:\\n return\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def handle_close(self):\\n LOGGER.debug(\\\"server closed\\\")\\n self.clear()\\n data = {}\\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\\n self.close()\\n\\n # close all and restart\\n asyncore.close_all()\",\n \"def api_end_game(self):\\n pass\",\n \"def end(self) -> None:\",\n \"def post_virtual_network_create(self, resource_dict):\\n pass\",\n \"def postProcess(self, message):\\n self.conn.close()\",\n \"def onleaveerror(self, event):\\n #print(self.config)\\n #try:\\n # self.mqttclient.publish(self.config['MQTT']['TOPICBASE'] + '/fsm/state', e.src + '->' + e.dst)\\n # self.mqttclient.disconnect()\\n # self.network.disconnect()\\n #except:\\n # pass\\n print('onleaveerror; event: %s, %s->%s' % (event.event, event.src, event.dst))\",\n \"def endElement(self, name):\\n if name == 'message':\\n self.inMessageContent = False\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def on_epoch_end(self):\\n pass\",\n \"def create_new_event(self):\\n pass\",\n \"def notifyEDEN2disconnected(self):\\n self.notifyGUItask(\\\"EDEN_DISCONNECTED\\\")\",\n \"def process_employee_exit(self):\\n if self.is_employee_serving():\\n self._end_date.append(datetime.now().isoformat())\\n\\n print(f\\\"Successfully processed exit for employee {self.name} on\\\" \\\\\\n f\\\"{self._end_date[-1]}\\\\nWe wish {self.name} for future endeavours\\\")\\n return\\n raise RejoiningException(\\\"Employee not in service. Cannot process exit.\\\")\",\n \"def handleClose(self):\\n logging.info(\\\"%s %s\\\", self.address, \\\"closed\\\")\\n self.logbook.clients_disconnected_count += 1\",\n \"def signal_creation_event(bb_object):\\n global EVENTS\\n EVENTS.append( (creation_event, bb_object.data) )\",\n \"def end(self):\\n self._log.debug('%s: doing ..', __class__.__name__)\\n self._log.debug('%s: done.', __class__.__name__)\",\n \"def dcnm_network_delete_event(self, network_info):\\n seg_id = network_info.get('segmentation_id')\\n if not seg_id:\\n LOG.error(_LE('Failed to delete network. Invalid network '\\n 'info %s.'), network_info)\\n query_net = self.get_network_by_segid(seg_id)\\n if not query_net:\\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\\n 'does not exist.'), {'segid': seg_id})\\n return\\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\\n LOG.info(_LI(\\\"Service network %s, returning\\\"), query_net.name)\\n return\\n # Send network delete request to neutron\\n try:\\n del_net = self.network.pop(query_net.network_id)\\n self.neutronclient.delete_network(query_net.network_id)\\n self.delete_network_db(query_net.network_id)\\n except Exception as exc:\\n # Failed to delete network.\\n # Put back the entry to the local cache???\\n self.network[query_net.network_id] = del_net\\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\\n '%(network)s. Reason %(err)s.'),\\n {'network': query_net.name, 'err': str(exc)})\",\n \"def leave(ctx, network):\\n return _leave(ctx.obj['client'], network)\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def connectionLost(self, reason):\\r\\n _Protocol.remote_destroy(self)\",\n \"def edgeDragEnd(self, item):\\n self.mode = MODE_NOOP\\n # View::edgeDragEnd ~ End dragging edge\\n self.drag_edge.remove()\\n self.drag_edge = None\\n\\n if type(item) is GraphicsSocket:\\n if item.socket != self.drag_start_socket:\\n if item.socket != self.drag_start_socket:\\n # if we released dragging on a socket (other then the beginning socket)\\n\\n # we wanna keep all the edges comming from target socket\\n if not item.socket.is_multi_edges:\\n item.socket.removeAllEdges()\\n\\n # we wanna keep all the edges comming from start socket\\n if not self.drag_start_socket.is_multi_edges:\\n self.drag_start_socket.removeAllEdges()\\n\\n new_edge = Edge(self.grScene.scene, self.drag_start_socket, item.socket, edge_type=EDGE_TYPE_BEZIER)\\n #\\\"View::edgeDragEnd ~ created new edge:\\\", new_edge, \\\"connecting\\\",new_edge.start_socket, \\\"<-->\\\", new_edge.end_socket)\\n # history\\n self.grScene.scene.history.storeHistory(\\\"Created new edge by dragging\\\")\\n return True\\n\\n # View::edgeDragEnd ~ everything done\\n return False\",\n \"def onDone(self):\\n pass\",\n \"def add_end_listener(self, listener):\\n self.end_listeners.append(listener)\",\n \"def end(self):\\n\\t\\t\\t#kill bgui objects\\n\\t\\t\\tself.notificationSystem.activeNote = None\\n\\t\\t\\tself._remove_widget(self.frame)\",\n \"def on_finish(self):\\n return self._on_finish\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def connectionLost(self, reason):\\n self.factory._r_on_connection_lost(self)\",\n \"def notify_new(self, **kwargs):\\n return self.notify(\\\"notify_new\\\", **kwargs)\",\n \"def run(self, network_create_args=None):\\n network = self.neutron.create_network(**(network_create_args or {}))\\n self.neutron.delete_network(network[\\\"id\\\"])\",\n \"def sendEnd(self):\\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))\",\n \"def handle_close(self):\\r\\n self._connection_state = STATE_DISCONNECTED\\r\\n super(http_evented, self).handle_close()\\r\\n self._fail_all_pending_event_handlers()\\r\\n call_if_not_none_and_callable(self._onClose)\",\n \"def server_close(self):\\n\\t\\tpass\",\n \"def on_data(self, event):\\n if not self.quitting:\\n self.the_server.process_ready_socks([event.socket_ID])\",\n \"def leave_notify_event(self, widget, event):\\n self.logger.debug(\\\"leaving widget...\\\")\\n return self.make_callback('leave')\",\n \"def close(self):\\n if self.is_connected:\\n def _wrapper_callback(proto):\\n proto.send_close()\\n return proto\\n\\n self.factory.on_ready(_wrapper_callback)\",\n \"def on_client_exit(self, game) -> None:\\n pass\",\n \"def outConnectEvent(self):\\r\\n pass\",\n \"def on_epoch_end(self):\\n for callback in self.callbacks:\\n callback.on_epoch_end(self, self.get_model())\",\n \"def onShutdown(self, connection:MQTTConnection) -> None:\",\n \"def after_send(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6248566","0.6031988","0.57522935","0.57338864","0.5701586","0.5675204","0.5653594","0.5636173","0.5629332","0.5538093","0.54424596","0.5430942","0.5419787","0.53785735","0.53761613","0.53417826","0.53303516","0.5320978","0.531378","0.530669","0.52717966","0.52618504","0.52574766","0.52473176","0.5196618","0.5189299","0.51856947","0.5183474","0.5174494","0.5157761","0.5155785","0.51549333","0.51523936","0.514916","0.5136147","0.5128437","0.5124976","0.5118526","0.5107933","0.51052934","0.51052934","0.51052934","0.50943136","0.5090518","0.5068917","0.50622106","0.5054155","0.5045453","0.50327784","0.5025173","0.50181","0.50173414","0.4983114","0.49732122","0.4966602","0.49664408","0.4965179","0.49610636","0.49572384","0.49567506","0.49480474","0.49430752","0.49098212","0.49036303","0.48900935","0.48875558","0.48852873","0.48845953","0.48833776","0.4878243","0.48781145","0.48777747","0.48686397","0.48501068","0.48449403","0.48436743","0.48258922","0.4813398","0.4813398","0.4812188","0.48040742","0.47922134","0.47882873","0.47876397","0.47868443","0.4782286","0.47797775","0.47776344","0.47740403","0.47726026","0.47677127","0.4765754","0.47631866","0.47584423","0.47563186","0.4755387","0.4754021","0.47525248","0.47469202","0.47441378"],"string":"[\n \"0.6248566\",\n \"0.6031988\",\n \"0.57522935\",\n \"0.57338864\",\n \"0.5701586\",\n \"0.5675204\",\n \"0.5653594\",\n \"0.5636173\",\n \"0.5629332\",\n \"0.5538093\",\n \"0.54424596\",\n \"0.5430942\",\n \"0.5419787\",\n \"0.53785735\",\n \"0.53761613\",\n \"0.53417826\",\n \"0.53303516\",\n \"0.5320978\",\n \"0.531378\",\n \"0.530669\",\n \"0.52717966\",\n \"0.52618504\",\n \"0.52574766\",\n \"0.52473176\",\n \"0.5196618\",\n \"0.5189299\",\n \"0.51856947\",\n \"0.5183474\",\n \"0.5174494\",\n \"0.5157761\",\n \"0.5155785\",\n \"0.51549333\",\n \"0.51523936\",\n \"0.514916\",\n \"0.5136147\",\n \"0.5128437\",\n \"0.5124976\",\n \"0.5118526\",\n \"0.5107933\",\n \"0.51052934\",\n \"0.51052934\",\n \"0.51052934\",\n \"0.50943136\",\n \"0.5090518\",\n \"0.5068917\",\n \"0.50622106\",\n \"0.5054155\",\n \"0.5045453\",\n \"0.50327784\",\n \"0.5025173\",\n \"0.50181\",\n \"0.50173414\",\n \"0.4983114\",\n \"0.49732122\",\n \"0.4966602\",\n \"0.49664408\",\n \"0.4965179\",\n \"0.49610636\",\n \"0.49572384\",\n \"0.49567506\",\n \"0.49480474\",\n \"0.49430752\",\n \"0.49098212\",\n \"0.49036303\",\n \"0.48900935\",\n \"0.48875558\",\n \"0.48852873\",\n \"0.48845953\",\n \"0.48833776\",\n \"0.4878243\",\n \"0.48781145\",\n \"0.48777747\",\n \"0.48686397\",\n \"0.48501068\",\n \"0.48449403\",\n \"0.48436743\",\n \"0.48258922\",\n \"0.4813398\",\n \"0.4813398\",\n \"0.4812188\",\n \"0.48040742\",\n \"0.47922134\",\n \"0.47882873\",\n \"0.47876397\",\n \"0.47868443\",\n \"0.4782286\",\n \"0.47797775\",\n \"0.47776344\",\n \"0.47740403\",\n \"0.47726026\",\n \"0.47677127\",\n \"0.4765754\",\n \"0.47631866\",\n \"0.47584423\",\n \"0.47563186\",\n \"0.4755387\",\n \"0.4754021\",\n \"0.47525248\",\n \"0.47469202\",\n \"0.47441378\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6849954"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79791,"cells":{"query":{"kind":"string","value":"Handle the network.update.end notification event."},"document":{"kind":"string","value":"def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def notify_end(self, status, objective):\n pass # pragma: no cover","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def end(update: Update, context: CallbackContext) -> int:\n update.callback_query.answer()\n\n text = \"See you around!\"\n update.callback_query.edit_message_text(text=text)\n\n return END","def onMessageEnd(self):","def end(update, context) -> int:\n update.callback_query.edit_message_text(\n 'Bye! I hope we can talk again some day.')\n\n logger.info(\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\",\n update.callback_query.message.chat.first_name)\n return ConversationHandler.END","def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def notify_finish_event(self):\n self.notify(self._finish_event_type())","def subnet_update_end(self, payload):\n network_id = payload['subnet']['network_id']\n self.refresh_dhcp_helper(network_id)","def endMessage(self):","def sync_end(self):","def visit_graph_end(self, network: Network):\n pass","def end(update, context):\n query = update.callback_query\n bot = context.bot\n bot.edit_message_text(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\"See you next time!\"\n )\n return ConversationHandler.END","def handle_updates(self, update):\r\n self.__manage_pump()","def on_connection_closed(self):","def api_end_game(self):\n pass","def _notify_listeners_end_operation(self):\n op = self.operation\n for lstnr in self.listeners:\n lstnr.end_operation(self, op)\n self.operation = None","async def on_end(self, game_result: Result):","def end(self, obs: AgentObservation):\n self.brain.end(obs)","def on_end(self, ctx):\n pass","def on_epoch_end(self):\n pass","def _handle_service_end(node: int, system: System, params: Params,\n records: Records):\n time_now = system.time\n server = system.get_server(node)\n queue = system.get_queue(node)\n\n # Extract the packet from the server and mark it as served:\n packet = server.pop()\n packet.service_finished[node] = time_now\n packet.was_served[node] = True\n\n # If this node is the target, then packet is delivered.\n # Otherwise, pass it to the next node.\n if packet.target == node:\n packet.was_delivered = True\n packet.delivery_time = time_now\n elif node < params.num_stations - 1:\n _process_packet(node + 1, packet, system, params, records)\n else:\n raise RuntimeError(\"shouldn't arrive here: packet reached network\"\n \"end, and this is not its destination!!\")\n\n # Start serving next packet, if exists:\n if (packet := queue.pop()) is not None:\n server.serve(packet)\n packet.service_started[node] = time_now\n system.schedule(Event.SERVICE_END, params.services[node](), node)\n\n # Record new system size:\n records.get_system_size(node).add(time_now, system.get_size(node))","def _end(self):\n\n self.logger.msg1(\"Done\")","def handle_finish(self, dataset):\n\n logger.info('End of scheduling block message received.')","def end_message(self):\r\n if self.cur_message is not None:\r\n self.cur_message.end_time = datetime.now()\r\n self.wait_message()","def END(self):\n log.debug(\"All packets successfully transmitted!\")","def complete(self, update):\n self.reactor.callFromThread(self._complete, update)","def on_update(self, event, signal):\n t = ppb.get_time() - self.start_time\n if t >= self.duration:\n signal(ppb.events.Quit())","def finished(self, reply):\n pass","def on_event_finished(self, event):","def on_connection_close(self):\n self._stat.http_stream_close += 1\n self._logger.info(\"HTTP EventStream %s closed\", self)\n self.finish()\n asyncio.async(self._nc_handler.close(), loop=self._asyncio_loop)","def on_chat_end(self, request, trigger_context):\n raise NotImplementedError","def connectionLost(self, reason):\n self.finished.callback(self.display)","def on_close(self):\n # remove callback so it doesn't get called in the future\n self._notifier.remove_callback(self.process_notification)","def stop(update: Update, context: CallbackContext) -> int:\n update.message.reply_text(\"Okay, bye.\")\n\n return END","async def handle_player_update(self, update: andesite.PlayerUpdate) -> None:\n ...","async def last(self, update: Update, _: ContextTypes.DEFAULT_TYPE) -> None:\n self.door_status.update_status()\n await update.message.reply_text(text=f\"🐤 {self.door_status.last_line}\")","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","async def after_update_callback(self, _: XknxConnectionState) -> None:\n self.async_write_ha_state()","def on_epoch_end(self):","def onEnd(self, agent):\n\n pass","def handleResponseEnd(self):\r\n try:\r\n if not self._finished:\r\n reactor.callInThread(\r\n self.resource.cacheContent,\r\n self.father,\r\n self._response,\r\n self.buffer\r\n )\r\n proxy.ProxyClient.handleResponseEnd(self)\r\n except RuntimeError:\r\n # because we don't care if the user hits\r\n # refresh before the request is done\r\n pass","def bcp_game_end(self, **kwargs):\n self.player = None\n self.events.post('game_ended', **kwargs)","def onEpochEnd(self, agent):\n\n pass","def halt_async_handle_updates(self):\n self._halt = True","def end(self, won, reason):\n pass\n # replace with your end logic","def _onEnd(self, name, completed):\n logging.debug(\"onEnd...\")","def _async_update_callback(self):\n self._async_update_device_data()\n self.async_write_ha_state()","def onMessageFrameEnd(self):","async def end_object(self):","def after_update(self, *args):\n raise NotImplementedError","def end(self):\n ...","def on_session_ended():\n #print(\"on_session_ended\")","def on_session_ended():\n #print(\"on_session_ended\")","def set_update_received_callback(self, callback):\n self.__update_received = callback","def on_epoch_end(self):\n for callback in self.callbacks:\n callback.on_epoch_end(self, self.get_model())","async def on_disconnect(self) -> None:","def _telegram_stop_callback(self, update: Update, _: CallbackContext):\n\n rospy.loginfo(\"Stopping Telegram ROS bridge for chat id {}\".format(self._telegram_chat_id))\n update.message.reply_text(\n \"Disconnecting chat_id {}. So long and thanks for all the fish!\"\n \" Type /start to reconnect\".format(self._telegram_chat_id)\n )\n self._telegram_chat_id = None","def sendSslEnd(self, req):\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))","def end(self):\n winners = mafia.str_player_list(self.game.winners())\n logging.info(\"Game over! Winners: %s\" % winners)\n\n subject = \"%s: The End\" % self.name\n body = \"Game over!\\n\\nCongratulations to %s for a well \" \\\n \"(or poorly; I can't tell) played game!\" % winners\n self.send_message(mafia.events.PUBLIC, subject, body)","def bcp_ball_end(self, **kwargs):\n self.events.post('ball_ended', **kwargs)","def end(self):\n pass","def end(self):\n pass","def end(self):\n pass","def handle_close(self):\n LOGGER.debug(\"server closed\")\n self.clear()\n data = {}\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\n self.close()\n\n # close all and restart\n asyncore.close_all()","def sendEnd(self):\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))","def round_end(event_var):\r\n debug.write(\"[SourceRPG] Round End Processing\", 1)\r\n if not currentTurboMode:\r\n debug.write(\"Normal mode is on\", 1)\r\n if str(saveType).lower() == \"round end\":\r\n debug.write(\"Attempting to save the database\", 1)\r\n saveDatabase()\r\n debug.write(\"Database successfully saved.\", 1)\r\n for userid in es.getUseridList():\r\n debug.write(\"Fetching player object for %s\" % es.getplayername(userid), 2)\r\n player = players[userid]\r\n if player is not None:\r\n debug.write(\"Object successfully fetched, delaying 4.8 seconds to reset default attributes\", 2)\r\n gamethread.delayedname(2, 'sourcerpg_reset_%s' % player, player.resetPlayerDefaultAttributes)\r\n debug.write(\"[SourceRPG] Round End has been processed\\n\", 1)","def async_update_callback(self, data):\n was_available = self._available\n self._available = data.get(KEY_CONNECTED, True)\n if update_needs_bg_power_workaround(data) or (\n self._did_first_update and not was_available and self._available\n ):\n # On reconnect the properties may be out of sync\n #\n # We need to make sure the DEVICE_INITIALIZED dispatcher is setup\n # before we can update on reconnect by checking self._did_first_update\n #\n # If the device drops the connection right away, we do not want to\n # do a property resync via async_update since its about\n # to be called when async_setup_entry reaches the end of the\n # function\n #\n async_call_later(self._hass, STATE_CHANGE_TIME, self._async_forced_update)\n async_dispatcher_send(self._hass, DATA_UPDATED.format(self._host))","def handle_actual_updated(self):\n self._actual_updated()","def processEnded(self, reason):\n if not self.empty:\n self.output.dataReceived('\\n')\n self.service.processEnded(self.name)","def end(self):\n self._log.debug('%s: doing ..', __class__.__name__)\n self._log.debug('%s: done.', __class__.__name__)","def _notify_update(self, cuds_object):","def on_epoch_end(self):\n self.epoch_bar.update()","async def notify_disconnected(self):\n payload = {\n \"uuid\": self.uuid,\n \"addr\": self.addr,\n \"port\": self.port,\n }\n msg = {\n \"event\": \"connection/disconnected\",\n \"secret\": WS_SECRET,\n \"payload\": payload,\n }\n\n asyncio.create_task(messages_to_game.put(Message(\"IO\", message=json.dumps(msg, sort_keys=True, indent=4))))","def notifyEDEN2disconnected(self):\n self.notifyGUItask(\"EDEN_DISCONNECTED\")","def on_epoch_end(self, epoch, logs=None):","def on_epoch_end(self, epoch, logs=None):","def onShutdown(self, connection:MQTTConnection) -> None:","def end(event: EventType, widget: WidgetType) -> bool:\n return event.key == _locals.K_END","def end(self):\n\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\n # 3 minutes before giving up.\n if self.device_details['device_type'] == 'PBR':\n pump_id = self.device_details['setup']['pump_id']\n counter = 0\n while counter < 60:\n try:\n result = True, self.device.set_pump_state(pump_id, False)\n except Exception as e:\n result = False, e\n\n response = [(datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")),\n self.device_details['node_id'],\n self.device_details['device_type'],\n 8,\n [pump_id, False],\n result,\n 'internal']\n\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\n\n # end the loop if the pump has been successfully turned off\n if result[0]:\n break\n else:\n counter += 1\n sleep(3)\n\n self.device.disconnect()","def on_disconnect(self):\n # self.node_worker_ref.socketIO.disconnect()\n self.log.info('Disconnected from the server')","def end(self):\n self.my_print(\"\\t[DONE]\", msg_types.INFO)\n self.in_progress = False","def _on_stop(self, _):\n self.unit.status = MaintenanceStatus(\"Pod is terminating.\")\n logger.info(\"Pod is terminating.\")","def do_end(end):\n if end:\n do_action(end)","def _handle_event(self, resp: Message):\n if resp.state_update == {}:\n _LOGGER.debug(\"No state update in message: %s\", resp.message)\n if self._avr.update_state(resp.state_update):\n self._avr.dispatcher.send(const.SIGNAL_STATE_UPDATE, resp.message)\n _LOGGER.debug(\"Event received: %s\", resp.state_update)\n if expected_response_items := self._expected_responses.popmatch(resp.group):\n _, expected_response = expected_response_items\n expected_response.set(resp)\n else:\n _LOGGER.debug(\"No expected response matched: %s\", resp.group)","def finish(self):\n global log_th\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))","def on_epoch_end(self, epoch, logs):\n raise NotImplemented","def link_down_respond(self, neighbor):\n neighbor.is_killed = True\n if self.update_dv():\n for name in self.neighbors:\n self.neighbors[name].update_ready = True\n self.neighbors[name].send_timer = time.time()","def handle_update(self, call):\n self.fire_event(EVENT_UPDATE)","def update(self,data):\r\n data = data.split(':',1)\r\n\r\n self.log('Signal','Received an update: %s...' % repr(data)[0:10],'update')\r\n \r\n #print \"*** local: \" + repr(data)\r\n \r\n if data[0] == 'Results':\r\n\r\n self.log('Signal','The local node returned these passwords: %s' % repr(data[1]),'update')\r\n\r\n self.addResult(data[1])\r\n elif data[0] == 'Bench':\r\n self.log('Signal','The local node returned these benches: %s' % repr(data[1]),'update')\r\n \r\n self.addBench(data[1])\r\n\r\n elif data[0] == 'Work':\r\n if data[1] == 'Done':\r\n self.finished += 1\r\n if self.finished >= len(self.nodes):\r\n self.runningWork = False\r\n self.log('Signal','Finished working','update')\r\n\r\n notification = 'Work:Done'\r\n self.notifyObservers(notification)","def port_update_end(self, payload):\n port = DictModel(payload['port'])\n network = self.cache.get_network_by_id(port.network_id)\n if network:\n self.cache.put_port(port)\n self.call_driver('reload_allocations', network)","def process_employee_exit(self):\n if self.is_employee_serving():\n self._end_date.append(datetime.now().isoformat())\n\n print(f\"Successfully processed exit for employee {self.name} on\" \\\n f\"{self._end_date[-1]}\\nWe wish {self.name} for future endeavours\")\n return\n raise RejoiningException(\"Employee not in service. Cannot process exit.\")","def end(self) -> None:","def on_epoch_end(self):\n pass","async def on_close(self) -> None:\n for device_id in self.devices:\n is_reachable: bool = False\n async_dispatcher_send(\n self.hass,\n f\"{LIVISI_REACHABILITY_CHANGE}_{device_id}\",\n is_reachable,\n )\n\n await self.websocket.connect(self.on_data, self.on_close, self.port)","def update_callback(self, output_id=0):\n if output_id == 0 | output_id == self._output_id:\n _LOGGER.info(\"SmartVideoHub sent a status update for output %i\", output_id)\n self.update()\n self.schedule_update_ha_state(False)","def end(self):\n self.shutdown = True","def RequestHandler_finish(self):\n if self.request._oboe_finish_ev and self.request._oboe_ctx and self.request._oboe_ctx.is_valid():\n ev = self.request._oboe_finish_ev\n ctx = self.request._oboe_ctx\n if hasattr(self, 'get_status'): # recent Tornado\n ev.add_info(\"Status\", self.get_status())\n elif hasattr(self, '_status_code'): # older Tornado\n ev.add_info(\"Status\", self._status_code)\n\n ev.add_edge(oboe.Context.get_default())\n ctx.report(ev)\n\n # clear the stored oboe event/metadata from the request object\n self.request._oboe_ctx = None\n self.request._oboe_finish_ev = None","def keepAliveReceived(self):","def end_of_level(self):\n Clock.unschedule(self.update)\n self.no_events()","def check_track_end_event():\n global check_track_end_id\n global TRACK_END\n\n if track_status.get() == \"(Playing)\":\n\n # If we reach the end of the track, initialize the player\n for event in pygame.event.get():\n if event.type == TRACK_END:\n cancel_track_end_event_loop()\n cancel_update_play_time_loop()\n # Displaying Status\n track_status.set(\"(Stopped)\")\n reset_track()\n play_next()\n return\n\n check_track_end_id = root.after(100, check_track_end_event)"],"string":"[\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def end(update: Update, context: CallbackContext) -> int:\\n update.callback_query.answer()\\n\\n text = \\\"See you around!\\\"\\n update.callback_query.edit_message_text(text=text)\\n\\n return END\",\n \"def onMessageEnd(self):\",\n \"def end(update, context) -> int:\\n update.callback_query.edit_message_text(\\n 'Bye! I hope we can talk again some day.')\\n\\n logger.info(\\\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\\\",\\n update.callback_query.message.chat.first_name)\\n return ConversationHandler.END\",\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def notify_finish_event(self):\\n self.notify(self._finish_event_type())\",\n \"def subnet_update_end(self, payload):\\n network_id = payload['subnet']['network_id']\\n self.refresh_dhcp_helper(network_id)\",\n \"def endMessage(self):\",\n \"def sync_end(self):\",\n \"def visit_graph_end(self, network: Network):\\n pass\",\n \"def end(update, context):\\n query = update.callback_query\\n bot = context.bot\\n bot.edit_message_text(\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n text=\\\"See you next time!\\\"\\n )\\n return ConversationHandler.END\",\n \"def handle_updates(self, update):\\r\\n self.__manage_pump()\",\n \"def on_connection_closed(self):\",\n \"def api_end_game(self):\\n pass\",\n \"def _notify_listeners_end_operation(self):\\n op = self.operation\\n for lstnr in self.listeners:\\n lstnr.end_operation(self, op)\\n self.operation = None\",\n \"async def on_end(self, game_result: Result):\",\n \"def end(self, obs: AgentObservation):\\n self.brain.end(obs)\",\n \"def on_end(self, ctx):\\n pass\",\n \"def on_epoch_end(self):\\n pass\",\n \"def _handle_service_end(node: int, system: System, params: Params,\\n records: Records):\\n time_now = system.time\\n server = system.get_server(node)\\n queue = system.get_queue(node)\\n\\n # Extract the packet from the server and mark it as served:\\n packet = server.pop()\\n packet.service_finished[node] = time_now\\n packet.was_served[node] = True\\n\\n # If this node is the target, then packet is delivered.\\n # Otherwise, pass it to the next node.\\n if packet.target == node:\\n packet.was_delivered = True\\n packet.delivery_time = time_now\\n elif node < params.num_stations - 1:\\n _process_packet(node + 1, packet, system, params, records)\\n else:\\n raise RuntimeError(\\\"shouldn't arrive here: packet reached network\\\"\\n \\\"end, and this is not its destination!!\\\")\\n\\n # Start serving next packet, if exists:\\n if (packet := queue.pop()) is not None:\\n server.serve(packet)\\n packet.service_started[node] = time_now\\n system.schedule(Event.SERVICE_END, params.services[node](), node)\\n\\n # Record new system size:\\n records.get_system_size(node).add(time_now, system.get_size(node))\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def handle_finish(self, dataset):\\n\\n logger.info('End of scheduling block message received.')\",\n \"def end_message(self):\\r\\n if self.cur_message is not None:\\r\\n self.cur_message.end_time = datetime.now()\\r\\n self.wait_message()\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"def complete(self, update):\\n self.reactor.callFromThread(self._complete, update)\",\n \"def on_update(self, event, signal):\\n t = ppb.get_time() - self.start_time\\n if t >= self.duration:\\n signal(ppb.events.Quit())\",\n \"def finished(self, reply):\\n pass\",\n \"def on_event_finished(self, event):\",\n \"def on_connection_close(self):\\n self._stat.http_stream_close += 1\\n self._logger.info(\\\"HTTP EventStream %s closed\\\", self)\\n self.finish()\\n asyncio.async(self._nc_handler.close(), loop=self._asyncio_loop)\",\n \"def on_chat_end(self, request, trigger_context):\\n raise NotImplementedError\",\n \"def connectionLost(self, reason):\\n self.finished.callback(self.display)\",\n \"def on_close(self):\\n # remove callback so it doesn't get called in the future\\n self._notifier.remove_callback(self.process_notification)\",\n \"def stop(update: Update, context: CallbackContext) -> int:\\n update.message.reply_text(\\\"Okay, bye.\\\")\\n\\n return END\",\n \"async def handle_player_update(self, update: andesite.PlayerUpdate) -> None:\\n ...\",\n \"async def last(self, update: Update, _: ContextTypes.DEFAULT_TYPE) -> None:\\n self.door_status.update_status()\\n await update.message.reply_text(text=f\\\"🐤 {self.door_status.last_line}\\\")\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"async def after_update_callback(self, _: XknxConnectionState) -> None:\\n self.async_write_ha_state()\",\n \"def on_epoch_end(self):\",\n \"def onEnd(self, agent):\\n\\n pass\",\n \"def handleResponseEnd(self):\\r\\n try:\\r\\n if not self._finished:\\r\\n reactor.callInThread(\\r\\n self.resource.cacheContent,\\r\\n self.father,\\r\\n self._response,\\r\\n self.buffer\\r\\n )\\r\\n proxy.ProxyClient.handleResponseEnd(self)\\r\\n except RuntimeError:\\r\\n # because we don't care if the user hits\\r\\n # refresh before the request is done\\r\\n pass\",\n \"def bcp_game_end(self, **kwargs):\\n self.player = None\\n self.events.post('game_ended', **kwargs)\",\n \"def onEpochEnd(self, agent):\\n\\n pass\",\n \"def halt_async_handle_updates(self):\\n self._halt = True\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"def _onEnd(self, name, completed):\\n logging.debug(\\\"onEnd...\\\")\",\n \"def _async_update_callback(self):\\n self._async_update_device_data()\\n self.async_write_ha_state()\",\n \"def onMessageFrameEnd(self):\",\n \"async def end_object(self):\",\n \"def after_update(self, *args):\\n raise NotImplementedError\",\n \"def end(self):\\n ...\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def set_update_received_callback(self, callback):\\n self.__update_received = callback\",\n \"def on_epoch_end(self):\\n for callback in self.callbacks:\\n callback.on_epoch_end(self, self.get_model())\",\n \"async def on_disconnect(self) -> None:\",\n \"def _telegram_stop_callback(self, update: Update, _: CallbackContext):\\n\\n rospy.loginfo(\\\"Stopping Telegram ROS bridge for chat id {}\\\".format(self._telegram_chat_id))\\n update.message.reply_text(\\n \\\"Disconnecting chat_id {}. So long and thanks for all the fish!\\\"\\n \\\" Type /start to reconnect\\\".format(self._telegram_chat_id)\\n )\\n self._telegram_chat_id = None\",\n \"def sendSslEnd(self, req):\\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))\",\n \"def end(self):\\n winners = mafia.str_player_list(self.game.winners())\\n logging.info(\\\"Game over! Winners: %s\\\" % winners)\\n\\n subject = \\\"%s: The End\\\" % self.name\\n body = \\\"Game over!\\\\n\\\\nCongratulations to %s for a well \\\" \\\\\\n \\\"(or poorly; I can't tell) played game!\\\" % winners\\n self.send_message(mafia.events.PUBLIC, subject, body)\",\n \"def bcp_ball_end(self, **kwargs):\\n self.events.post('ball_ended', **kwargs)\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def handle_close(self):\\n LOGGER.debug(\\\"server closed\\\")\\n self.clear()\\n data = {}\\n self.update_notify_state(data, STAGE_SERVER_UNCONNECTED)\\n self.close()\\n\\n # close all and restart\\n asyncore.close_all()\",\n \"def sendEnd(self):\\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))\",\n \"def round_end(event_var):\\r\\n debug.write(\\\"[SourceRPG] Round End Processing\\\", 1)\\r\\n if not currentTurboMode:\\r\\n debug.write(\\\"Normal mode is on\\\", 1)\\r\\n if str(saveType).lower() == \\\"round end\\\":\\r\\n debug.write(\\\"Attempting to save the database\\\", 1)\\r\\n saveDatabase()\\r\\n debug.write(\\\"Database successfully saved.\\\", 1)\\r\\n for userid in es.getUseridList():\\r\\n debug.write(\\\"Fetching player object for %s\\\" % es.getplayername(userid), 2)\\r\\n player = players[userid]\\r\\n if player is not None:\\r\\n debug.write(\\\"Object successfully fetched, delaying 4.8 seconds to reset default attributes\\\", 2)\\r\\n gamethread.delayedname(2, 'sourcerpg_reset_%s' % player, player.resetPlayerDefaultAttributes)\\r\\n debug.write(\\\"[SourceRPG] Round End has been processed\\\\n\\\", 1)\",\n \"def async_update_callback(self, data):\\n was_available = self._available\\n self._available = data.get(KEY_CONNECTED, True)\\n if update_needs_bg_power_workaround(data) or (\\n self._did_first_update and not was_available and self._available\\n ):\\n # On reconnect the properties may be out of sync\\n #\\n # We need to make sure the DEVICE_INITIALIZED dispatcher is setup\\n # before we can update on reconnect by checking self._did_first_update\\n #\\n # If the device drops the connection right away, we do not want to\\n # do a property resync via async_update since its about\\n # to be called when async_setup_entry reaches the end of the\\n # function\\n #\\n async_call_later(self._hass, STATE_CHANGE_TIME, self._async_forced_update)\\n async_dispatcher_send(self._hass, DATA_UPDATED.format(self._host))\",\n \"def handle_actual_updated(self):\\n self._actual_updated()\",\n \"def processEnded(self, reason):\\n if not self.empty:\\n self.output.dataReceived('\\\\n')\\n self.service.processEnded(self.name)\",\n \"def end(self):\\n self._log.debug('%s: doing ..', __class__.__name__)\\n self._log.debug('%s: done.', __class__.__name__)\",\n \"def _notify_update(self, cuds_object):\",\n \"def on_epoch_end(self):\\n self.epoch_bar.update()\",\n \"async def notify_disconnected(self):\\n payload = {\\n \\\"uuid\\\": self.uuid,\\n \\\"addr\\\": self.addr,\\n \\\"port\\\": self.port,\\n }\\n msg = {\\n \\\"event\\\": \\\"connection/disconnected\\\",\\n \\\"secret\\\": WS_SECRET,\\n \\\"payload\\\": payload,\\n }\\n\\n asyncio.create_task(messages_to_game.put(Message(\\\"IO\\\", message=json.dumps(msg, sort_keys=True, indent=4))))\",\n \"def notifyEDEN2disconnected(self):\\n self.notifyGUItask(\\\"EDEN_DISCONNECTED\\\")\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def onShutdown(self, connection:MQTTConnection) -> None:\",\n \"def end(event: EventType, widget: WidgetType) -> bool:\\n return event.key == _locals.K_END\",\n \"def end(self):\\n\\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\\n # 3 minutes before giving up.\\n if self.device_details['device_type'] == 'PBR':\\n pump_id = self.device_details['setup']['pump_id']\\n counter = 0\\n while counter < 60:\\n try:\\n result = True, self.device.set_pump_state(pump_id, False)\\n except Exception as e:\\n result = False, e\\n\\n response = [(datetime.datetime.utcnow().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")),\\n self.device_details['node_id'],\\n self.device_details['device_type'],\\n 8,\\n [pump_id, False],\\n result,\\n 'internal']\\n\\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\\n\\n # end the loop if the pump has been successfully turned off\\n if result[0]:\\n break\\n else:\\n counter += 1\\n sleep(3)\\n\\n self.device.disconnect()\",\n \"def on_disconnect(self):\\n # self.node_worker_ref.socketIO.disconnect()\\n self.log.info('Disconnected from the server')\",\n \"def end(self):\\n self.my_print(\\\"\\\\t[DONE]\\\", msg_types.INFO)\\n self.in_progress = False\",\n \"def _on_stop(self, _):\\n self.unit.status = MaintenanceStatus(\\\"Pod is terminating.\\\")\\n logger.info(\\\"Pod is terminating.\\\")\",\n \"def do_end(end):\\n if end:\\n do_action(end)\",\n \"def _handle_event(self, resp: Message):\\n if resp.state_update == {}:\\n _LOGGER.debug(\\\"No state update in message: %s\\\", resp.message)\\n if self._avr.update_state(resp.state_update):\\n self._avr.dispatcher.send(const.SIGNAL_STATE_UPDATE, resp.message)\\n _LOGGER.debug(\\\"Event received: %s\\\", resp.state_update)\\n if expected_response_items := self._expected_responses.popmatch(resp.group):\\n _, expected_response = expected_response_items\\n expected_response.set(resp)\\n else:\\n _LOGGER.debug(\\\"No expected response matched: %s\\\", resp.group)\",\n \"def finish(self):\\n global log_th\\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))\",\n \"def on_epoch_end(self, epoch, logs):\\n raise NotImplemented\",\n \"def link_down_respond(self, neighbor):\\n neighbor.is_killed = True\\n if self.update_dv():\\n for name in self.neighbors:\\n self.neighbors[name].update_ready = True\\n self.neighbors[name].send_timer = time.time()\",\n \"def handle_update(self, call):\\n self.fire_event(EVENT_UPDATE)\",\n \"def update(self,data):\\r\\n data = data.split(':',1)\\r\\n\\r\\n self.log('Signal','Received an update: %s...' % repr(data)[0:10],'update')\\r\\n \\r\\n #print \\\"*** local: \\\" + repr(data)\\r\\n \\r\\n if data[0] == 'Results':\\r\\n\\r\\n self.log('Signal','The local node returned these passwords: %s' % repr(data[1]),'update')\\r\\n\\r\\n self.addResult(data[1])\\r\\n elif data[0] == 'Bench':\\r\\n self.log('Signal','The local node returned these benches: %s' % repr(data[1]),'update')\\r\\n \\r\\n self.addBench(data[1])\\r\\n\\r\\n elif data[0] == 'Work':\\r\\n if data[1] == 'Done':\\r\\n self.finished += 1\\r\\n if self.finished >= len(self.nodes):\\r\\n self.runningWork = False\\r\\n self.log('Signal','Finished working','update')\\r\\n\\r\\n notification = 'Work:Done'\\r\\n self.notifyObservers(notification)\",\n \"def port_update_end(self, payload):\\n port = DictModel(payload['port'])\\n network = self.cache.get_network_by_id(port.network_id)\\n if network:\\n self.cache.put_port(port)\\n self.call_driver('reload_allocations', network)\",\n \"def process_employee_exit(self):\\n if self.is_employee_serving():\\n self._end_date.append(datetime.now().isoformat())\\n\\n print(f\\\"Successfully processed exit for employee {self.name} on\\\" \\\\\\n f\\\"{self._end_date[-1]}\\\\nWe wish {self.name} for future endeavours\\\")\\n return\\n raise RejoiningException(\\\"Employee not in service. Cannot process exit.\\\")\",\n \"def end(self) -> None:\",\n \"def on_epoch_end(self):\\n pass\",\n \"async def on_close(self) -> None:\\n for device_id in self.devices:\\n is_reachable: bool = False\\n async_dispatcher_send(\\n self.hass,\\n f\\\"{LIVISI_REACHABILITY_CHANGE}_{device_id}\\\",\\n is_reachable,\\n )\\n\\n await self.websocket.connect(self.on_data, self.on_close, self.port)\",\n \"def update_callback(self, output_id=0):\\n if output_id == 0 | output_id == self._output_id:\\n _LOGGER.info(\\\"SmartVideoHub sent a status update for output %i\\\", output_id)\\n self.update()\\n self.schedule_update_ha_state(False)\",\n \"def end(self):\\n self.shutdown = True\",\n \"def RequestHandler_finish(self):\\n if self.request._oboe_finish_ev and self.request._oboe_ctx and self.request._oboe_ctx.is_valid():\\n ev = self.request._oboe_finish_ev\\n ctx = self.request._oboe_ctx\\n if hasattr(self, 'get_status'): # recent Tornado\\n ev.add_info(\\\"Status\\\", self.get_status())\\n elif hasattr(self, '_status_code'): # older Tornado\\n ev.add_info(\\\"Status\\\", self._status_code)\\n\\n ev.add_edge(oboe.Context.get_default())\\n ctx.report(ev)\\n\\n # clear the stored oboe event/metadata from the request object\\n self.request._oboe_ctx = None\\n self.request._oboe_finish_ev = None\",\n \"def keepAliveReceived(self):\",\n \"def end_of_level(self):\\n Clock.unschedule(self.update)\\n self.no_events()\",\n \"def check_track_end_event():\\n global check_track_end_id\\n global TRACK_END\\n\\n if track_status.get() == \\\"(Playing)\\\":\\n\\n # If we reach the end of the track, initialize the player\\n for event in pygame.event.get():\\n if event.type == TRACK_END:\\n cancel_track_end_event_loop()\\n cancel_update_play_time_loop()\\n # Displaying Status\\n track_status.set(\\\"(Stopped)\\\")\\n reset_track()\\n play_next()\\n return\\n\\n check_track_end_id = root.after(100, check_track_end_event)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64979863","0.63618815","0.6354132","0.6308048","0.6289232","0.61400086","0.6041612","0.60274327","0.59674954","0.5949109","0.5926635","0.58869374","0.58779126","0.5860421","0.5796766","0.5758815","0.57569045","0.57188445","0.5713383","0.5688551","0.5639454","0.56261617","0.5614047","0.5610805","0.5579579","0.55723536","0.5565737","0.5553914","0.5553683","0.55516136","0.55378985","0.55308616","0.5527309","0.5524716","0.5523552","0.5522813","0.5520602","0.5518598","0.5514556","0.5513191","0.5493561","0.5490674","0.54806864","0.5479189","0.54783887","0.5474353","0.54729","0.54684716","0.54673254","0.5465557","0.5460218","0.5454656","0.5454656","0.54488057","0.5447819","0.54457766","0.5403447","0.53965867","0.5392311","0.5392162","0.53836817","0.53836817","0.53836817","0.5378836","0.53715086","0.53664166","0.5365508","0.5360858","0.53518903","0.5347234","0.5340778","0.5332184","0.532998","0.53234285","0.5323044","0.5323044","0.5319764","0.531318","0.5310781","0.53069144","0.53056407","0.52992946","0.52988523","0.52958894","0.5290659","0.52893335","0.52886605","0.5286243","0.5284552","0.5277111","0.5275109","0.5274437","0.5274289","0.5268004","0.5266217","0.52658784","0.5263626","0.5261985","0.5260216","0.52535564"],"string":"[\n \"0.64979863\",\n \"0.63618815\",\n \"0.6354132\",\n \"0.6308048\",\n \"0.6289232\",\n \"0.61400086\",\n \"0.6041612\",\n \"0.60274327\",\n \"0.59674954\",\n \"0.5949109\",\n \"0.5926635\",\n \"0.58869374\",\n \"0.58779126\",\n \"0.5860421\",\n \"0.5796766\",\n \"0.5758815\",\n \"0.57569045\",\n \"0.57188445\",\n \"0.5713383\",\n \"0.5688551\",\n \"0.5639454\",\n \"0.56261617\",\n \"0.5614047\",\n \"0.5610805\",\n \"0.5579579\",\n \"0.55723536\",\n \"0.5565737\",\n \"0.5553914\",\n \"0.5553683\",\n \"0.55516136\",\n \"0.55378985\",\n \"0.55308616\",\n \"0.5527309\",\n \"0.5524716\",\n \"0.5523552\",\n \"0.5522813\",\n \"0.5520602\",\n \"0.5518598\",\n \"0.5514556\",\n \"0.5513191\",\n \"0.5493561\",\n \"0.5490674\",\n \"0.54806864\",\n \"0.5479189\",\n \"0.54783887\",\n \"0.5474353\",\n \"0.54729\",\n \"0.54684716\",\n \"0.54673254\",\n \"0.5465557\",\n \"0.5460218\",\n \"0.5454656\",\n \"0.5454656\",\n \"0.54488057\",\n \"0.5447819\",\n \"0.54457766\",\n \"0.5403447\",\n \"0.53965867\",\n \"0.5392311\",\n \"0.5392162\",\n \"0.53836817\",\n \"0.53836817\",\n \"0.53836817\",\n \"0.5378836\",\n \"0.53715086\",\n \"0.53664166\",\n \"0.5365508\",\n \"0.5360858\",\n \"0.53518903\",\n \"0.5347234\",\n \"0.5340778\",\n \"0.5332184\",\n \"0.532998\",\n \"0.53234285\",\n \"0.5323044\",\n \"0.5323044\",\n \"0.5319764\",\n \"0.531318\",\n \"0.5310781\",\n \"0.53069144\",\n \"0.53056407\",\n \"0.52992946\",\n \"0.52988523\",\n \"0.52958894\",\n \"0.5290659\",\n \"0.52893335\",\n \"0.52886605\",\n \"0.5286243\",\n \"0.5284552\",\n \"0.5277111\",\n \"0.5275109\",\n \"0.5274437\",\n \"0.5274289\",\n \"0.5268004\",\n \"0.5266217\",\n \"0.52658784\",\n \"0.5263626\",\n \"0.5261985\",\n \"0.5260216\",\n \"0.52535564\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6571846"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79792,"cells":{"query":{"kind":"string","value":"Handle the network.delete.end notification event."},"document":{"kind":"string","value":"def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def process_IN_DELETE(self, event):","def _notify_delete(self, cuds_object):","def on_deleted(self, event):\n\n # the absolute path of the event file/folder\n abs_path = event.src_path\n # replace the root path with a '.' to build a relative path to be sent to server\n relative_event_path = abs_path.replace(self.root_path, \".\")\n\n # retrieve event type and the flag for directory/folder\n event_type = event.event_type\n is_directory = event.is_directory\n\n # only propagate changes if there is a connection with the server\n if self.protocol.connected:\n self.protocol.send_event(event_type, is_directory, relative_event_path)\n else:\n logging.info(\"Connection with server has not been established, 'delete' changes will not be propagated.\")","def on_delete(self, payload):\n pass","def delete_callback(self):\n pass","def process_IN_DELETE_SELF(self, event):","def link_delete_callback(self):\n pass","async def on_disconnect(self) -> None:","def on_disconnect(self, raw_msg, server, port, **kwargs):","def dcnm_network_delete_event(self, network_info):\n seg_id = network_info.get('segmentation_id')\n if not seg_id:\n LOG.error(_LE('Failed to delete network. Invalid network '\n 'info %s.'), network_info)\n query_net = self.get_network_by_segid(seg_id)\n if not query_net:\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\n 'does not exist.'), {'segid': seg_id})\n return\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\n LOG.info(_LI(\"Service network %s, returning\"), query_net.name)\n return\n # Send network delete request to neutron\n try:\n del_net = self.network.pop(query_net.network_id)\n self.neutronclient.delete_network(query_net.network_id)\n self.delete_network_db(query_net.network_id)\n except Exception as exc:\n # Failed to delete network.\n # Put back the entry to the local cache???\n self.network[query_net.network_id] = del_net\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\n '%(network)s. Reason %(err)s.'),\n {'network': query_net.name, 'err': str(exc)})","def m_ts_OrderDeleted(self, sender, e):\r\n print(\"Order was deleted.\")","def on_disconnect(mqttc, obj, rc):\n if rc == 0:\n logger.debug(\"MQTT DISCONNECTED: rc: \" + str(rc))\n logger.debug(\"Disconnected Successfully from MQTT Broker\")","async def delete(self, delete: TPayload) -> None:","def on_delete(self, status_id, user_id):\n log.debug(\"Received status deletion notice: %d\", status_id)","def subnet_delete_end(self, payload):\n subnet_id = payload['subnet_id']\n network = self.cache.get_network_by_subnet_id(subnet_id)\n if network:\n self.refresh_dhcp_helper(network.id)","def endMessage(self):","def on_disconnect( client, userdata, rc ):\n logging.info( \"Disconnected from Broker. Returned code: %s\\n\" %rc )\n client.connected_flag = False\n client.disconnect_flag = True","def after_delete(self, obj, st):\n pass","def on_disconnect(self, mqtt_client, userdata, rc):\n logging.debug('DEBUG - Disconnected from broker')","def on_disconnect(client, userdata, rc):\n if rc != 0:\n print(\"Error: Mqtt server disconnection.\")","def on_disconnect(self):\n # self.node_worker_ref.socketIO.disconnect()\n self.log.info('Disconnected from the server')","def on_disconnect(self):\n print('Client disconnected!')","def onMessageEnd(self):","def post_destroy(self) -> Any:\n raise NotImplementedError","def on_deleted(self, event):\n\t\tfileInfo = event.src_path.split('/')\n\n\t\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]","def delete(event, context):\n\n if type(event) == str:\n event = json.loads(event)\n\n for record in event['Records']:\n data = json.loads(record['Sns']['Message'])\n\n item = table.get_item(\n Key={\n 'id':data['id']\n }\n )\n\n if 'Item' in item:\n result = table.update_item(\n Key={ 'id': data['id']},\n ExpressionAttributeNames={\n '#is_deleted': 'deleted'\n },\n ExpressionAttributeValues={\n ':delete_flg': True,\n ':updatedAt': str(dt.datetime.utcnow())\n },\n UpdateExpression='SET #is_deleted = :delete_flg, modifiedAt = :updatedAt',\n ReturnValues='ALL_NEW'\n )\n\n\n response = {\n 'statusCode': 200\n }\n\n return response","def _on_disconnect(self, *args, **kwargs):\n logger.info('Client disconnected %r', self.address)\n self.server.client_disconnected(self)\n # self.unregister()","def handle_delete_response(self, response):\n\n self.handle_response(response)","async def on_cancel(self, payload):\n\n await self._delete_message(0)\n self.stop()","def delete(self):\n\t\tself.eventStatus = 0\n\t\tself.save()\n\n\t\t# DEBUG\n\t\tcontext = {\n\t\t\t'call_stack': ''.join(traceback.format_stack()),\n\t\t\t'event': self,\n\t\t\t'server_addr': settings.SERVER_ADDRESS,\n\t\t}\n\t\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\n\t\tmail_admins(_('Event Deletion Attempt!'), body)","def notify_end(self, status, objective):\n pass # pragma: no cover","def on_delete(key):\n pass","def delete(self, downstream = None):\n # emit delete message\n self.deleteSignal.emit(self)","def on_delete(self, status_id, user_id):\n #print \"Delete notice for %s. %s\" % (status_id, user_id)\n return","def flow_removed_handler(self, ev):\n msg = ev.msg\n datapath = msg.datapath\n ofp = datapath.ofproto\n if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n reason = 'IDLE TIMEOUT'\n elif msg.reason == ofp.OFPRR_HARD_TIMEOUT:\n reason = 'HARD TIMEOUT'\n elif msg.reason == ofp.OFPRR_DELETE:\n reason = 'DELETE'\n elif msg.reason == ofp.OFPRR_GROUP_DELETE:\n reason = 'GROUP DELETE'\n else:\n reason = 'unknown'\n self.logger.info('Flow removed msg '\n 'cookie=%d priority=%d reason=%s table_id=%d '\n 'duration_sec=%d '\n 'idle_timeout=%d hard_timeout=%d '\n 'packets=%d bytes=%d match=%s',\n msg.cookie, msg.priority, reason, msg.table_id,\n msg.duration_sec,\n msg.idle_timeout, msg.hard_timeout,\n msg.packet_count, msg.byte_count, msg.match)\n # Is it a MAC learning suppression FE idle timeout?\n if msg.table_id == self.ft_iim and \\\n msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n switch = self.switches[datapath.id]\n #*** Extract the MAC from the match:\n mac = msg.match['eth_src']\n in_port = msg.match['in_port']\n #*** TBD, deal with context:\n context = self.context_default\n #*** Call method to delete FEs:\n switch.mactable.delete(mac, in_port, context)","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\n if rc == mqtt.MQTT_ERR_SUCCESS:\n logging.info(\n f\"Successfully disconnected from the broker {self.config.broker}\"\n )\n sys.exit(0)\n else:\n logging.critical(\n f\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\"\n )\n sys.exit(1)","def on_terminate(self):\n pass","def on_connection_closed(self):","def handle_delf_conf(self, job):\n print(\"User deleted a file.\")\n # Delete the file from all servers\n self.put_job_in_all_queues(job)\n\n list_job_results = self.get_internal_results_from_all_servers()\n\n if len(list_job_results) == 0:\n # We got no responses back, there are probably no servers active\n self.put_external_result(self.generate_failure_job(\"Unsuccessful, no servers responded\"))\n return\n\n # Check all the servers had success\n for result in list_job_results:\n if result.result[\"outcome\"] != \"success\":\n self.put_external_result(\n self.generate_failure_job(\"Unsuccessful, one of the servers did not have success\"))\n return\n print(\"All servers deleted file '{}'.\".format(job.data[\"file_name\"]))\n\n # Tell the client we successfully uploaded to all servers\n response_result = copy.deepcopy(list_job_results[0])\n response_result.processed_by = None\n self.put_external_result(response_result)","def callback_disconnect():\n # if Networking.get_instance().is_host:\n logger.warning(\"It seems that client is not connected...\")\n Networking.get_instance().disconnect()\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))","def on_del(self, callback):\n self._del_callback = callback if callable(callback) else _void","def on_del(self, callback):\n self._del_callback = callback if callable(callback) else _void","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","async def delete(self, *, reason: Optional[Any] = ...):\n ...","def feed_entry_post_delete(sender, instance, **kwargs):\n del kwargs\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\n try:\n if instance.command_group and instance.command_group.id is not None:\n instance.command_group.delete()\n except CommandGroupEntry.DoesNotExist:\n pass\n\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)","def delete_network_postcommit(self, context):\n if self.rpc_handler is None:\n return\n network = self._get_network_info(context._network)\n for _, _network in network.items():\n network_type = _network.get('network_type', '')\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\n return\n if network is not None:\n try:\n self.rpc_handler.delete_network(network)\n except:\n pass","async def send_delete(self, timeout, message, *args, **kwargs):\n msg = await self.send(message, *args, **kwargs)\n await self.delay(timeout, self.delete_message, msg)","def finished(self, reply):\n pass","def device_disconnect(self):\n pass","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def event_delete(req):\n event_id = req.match_dict['event_id']\n try:\n db_conn.event_delete(event_id)\n json = {'deleted': True}\n except Exception as e:\n json = {'errors': [str(e)]}\n return req.Response(json=json)","def delete_event(self,widget=None):\n self.on_device_dialog_cancel_clicked()\n return True","def _on_mqtt_disconnect(\n self, client: mqtt.Client, userdata: str, rc: int\n ) -> None:\n self.log.debug(f\"Disconnect return code: {rc}\")\n self._connected = False\n if rc != 0:\n self.log.warning(\n f\"Unexpected disconnect on {self.host}:{self.port}!\"\n )","def delete_event(self, widget, event, data=None): \r\n\t\t# XXX: Are you sure you want to quit?\r\n\t\tpreferences.save()\r\n\t\treturn False","def on_disconnect(self, client, userdata, msg):\n self.log.warning(\"Disconnected: \" + str(msg))\n self.connected = False\n self.dconn += 1","def on_close(self):\n # remove callback so it doesn't get called in the future\n self._notifier.remove_callback(self.process_notification)","def action_delete():\n try:\n deleted = delete_notification()\n except:\n raise HTTPResponse(body=\"Unexpected error\", status=400)\n \n if deleted:\n return dict(msg=\"Notification deleted\")\n else:\n return dict(msg=\"No notification to delete\")","def listen_del(self, f):\n self._coms.register_leaving_callback(f)","def delete_event(self, widget, event, Data=None):\n\t\tself.todolist.save()\n\t\tgtk.main_quit()\n\t\treturn False","def do_DELETE(self):\n global data_store\n logger.info(\"Received a DELETE request: {}\".format(self.path))\n data = self.parse_data()\n # Data should be the key name\n logger.info(\"Data received {}: {}\".format(type(data), data))\n path = self.strip_path()\n if path == SET_OP:\n logger.info(\"Running delete\")\n data_store.delete_data(data)\n response_code = 200\n else:\n logger.error(\"Invalid DELETE operation {} was received.\".format(path))\n response_code = 404\n self.send_response(response_code)","def onShutdown(self, connection:MQTTConnection) -> None:","async def end_object(self):","async def delete(self):\n return await self.set_message(text='')","def network_delete_event(self, network_info):\n\n net_id = network_info['network_id']\n if net_id not in self.network:\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\n net_id)\n return\n\n segid = self.network[net_id].get('segmentation_id')\n tenant_id = self.network[net_id].get('tenant_id')\n tenant_name = self.get_project_name(tenant_id)\n net = utils.Dict2Obj(self.network[net_id])\n if not tenant_name:\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\n {'tenant_id': tenant_id})\n self.update_network_db(net.id, constants.DELETE_FAIL)\n return\n\n try:\n self.dcnm_client.delete_network(tenant_name, net)\n # Put back the segmentation id into the pool.\n self.seg_drvr.release_segmentation_id(segid)\n\n # Remove entry from database and cache.\n self.delete_network_db(net_id)\n del self.network[net_id]\n snets = [k for k in self.subnet if (\n self.subnet[k].get('network_id') == net_id)]\n [self.subnet.pop(s) for s in snets]\n except dexc.DfaClientRequestFailed:\n LOG.error(_LE('Failed to create network %(net)s.'),\n {'net': net.name})\n self.update_network_db(net_id, constants.DELETE_FAIL)\n # deleting all related VMs\n instances = self.get_vms()\n instances_related = [k for k in instances if k.network_id == net_id]\n for vm in instances_related:\n LOG.debug(\"deleting vm %s because network is deleted\", vm.name)\n self.delete_vm_function(vm.port_id, vm)\n self.network_del_notif(tenant_id, tenant_name, net_id)","def process_quit(message):\n try:\n Resident.objects.get(phone_number=message.sender).delete()\n except Resident.DoesNotExist:\n pass\n \n # TODO - wording...\n message.respond('You have been removed from our system and will no longer get text messages.')\n \n return TropoOkResponse()","def on_disconnect():\n print(\"User disconnected!\")","def on_delete(self, req, resp):\n try:\n days_to_retain = int(req.params[\"days\"])\n except Exception:\n days_to_retain = 90\n\n try:\n retention_status = self.state_manager.task_retention(\n retain_days=str(days_to_retain))\n if not retention_status:\n resp.status = falcon.HTTP_404\n return\n resp.text = \"Tables purged successfully.\"\n except Exception as e:\n self.error(req.context, \"Unknown error: %s\" % (str(e)))\n resp.text = \"Unexpected error.\"\n resp.status = falcon.HTTP_500\n return\n resp.status = falcon.HTTP_200","async def disconnect(self):","def after_delete(self, record):\n debug = logging.getLogger(__name__).debug\n debug('deleted group %r (%r)', record['name'], record['group_id'])\n audit('delete group', record['name'])","def on_deleted(self, event):\n super(myEventHandler,self).on_deleted(event)\n #print \"Removed: \" + event.src_path\n if self.onedir.cookies is None or not self.onedir.autosyncstatus():\n return\n\n source = event.src_path\n if event.is_directory:\n try:\n pathtoonedir = self.onedir.getonedirrectory()\n relpath = source.replace(pathtoonedir ,\"\")\n self.onedir.deldirectory(relpath)\n except Exception as e:\n print \"Error syncing directory\" + e\n exit(1)\n else:\n try:\n #use os.path.split to get file name and path\n splitpath = split(source)\n file = splitpath[1]\n pathtoonedir = self.onedir.getonedirrectory()\n #print pathtoonedir\n #print \"truncated path:\"\n relpath = splitpath[0].replace(pathtoonedir ,\"\")\n self.onedir.deletefile(file, relpath)\n except OSError as e:\n print \"Error copying file! \" + e.strerror\n exit(1)\n except IOError as e:\n print \"IOerror creating file \" + e.strerror\n exit(1)","def handler(self):\n\t\tself.exitClient()","def __on_group_deleted(self, logger, *args):","async def async_device_removed(event):\n if event.data[\"action\"] != \"remove\":\n return\n await async_remove_automations(hass, event.data[\"device_id\"])","def finish(self):\n global log_th\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def DeleteLayer(self, event):\n pass","def on_disconnect(self):\n log.info(\"Stream disconnected\")","def finalize_pending(self, data):\n if self.request.method.upper() == 'DELETE':\n data.delete()","async def deleted(self, value):\n pass","def on_disconnect(unused_client, unused_userdata, rc):\n\tprint('on_disconnect', error_str(rc))\n\n\t# Since a disconnect occurred, the next loop iteration will wait with\n\t# exponential backoff.\n\tglobal should_backoff\n\tshould_backoff = True","def handle_disconnect(self,message,conn):\n if self.connections[message[\"ID\"]][\"host\"] == True: #if user is host, delete all connections\n print(\"HOST DISCONNECTING\")\n host = message[\"ID\"]\n session_location = self.connections[message[\"ID\"]][\"session_id\"]\n for key in self.sessions[session_location][\"USERS\"].keys():\n self.disconnect(self.connections[key][\"CONN\"],message[\"ID\"],f\"[HOST <{host}>:<{self.connections[host]['display_name']}>] Unexpectedly Disconnected\") #close each connection\n self.delete_connection_entry(key) #delete the connection from the connections dictionary\n self.delete_session(session_location) # delete the session\n self.delete_connection_entry(message[\"ID\"])\n self.disconnect(conn,message[\"ID\"], \"You Disconnected\")\n else:\n session_location = self.connections[message[\"ID\"]][\"session_id\"]\n self.delete_session_entry(session_location,message[\"ID\"])\n self.broadcast_to_session(session_location, \"USER_DISCONNECT\",f\"[USER <{message['ID']}>:<{self.connections[message['ID']]['display_name']}>] Disconnected\", exclude=[message[\"ID\"]])\n self.delete_connection_entry(message[\"ID\"])\n self.broadcast_to_session(session_location, \"USERS\", json.dumps(self.sessions[session_location][\"USERS\"]))\n self.disconnect(conn,message[\"ID\"],\"You Disconnected\")","def delete_handler(event, context):\n delete_endpoint_config(event)","def delete(self): \n params = {'command':'deleteNetwork',\n 'id':self.id}\n \n self.logger.debug('Remove network %s' % self.name)\n \n try:\n response = self.send_request(params)\n res = json.loads(response)\n clsk_job_id = res['deletenetworkresponse']['jobid']\n self.logger.debug('Start job over %s.%s - %s: %s' % (\n self._obj_type, self.name, \n 'deleteNetwork', res))\n return clsk_job_id\n except KeyError as ex :\n self.logger.error('Error parsing json data: %s' % ex)\n raise ClskError('Error parsing json data: %s' % ex)\n except ApiError as ex:\n self.logger.error(ex)\n raise ClskError(ex)","async def on_message_delete(self, msg: Message):\n\n if not self._is_tracked(msg.guild, EventPriority.delete):\n return\n\n modlog_channels = [int(channel_id) for channel_id in self.get_guild_config(msg.guild).values()]\n\n # If message deleted from modlog, record event with header only\n if msg.channel.id in modlog_channels:\n\n description = f\"\\n\\n{msg.embeds[0].description}\" if msg.embeds else \"\"\n description = escape_markdown(description.replace(\"Modlog message deleted\\n\\n\", \"\"))\n\n em = self.em_base(\n msg.author,\n f\"Modlog message deleted{description}\",\n EventColors.delete.value\n )\n\n return await self.log_event(em, msg.guild, EventPriority.delete)\n\n # Otherwise, ignore bot's deleted embed-only (help pages, et.) messages\n elif msg.author.id == self.bot.user.id and not msg.content:\n return\n\n em = self.em_base(\n msg.author,\n f\"Message by {msg.author.mention} ({msg.author.name}) deleted\",\n EventColors.delete.value\n )\n\n em.description = f\"{em.description}\\n\\nChannel: {msg.channel.mention} ({msg.channel.name})\"\n\n if msg.content:\n chunks = [msg.content[i:i + 1024] for i in range(0, len(msg.content), 1024)]\n for i, chunk in enumerate(chunks):\n em.add_field(\n name=f\"🗑 Content [{i + 1}/{len(chunks)}]\",\n value=chunk\n )\n\n else:\n em.add_field(\n name=\"🗑 Content [0/0]\",\n value=\"Message had no content\"\n )\n\n # Try to re-download attached images if possible. The proxy url doesn't 404 immediately unlike the\n # regular URL, so it may be possible to download from it before it goes down as well.\n reupload = None\n\n if msg.attachments:\n temp_image = BytesIO()\n attachment = msg.attachments[0]\n if attachment.size > 5000000:\n # caching is important and all, but this will just cause more harm than good\n return\n\n try:\n await download_image(msg.attachments[0].proxy_url, temp_image)\n reupload = File(temp_image, filename=\"reupload.{}\".format(attachment.filename))\n\n em.description = f\"{em.description}\\n\\n**Attachment Included Above**\"\n\n except Exception as error:\n await self.errorlog.send(error)\n reupload = None\n\n em.description = f\"{em.description}\\n\\n**Attachment Reupload Failed (See Error Log)**\"\n\n await self.log_event(em, msg.guild, priority=EventPriority.delete, file=reupload)","def stopped(self, event, source):\n\n self.log('Disconnecting IRC bot')\n self.fire(disconnect())","async def _async_cast_removed(self, discover: ChromecastInfo) -> None:","def DeleteEvents(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def deserialize_guild_channel_delete_event(\n self, shard: gateway_shard.GatewayShard, payload: data_binding.JSONObject\n ) -> channel_events.GuildChannelDeleteEvent:","def visit_graph_end(self, network: Network):\n pass","def on_disconnect(self, client, userdata, rc):\n\t\tprint (\"[{}] Client disconnected\".format(\n\t\t\tint(time.time())\n\t\t))","def _objectDeleted(self, obj):\n pass","def delete(self, data):\r\n pass","def _disconnect(self):\n self.socket.send_json({\"worker_id\": self.socket_id, \"message\": \"disconnect\"})\n self.socket.close()\n self.context.term()\n exit()","def on_disconnect():\n print('User disconnected!')\n return 'disconnected'","def on_disconnect():\n logger.info(f\"{request.sid} Disconnected\")","def delete():","def on_quit(self, raw_msg, source, **kwargs):","def handleClose(self):\n logging.info(\"%s %s\", self.address, \"closed\")\n self.logbook.clients_disconnected_count += 1","def __del__(self):\n self.logger.info(\"Finishing\")\n self.__disconnect()"],"string":"[\n \"def process_IN_DELETE(self, event):\",\n \"def _notify_delete(self, cuds_object):\",\n \"def on_deleted(self, event):\\n\\n # the absolute path of the event file/folder\\n abs_path = event.src_path\\n # replace the root path with a '.' to build a relative path to be sent to server\\n relative_event_path = abs_path.replace(self.root_path, \\\".\\\")\\n\\n # retrieve event type and the flag for directory/folder\\n event_type = event.event_type\\n is_directory = event.is_directory\\n\\n # only propagate changes if there is a connection with the server\\n if self.protocol.connected:\\n self.protocol.send_event(event_type, is_directory, relative_event_path)\\n else:\\n logging.info(\\\"Connection with server has not been established, 'delete' changes will not be propagated.\\\")\",\n \"def on_delete(self, payload):\\n pass\",\n \"def delete_callback(self):\\n pass\",\n \"def process_IN_DELETE_SELF(self, event):\",\n \"def link_delete_callback(self):\\n pass\",\n \"async def on_disconnect(self) -> None:\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def dcnm_network_delete_event(self, network_info):\\n seg_id = network_info.get('segmentation_id')\\n if not seg_id:\\n LOG.error(_LE('Failed to delete network. Invalid network '\\n 'info %s.'), network_info)\\n query_net = self.get_network_by_segid(seg_id)\\n if not query_net:\\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\\n 'does not exist.'), {'segid': seg_id})\\n return\\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\\n LOG.info(_LI(\\\"Service network %s, returning\\\"), query_net.name)\\n return\\n # Send network delete request to neutron\\n try:\\n del_net = self.network.pop(query_net.network_id)\\n self.neutronclient.delete_network(query_net.network_id)\\n self.delete_network_db(query_net.network_id)\\n except Exception as exc:\\n # Failed to delete network.\\n # Put back the entry to the local cache???\\n self.network[query_net.network_id] = del_net\\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\\n '%(network)s. Reason %(err)s.'),\\n {'network': query_net.name, 'err': str(exc)})\",\n \"def m_ts_OrderDeleted(self, sender, e):\\r\\n print(\\\"Order was deleted.\\\")\",\n \"def on_disconnect(mqttc, obj, rc):\\n if rc == 0:\\n logger.debug(\\\"MQTT DISCONNECTED: rc: \\\" + str(rc))\\n logger.debug(\\\"Disconnected Successfully from MQTT Broker\\\")\",\n \"async def delete(self, delete: TPayload) -> None:\",\n \"def on_delete(self, status_id, user_id):\\n log.debug(\\\"Received status deletion notice: %d\\\", status_id)\",\n \"def subnet_delete_end(self, payload):\\n subnet_id = payload['subnet_id']\\n network = self.cache.get_network_by_subnet_id(subnet_id)\\n if network:\\n self.refresh_dhcp_helper(network.id)\",\n \"def endMessage(self):\",\n \"def on_disconnect( client, userdata, rc ):\\n logging.info( \\\"Disconnected from Broker. Returned code: %s\\\\n\\\" %rc )\\n client.connected_flag = False\\n client.disconnect_flag = True\",\n \"def after_delete(self, obj, st):\\n pass\",\n \"def on_disconnect(self, mqtt_client, userdata, rc):\\n logging.debug('DEBUG - Disconnected from broker')\",\n \"def on_disconnect(client, userdata, rc):\\n if rc != 0:\\n print(\\\"Error: Mqtt server disconnection.\\\")\",\n \"def on_disconnect(self):\\n # self.node_worker_ref.socketIO.disconnect()\\n self.log.info('Disconnected from the server')\",\n \"def on_disconnect(self):\\n print('Client disconnected!')\",\n \"def onMessageEnd(self):\",\n \"def post_destroy(self) -> Any:\\n raise NotImplementedError\",\n \"def on_deleted(self, event):\\n\\t\\tfileInfo = event.src_path.split('/')\\n\\n\\t\\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]\",\n \"def delete(event, context):\\n\\n if type(event) == str:\\n event = json.loads(event)\\n\\n for record in event['Records']:\\n data = json.loads(record['Sns']['Message'])\\n\\n item = table.get_item(\\n Key={\\n 'id':data['id']\\n }\\n )\\n\\n if 'Item' in item:\\n result = table.update_item(\\n Key={ 'id': data['id']},\\n ExpressionAttributeNames={\\n '#is_deleted': 'deleted'\\n },\\n ExpressionAttributeValues={\\n ':delete_flg': True,\\n ':updatedAt': str(dt.datetime.utcnow())\\n },\\n UpdateExpression='SET #is_deleted = :delete_flg, modifiedAt = :updatedAt',\\n ReturnValues='ALL_NEW'\\n )\\n\\n\\n response = {\\n 'statusCode': 200\\n }\\n\\n return response\",\n \"def _on_disconnect(self, *args, **kwargs):\\n logger.info('Client disconnected %r', self.address)\\n self.server.client_disconnected(self)\\n # self.unregister()\",\n \"def handle_delete_response(self, response):\\n\\n self.handle_response(response)\",\n \"async def on_cancel(self, payload):\\n\\n await self._delete_message(0)\\n self.stop()\",\n \"def delete(self):\\n\\t\\tself.eventStatus = 0\\n\\t\\tself.save()\\n\\n\\t\\t# DEBUG\\n\\t\\tcontext = {\\n\\t\\t\\t'call_stack': ''.join(traceback.format_stack()),\\n\\t\\t\\t'event': self,\\n\\t\\t\\t'server_addr': settings.SERVER_ADDRESS,\\n\\t\\t}\\n\\t\\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\\n\\t\\tmail_admins(_('Event Deletion Attempt!'), body)\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def on_delete(key):\\n pass\",\n \"def delete(self, downstream = None):\\n # emit delete message\\n self.deleteSignal.emit(self)\",\n \"def on_delete(self, status_id, user_id):\\n #print \\\"Delete notice for %s. %s\\\" % (status_id, user_id)\\n return\",\n \"def flow_removed_handler(self, ev):\\n msg = ev.msg\\n datapath = msg.datapath\\n ofp = datapath.ofproto\\n if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\\n reason = 'IDLE TIMEOUT'\\n elif msg.reason == ofp.OFPRR_HARD_TIMEOUT:\\n reason = 'HARD TIMEOUT'\\n elif msg.reason == ofp.OFPRR_DELETE:\\n reason = 'DELETE'\\n elif msg.reason == ofp.OFPRR_GROUP_DELETE:\\n reason = 'GROUP DELETE'\\n else:\\n reason = 'unknown'\\n self.logger.info('Flow removed msg '\\n 'cookie=%d priority=%d reason=%s table_id=%d '\\n 'duration_sec=%d '\\n 'idle_timeout=%d hard_timeout=%d '\\n 'packets=%d bytes=%d match=%s',\\n msg.cookie, msg.priority, reason, msg.table_id,\\n msg.duration_sec,\\n msg.idle_timeout, msg.hard_timeout,\\n msg.packet_count, msg.byte_count, msg.match)\\n # Is it a MAC learning suppression FE idle timeout?\\n if msg.table_id == self.ft_iim and \\\\\\n msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\\n switch = self.switches[datapath.id]\\n #*** Extract the MAC from the match:\\n mac = msg.match['eth_src']\\n in_port = msg.match['in_port']\\n #*** TBD, deal with context:\\n context = self.context_default\\n #*** Call method to delete FEs:\\n switch.mactable.delete(mac, in_port, context)\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\\n if rc == mqtt.MQTT_ERR_SUCCESS:\\n logging.info(\\n f\\\"Successfully disconnected from the broker {self.config.broker}\\\"\\n )\\n sys.exit(0)\\n else:\\n logging.critical(\\n f\\\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\\\"\\n )\\n sys.exit(1)\",\n \"def on_terminate(self):\\n pass\",\n \"def on_connection_closed(self):\",\n \"def handle_delf_conf(self, job):\\n print(\\\"User deleted a file.\\\")\\n # Delete the file from all servers\\n self.put_job_in_all_queues(job)\\n\\n list_job_results = self.get_internal_results_from_all_servers()\\n\\n if len(list_job_results) == 0:\\n # We got no responses back, there are probably no servers active\\n self.put_external_result(self.generate_failure_job(\\\"Unsuccessful, no servers responded\\\"))\\n return\\n\\n # Check all the servers had success\\n for result in list_job_results:\\n if result.result[\\\"outcome\\\"] != \\\"success\\\":\\n self.put_external_result(\\n self.generate_failure_job(\\\"Unsuccessful, one of the servers did not have success\\\"))\\n return\\n print(\\\"All servers deleted file '{}'.\\\".format(job.data[\\\"file_name\\\"]))\\n\\n # Tell the client we successfully uploaded to all servers\\n response_result = copy.deepcopy(list_job_results[0])\\n response_result.processed_by = None\\n self.put_external_result(response_result)\",\n \"def callback_disconnect():\\n # if Networking.get_instance().is_host:\\n logger.warning(\\\"It seems that client is not connected...\\\")\\n Networking.get_instance().disconnect()\\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))\",\n \"def on_del(self, callback):\\n self._del_callback = callback if callable(callback) else _void\",\n \"def on_del(self, callback):\\n self._del_callback = callback if callable(callback) else _void\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"async def delete(self, *, reason: Optional[Any] = ...):\\n ...\",\n \"def feed_entry_post_delete(sender, instance, **kwargs):\\n del kwargs\\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\\n try:\\n if instance.command_group and instance.command_group.id is not None:\\n instance.command_group.delete()\\n except CommandGroupEntry.DoesNotExist:\\n pass\\n\\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)\",\n \"def delete_network_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n network = self._get_network_info(context._network)\\n for _, _network in network.items():\\n network_type = _network.get('network_type', '')\\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\\n return\\n if network is not None:\\n try:\\n self.rpc_handler.delete_network(network)\\n except:\\n pass\",\n \"async def send_delete(self, timeout, message, *args, **kwargs):\\n msg = await self.send(message, *args, **kwargs)\\n await self.delay(timeout, self.delete_message, msg)\",\n \"def finished(self, reply):\\n pass\",\n \"def device_disconnect(self):\\n pass\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def event_delete(req):\\n event_id = req.match_dict['event_id']\\n try:\\n db_conn.event_delete(event_id)\\n json = {'deleted': True}\\n except Exception as e:\\n json = {'errors': [str(e)]}\\n return req.Response(json=json)\",\n \"def delete_event(self,widget=None):\\n self.on_device_dialog_cancel_clicked()\\n return True\",\n \"def _on_mqtt_disconnect(\\n self, client: mqtt.Client, userdata: str, rc: int\\n ) -> None:\\n self.log.debug(f\\\"Disconnect return code: {rc}\\\")\\n self._connected = False\\n if rc != 0:\\n self.log.warning(\\n f\\\"Unexpected disconnect on {self.host}:{self.port}!\\\"\\n )\",\n \"def delete_event(self, widget, event, data=None): \\r\\n\\t\\t# XXX: Are you sure you want to quit?\\r\\n\\t\\tpreferences.save()\\r\\n\\t\\treturn False\",\n \"def on_disconnect(self, client, userdata, msg):\\n self.log.warning(\\\"Disconnected: \\\" + str(msg))\\n self.connected = False\\n self.dconn += 1\",\n \"def on_close(self):\\n # remove callback so it doesn't get called in the future\\n self._notifier.remove_callback(self.process_notification)\",\n \"def action_delete():\\n try:\\n deleted = delete_notification()\\n except:\\n raise HTTPResponse(body=\\\"Unexpected error\\\", status=400)\\n \\n if deleted:\\n return dict(msg=\\\"Notification deleted\\\")\\n else:\\n return dict(msg=\\\"No notification to delete\\\")\",\n \"def listen_del(self, f):\\n self._coms.register_leaving_callback(f)\",\n \"def delete_event(self, widget, event, Data=None):\\n\\t\\tself.todolist.save()\\n\\t\\tgtk.main_quit()\\n\\t\\treturn False\",\n \"def do_DELETE(self):\\n global data_store\\n logger.info(\\\"Received a DELETE request: {}\\\".format(self.path))\\n data = self.parse_data()\\n # Data should be the key name\\n logger.info(\\\"Data received {}: {}\\\".format(type(data), data))\\n path = self.strip_path()\\n if path == SET_OP:\\n logger.info(\\\"Running delete\\\")\\n data_store.delete_data(data)\\n response_code = 200\\n else:\\n logger.error(\\\"Invalid DELETE operation {} was received.\\\".format(path))\\n response_code = 404\\n self.send_response(response_code)\",\n \"def onShutdown(self, connection:MQTTConnection) -> None:\",\n \"async def end_object(self):\",\n \"async def delete(self):\\n return await self.set_message(text='')\",\n \"def network_delete_event(self, network_info):\\n\\n net_id = network_info['network_id']\\n if net_id not in self.network:\\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\\n net_id)\\n return\\n\\n segid = self.network[net_id].get('segmentation_id')\\n tenant_id = self.network[net_id].get('tenant_id')\\n tenant_name = self.get_project_name(tenant_id)\\n net = utils.Dict2Obj(self.network[net_id])\\n if not tenant_name:\\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\\n {'tenant_id': tenant_id})\\n self.update_network_db(net.id, constants.DELETE_FAIL)\\n return\\n\\n try:\\n self.dcnm_client.delete_network(tenant_name, net)\\n # Put back the segmentation id into the pool.\\n self.seg_drvr.release_segmentation_id(segid)\\n\\n # Remove entry from database and cache.\\n self.delete_network_db(net_id)\\n del self.network[net_id]\\n snets = [k for k in self.subnet if (\\n self.subnet[k].get('network_id') == net_id)]\\n [self.subnet.pop(s) for s in snets]\\n except dexc.DfaClientRequestFailed:\\n LOG.error(_LE('Failed to create network %(net)s.'),\\n {'net': net.name})\\n self.update_network_db(net_id, constants.DELETE_FAIL)\\n # deleting all related VMs\\n instances = self.get_vms()\\n instances_related = [k for k in instances if k.network_id == net_id]\\n for vm in instances_related:\\n LOG.debug(\\\"deleting vm %s because network is deleted\\\", vm.name)\\n self.delete_vm_function(vm.port_id, vm)\\n self.network_del_notif(tenant_id, tenant_name, net_id)\",\n \"def process_quit(message):\\n try:\\n Resident.objects.get(phone_number=message.sender).delete()\\n except Resident.DoesNotExist:\\n pass\\n \\n # TODO - wording...\\n message.respond('You have been removed from our system and will no longer get text messages.')\\n \\n return TropoOkResponse()\",\n \"def on_disconnect():\\n print(\\\"User disconnected!\\\")\",\n \"def on_delete(self, req, resp):\\n try:\\n days_to_retain = int(req.params[\\\"days\\\"])\\n except Exception:\\n days_to_retain = 90\\n\\n try:\\n retention_status = self.state_manager.task_retention(\\n retain_days=str(days_to_retain))\\n if not retention_status:\\n resp.status = falcon.HTTP_404\\n return\\n resp.text = \\\"Tables purged successfully.\\\"\\n except Exception as e:\\n self.error(req.context, \\\"Unknown error: %s\\\" % (str(e)))\\n resp.text = \\\"Unexpected error.\\\"\\n resp.status = falcon.HTTP_500\\n return\\n resp.status = falcon.HTTP_200\",\n \"async def disconnect(self):\",\n \"def after_delete(self, record):\\n debug = logging.getLogger(__name__).debug\\n debug('deleted group %r (%r)', record['name'], record['group_id'])\\n audit('delete group', record['name'])\",\n \"def on_deleted(self, event):\\n super(myEventHandler,self).on_deleted(event)\\n #print \\\"Removed: \\\" + event.src_path\\n if self.onedir.cookies is None or not self.onedir.autosyncstatus():\\n return\\n\\n source = event.src_path\\n if event.is_directory:\\n try:\\n pathtoonedir = self.onedir.getonedirrectory()\\n relpath = source.replace(pathtoonedir ,\\\"\\\")\\n self.onedir.deldirectory(relpath)\\n except Exception as e:\\n print \\\"Error syncing directory\\\" + e\\n exit(1)\\n else:\\n try:\\n #use os.path.split to get file name and path\\n splitpath = split(source)\\n file = splitpath[1]\\n pathtoonedir = self.onedir.getonedirrectory()\\n #print pathtoonedir\\n #print \\\"truncated path:\\\"\\n relpath = splitpath[0].replace(pathtoonedir ,\\\"\\\")\\n self.onedir.deletefile(file, relpath)\\n except OSError as e:\\n print \\\"Error copying file! \\\" + e.strerror\\n exit(1)\\n except IOError as e:\\n print \\\"IOerror creating file \\\" + e.strerror\\n exit(1)\",\n \"def handler(self):\\n\\t\\tself.exitClient()\",\n \"def __on_group_deleted(self, logger, *args):\",\n \"async def async_device_removed(event):\\n if event.data[\\\"action\\\"] != \\\"remove\\\":\\n return\\n await async_remove_automations(hass, event.data[\\\"device_id\\\"])\",\n \"def finish(self):\\n global log_th\\n log_th.log_info('{} disconnected from {} thread'.format(self.client_ip, threading.current_thread().name))\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def DeleteLayer(self, event):\\n pass\",\n \"def on_disconnect(self):\\n log.info(\\\"Stream disconnected\\\")\",\n \"def finalize_pending(self, data):\\n if self.request.method.upper() == 'DELETE':\\n data.delete()\",\n \"async def deleted(self, value):\\n pass\",\n \"def on_disconnect(unused_client, unused_userdata, rc):\\n\\tprint('on_disconnect', error_str(rc))\\n\\n\\t# Since a disconnect occurred, the next loop iteration will wait with\\n\\t# exponential backoff.\\n\\tglobal should_backoff\\n\\tshould_backoff = True\",\n \"def handle_disconnect(self,message,conn):\\n if self.connections[message[\\\"ID\\\"]][\\\"host\\\"] == True: #if user is host, delete all connections\\n print(\\\"HOST DISCONNECTING\\\")\\n host = message[\\\"ID\\\"]\\n session_location = self.connections[message[\\\"ID\\\"]][\\\"session_id\\\"]\\n for key in self.sessions[session_location][\\\"USERS\\\"].keys():\\n self.disconnect(self.connections[key][\\\"CONN\\\"],message[\\\"ID\\\"],f\\\"[HOST <{host}>:<{self.connections[host]['display_name']}>] Unexpectedly Disconnected\\\") #close each connection\\n self.delete_connection_entry(key) #delete the connection from the connections dictionary\\n self.delete_session(session_location) # delete the session\\n self.delete_connection_entry(message[\\\"ID\\\"])\\n self.disconnect(conn,message[\\\"ID\\\"], \\\"You Disconnected\\\")\\n else:\\n session_location = self.connections[message[\\\"ID\\\"]][\\\"session_id\\\"]\\n self.delete_session_entry(session_location,message[\\\"ID\\\"])\\n self.broadcast_to_session(session_location, \\\"USER_DISCONNECT\\\",f\\\"[USER <{message['ID']}>:<{self.connections[message['ID']]['display_name']}>] Disconnected\\\", exclude=[message[\\\"ID\\\"]])\\n self.delete_connection_entry(message[\\\"ID\\\"])\\n self.broadcast_to_session(session_location, \\\"USERS\\\", json.dumps(self.sessions[session_location][\\\"USERS\\\"]))\\n self.disconnect(conn,message[\\\"ID\\\"],\\\"You Disconnected\\\")\",\n \"def delete_handler(event, context):\\n delete_endpoint_config(event)\",\n \"def delete(self): \\n params = {'command':'deleteNetwork',\\n 'id':self.id}\\n \\n self.logger.debug('Remove network %s' % self.name)\\n \\n try:\\n response = self.send_request(params)\\n res = json.loads(response)\\n clsk_job_id = res['deletenetworkresponse']['jobid']\\n self.logger.debug('Start job over %s.%s - %s: %s' % (\\n self._obj_type, self.name, \\n 'deleteNetwork', res))\\n return clsk_job_id\\n except KeyError as ex :\\n self.logger.error('Error parsing json data: %s' % ex)\\n raise ClskError('Error parsing json data: %s' % ex)\\n except ApiError as ex:\\n self.logger.error(ex)\\n raise ClskError(ex)\",\n \"async def on_message_delete(self, msg: Message):\\n\\n if not self._is_tracked(msg.guild, EventPriority.delete):\\n return\\n\\n modlog_channels = [int(channel_id) for channel_id in self.get_guild_config(msg.guild).values()]\\n\\n # If message deleted from modlog, record event with header only\\n if msg.channel.id in modlog_channels:\\n\\n description = f\\\"\\\\n\\\\n{msg.embeds[0].description}\\\" if msg.embeds else \\\"\\\"\\n description = escape_markdown(description.replace(\\\"Modlog message deleted\\\\n\\\\n\\\", \\\"\\\"))\\n\\n em = self.em_base(\\n msg.author,\\n f\\\"Modlog message deleted{description}\\\",\\n EventColors.delete.value\\n )\\n\\n return await self.log_event(em, msg.guild, EventPriority.delete)\\n\\n # Otherwise, ignore bot's deleted embed-only (help pages, et.) messages\\n elif msg.author.id == self.bot.user.id and not msg.content:\\n return\\n\\n em = self.em_base(\\n msg.author,\\n f\\\"Message by {msg.author.mention} ({msg.author.name}) deleted\\\",\\n EventColors.delete.value\\n )\\n\\n em.description = f\\\"{em.description}\\\\n\\\\nChannel: {msg.channel.mention} ({msg.channel.name})\\\"\\n\\n if msg.content:\\n chunks = [msg.content[i:i + 1024] for i in range(0, len(msg.content), 1024)]\\n for i, chunk in enumerate(chunks):\\n em.add_field(\\n name=f\\\"🗑 Content [{i + 1}/{len(chunks)}]\\\",\\n value=chunk\\n )\\n\\n else:\\n em.add_field(\\n name=\\\"🗑 Content [0/0]\\\",\\n value=\\\"Message had no content\\\"\\n )\\n\\n # Try to re-download attached images if possible. The proxy url doesn't 404 immediately unlike the\\n # regular URL, so it may be possible to download from it before it goes down as well.\\n reupload = None\\n\\n if msg.attachments:\\n temp_image = BytesIO()\\n attachment = msg.attachments[0]\\n if attachment.size > 5000000:\\n # caching is important and all, but this will just cause more harm than good\\n return\\n\\n try:\\n await download_image(msg.attachments[0].proxy_url, temp_image)\\n reupload = File(temp_image, filename=\\\"reupload.{}\\\".format(attachment.filename))\\n\\n em.description = f\\\"{em.description}\\\\n\\\\n**Attachment Included Above**\\\"\\n\\n except Exception as error:\\n await self.errorlog.send(error)\\n reupload = None\\n\\n em.description = f\\\"{em.description}\\\\n\\\\n**Attachment Reupload Failed (See Error Log)**\\\"\\n\\n await self.log_event(em, msg.guild, priority=EventPriority.delete, file=reupload)\",\n \"def stopped(self, event, source):\\n\\n self.log('Disconnecting IRC bot')\\n self.fire(disconnect())\",\n \"async def _async_cast_removed(self, discover: ChromecastInfo) -> None:\",\n \"def DeleteEvents(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def deserialize_guild_channel_delete_event(\\n self, shard: gateway_shard.GatewayShard, payload: data_binding.JSONObject\\n ) -> channel_events.GuildChannelDeleteEvent:\",\n \"def visit_graph_end(self, network: Network):\\n pass\",\n \"def on_disconnect(self, client, userdata, rc):\\n\\t\\tprint (\\\"[{}] Client disconnected\\\".format(\\n\\t\\t\\tint(time.time())\\n\\t\\t))\",\n \"def _objectDeleted(self, obj):\\n pass\",\n \"def delete(self, data):\\r\\n pass\",\n \"def _disconnect(self):\\n self.socket.send_json({\\\"worker_id\\\": self.socket_id, \\\"message\\\": \\\"disconnect\\\"})\\n self.socket.close()\\n self.context.term()\\n exit()\",\n \"def on_disconnect():\\n print('User disconnected!')\\n return 'disconnected'\",\n \"def on_disconnect():\\n logger.info(f\\\"{request.sid} Disconnected\\\")\",\n \"def delete():\",\n \"def on_quit(self, raw_msg, source, **kwargs):\",\n \"def handleClose(self):\\n logging.info(\\\"%s %s\\\", self.address, \\\"closed\\\")\\n self.logbook.clients_disconnected_count += 1\",\n \"def __del__(self):\\n self.logger.info(\\\"Finishing\\\")\\n self.__disconnect()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6687941","0.65360117","0.6504362","0.6332553","0.6319839","0.6128922","0.6042391","0.6031273","0.6013475","0.6010431","0.596033","0.59602207","0.59489894","0.58846825","0.58623254","0.58432305","0.5838188","0.58338237","0.58286065","0.5809571","0.57975805","0.57683474","0.5767183","0.57644516","0.5744926","0.57159925","0.57003987","0.568238","0.56634927","0.5653553","0.5648847","0.56422645","0.5640196","0.56337917","0.56175804","0.56107205","0.5591159","0.557803","0.55514413","0.55292916","0.55122","0.5507101","0.5507101","0.549937","0.548677","0.5485191","0.5484039","0.5482293","0.5472671","0.5465646","0.5462632","0.54567134","0.5451944","0.5449543","0.5448998","0.5436167","0.5434471","0.5428115","0.5419783","0.54168075","0.5410368","0.5404359","0.54019517","0.54004264","0.5398967","0.5384541","0.5378655","0.53784245","0.5377112","0.5376985","0.5376523","0.5372861","0.53616345","0.53511214","0.5346721","0.53421766","0.5336468","0.533622","0.53275627","0.5325832","0.5323873","0.53230417","0.53183573","0.53129774","0.53067434","0.53060496","0.53037715","0.5302298","0.5295074","0.5286985","0.5285882","0.52812374","0.5278622","0.5277434","0.5276826","0.5271209","0.5270336","0.5270279","0.5268985","0.5260117"],"string":"[\n \"0.6687941\",\n \"0.65360117\",\n \"0.6504362\",\n \"0.6332553\",\n \"0.6319839\",\n \"0.6128922\",\n \"0.6042391\",\n \"0.6031273\",\n \"0.6013475\",\n \"0.6010431\",\n \"0.596033\",\n \"0.59602207\",\n \"0.59489894\",\n \"0.58846825\",\n \"0.58623254\",\n \"0.58432305\",\n \"0.5838188\",\n \"0.58338237\",\n \"0.58286065\",\n \"0.5809571\",\n \"0.57975805\",\n \"0.57683474\",\n \"0.5767183\",\n \"0.57644516\",\n \"0.5744926\",\n \"0.57159925\",\n \"0.57003987\",\n \"0.568238\",\n \"0.56634927\",\n \"0.5653553\",\n \"0.5648847\",\n \"0.56422645\",\n \"0.5640196\",\n \"0.56337917\",\n \"0.56175804\",\n \"0.56107205\",\n \"0.5591159\",\n \"0.557803\",\n \"0.55514413\",\n \"0.55292916\",\n \"0.55122\",\n \"0.5507101\",\n \"0.5507101\",\n \"0.549937\",\n \"0.548677\",\n \"0.5485191\",\n \"0.5484039\",\n \"0.5482293\",\n \"0.5472671\",\n \"0.5465646\",\n \"0.5462632\",\n \"0.54567134\",\n \"0.5451944\",\n \"0.5449543\",\n \"0.5448998\",\n \"0.5436167\",\n \"0.5434471\",\n \"0.5428115\",\n \"0.5419783\",\n \"0.54168075\",\n \"0.5410368\",\n \"0.5404359\",\n \"0.54019517\",\n \"0.54004264\",\n \"0.5398967\",\n \"0.5384541\",\n \"0.5378655\",\n \"0.53784245\",\n \"0.5377112\",\n \"0.5376985\",\n \"0.5376523\",\n \"0.5372861\",\n \"0.53616345\",\n \"0.53511214\",\n \"0.5346721\",\n \"0.53421766\",\n \"0.5336468\",\n \"0.533622\",\n \"0.53275627\",\n \"0.5325832\",\n \"0.5323873\",\n \"0.53230417\",\n \"0.53183573\",\n \"0.53129774\",\n \"0.53067434\",\n \"0.53060496\",\n \"0.53037715\",\n \"0.5302298\",\n \"0.5295074\",\n \"0.5286985\",\n \"0.5285882\",\n \"0.52812374\",\n \"0.5278622\",\n \"0.5277434\",\n \"0.5276826\",\n \"0.5271209\",\n \"0.5270336\",\n \"0.5270279\",\n \"0.5268985\",\n \"0.5260117\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68294984"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79793,"cells":{"query":{"kind":"string","value":"Handle the subnet.update.end notification event."},"document":{"kind":"string","value":"def subnet_update_end(self, payload):\n network_id = payload['subnet']['network_id']\n self.refresh_dhcp_helper(network_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n subnet = self._get_subnet_info(context._subnet)\n if subnet is not None:\n try:\n self.rpc_handler.update_subnet(subnet)\n except:\n pass","def subnet_delete_end(self, payload):\n subnet_id = payload['subnet_id']\n network = self.cache.get_network_by_subnet_id(subnet_id)\n if network:\n self.refresh_dhcp_helper(network.id)","def update_subnet_postcommit(self, mech_context):\n LOG.debug(\"update_subnet_postcommit: called\")","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","def post_subnet_update(self, resource_id, resource_dict):\n pass","def notify_end(self, status, objective):\n pass # pragma: no cover","def test_update_subnet(self):\n self.assertEqual(\n type(self.the_client.update_subnet(subnet_id, 'test_update_name1',\n 'test_update_description1')),\n baidubce.bce_response.BceResponse)","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def onMessageEnd(self):","def bcp_ball_end(self, **kwargs):\n self.events.post('ball_ended', **kwargs)","def endMessage(self):","def end(update: Update, context: CallbackContext) -> int:\n update.callback_query.answer()\n\n text = \"See you around!\"\n update.callback_query.edit_message_text(text=text)\n\n return END","def delete_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n try:\n self.rpc_handler.delete_subnet({str(context._subnet.get('id', '')): {}})\n except:\n pass","def handle_finish(self, dataset):\n\n logger.info('End of scheduling block message received.')","def _notify_listeners_end_operation(self):\n op = self.operation\n for lstnr in self.listeners:\n lstnr.end_operation(self, op)\n self.operation = None","def end(update, context) -> int:\n update.callback_query.edit_message_text(\n 'Bye! I hope we can talk again some day.')\n\n logger.info(\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\",\n update.callback_query.message.chat.first_name)\n return ConversationHandler.END","def end(self, obs: AgentObservation):\n self.brain.end(obs)","def end_box(self):\n self.end_hooks.fire(EndEvent())","def on_epoch_end(self):\n pass","def END(self):\n log.debug(\"All packets successfully transmitted!\")","def onEpochEnd(self, agent):\n\n pass","def on_subnet_del(self, response, subnet_id):\n LOG.info(\"Subnet %s deleted\", subnet_id)\n if subnet_id in self.subnets_by_id:\n del self.subnets_by_id[subnet_id]\n return","def on_chat_end(self, request, trigger_context):\n raise NotImplementedError","def round_end(event_var):\r\n debug.write(\"[SourceRPG] Round End Processing\", 1)\r\n if not currentTurboMode:\r\n debug.write(\"Normal mode is on\", 1)\r\n if str(saveType).lower() == \"round end\":\r\n debug.write(\"Attempting to save the database\", 1)\r\n saveDatabase()\r\n debug.write(\"Database successfully saved.\", 1)\r\n for userid in es.getUseridList():\r\n debug.write(\"Fetching player object for %s\" % es.getplayername(userid), 2)\r\n player = players[userid]\r\n if player is not None:\r\n debug.write(\"Object successfully fetched, delaying 4.8 seconds to reset default attributes\", 2)\r\n gamethread.delayedname(2, 'sourcerpg_reset_%s' % player, player.resetPlayerDefaultAttributes)\r\n debug.write(\"[SourceRPG] Round End has been processed\\n\", 1)","def process_employee_exit(self):\n if self.is_employee_serving():\n self._end_date.append(datetime.now().isoformat())\n\n print(f\"Successfully processed exit for employee {self.name} on\" \\\n f\"{self._end_date[-1]}\\nWe wish {self.name} for future endeavours\")\n return\n raise RejoiningException(\"Employee not in service. Cannot process exit.\")","def api_end_game(self):\n pass","def _handle_service_end(node: int, system: System, params: Params,\n records: Records):\n time_now = system.time\n server = system.get_server(node)\n queue = system.get_queue(node)\n\n # Extract the packet from the server and mark it as served:\n packet = server.pop()\n packet.service_finished[node] = time_now\n packet.was_served[node] = True\n\n # If this node is the target, then packet is delivered.\n # Otherwise, pass it to the next node.\n if packet.target == node:\n packet.was_delivered = True\n packet.delivery_time = time_now\n elif node < params.num_stations - 1:\n _process_packet(node + 1, packet, system, params, records)\n else:\n raise RuntimeError(\"shouldn't arrive here: packet reached network\"\n \"end, and this is not its destination!!\")\n\n # Start serving next packet, if exists:\n if (packet := queue.pop()) is not None:\n server.serve(packet)\n packet.service_started[node] = time_now\n system.schedule(Event.SERVICE_END, params.services[node](), node)\n\n # Record new system size:\n records.get_system_size(node).add(time_now, system.get_size(node))","def End(self):\n # PROTECTED REGION ID(CspSubElementSubarray.End) ENABLED START #\n return self.GoToIdle()\n # PROTECTED REGION END # // CspSubElementSubarray.End","def notify_finish_event(self):\n self.notify(self._finish_event_type())","def delete_subnet_postcommit(self, mech_context):\n LOG.debug(\"delete_subnetwork_postcommit: called\")","def end(self, won, reason):\n pass\n # replace with your end logic","def on_epoch_end(self):\n for callback in self.callbacks:\n callback.on_epoch_end(self, self.get_model())","def on_epoch_end(self):","def end(self):\n ...","def _onEnd(self, name, completed):\n logging.debug(\"onEnd...\")","def onEnd(self, agent):\n\n pass","def on_connection_closed(self):","def state_end_sub(self, byte):\n self.telnet_cmd.append(byte)\n if byte == 240:\n self.handle_telnet_cmd(self.telnet_cmd)\n self.next_fn = self.state_text\n else:\n self.next_fn = self.state_sub","def on_event_finished(self, event):","async def end_object(self):","def end(self) -> None:\n self.process_event(\n PipelineEvent(\n PipelineEventType.RUN_END,\n )\n )","def sync_end(self):","def on_epoch_end(self, epoch, logs):\n raise NotImplemented","async def on_end(self, game_result: Result):","def on_epoch_end(self):\n self.epoch_bar.update()","def epoch_end(self, epoch: int, logs: Dict):\n val = logs.get(self.monitor)\n\n if self.improvement(val=val, best_val=self.best_val):\n\n self.checkpoint(epoch=epoch)","def _telegram_stop_callback(self, update: Update, _: CallbackContext):\n\n rospy.loginfo(\"Stopping Telegram ROS bridge for chat id {}\".format(self._telegram_chat_id))\n update.message.reply_text(\n \"Disconnecting chat_id {}. So long and thanks for all the fish!\"\n \" Type /start to reconnect\".format(self._telegram_chat_id)\n )\n self._telegram_chat_id = None","def create_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n subnet = self._get_subnet_info(context._subnet)\n if subnet is not None:\n try:\n self.rpc_handler.create_subnet(subnet)\n except:\n pass","def end(event: EventType, widget: WidgetType) -> bool:\n return event.key == _locals.K_END","def _end(self):\n\n self.logger.msg1(\"Done\")","def on_epoch_end(self, epoch, logs=None):","def on_epoch_end(self, epoch, logs=None):","def end(self):\n self.shutdown = True","def bcp_game_end(self, **kwargs):\n self.player = None\n self.events.post('game_ended', **kwargs)","def on_validation_epoch_end(self):\n for callback in self.callbacks:\n callback.on_validation_epoch_end(self, self.get_model())","def auction_end(auction_id: int):\n auction = Auction.objects.get(pk=auction_id)\n auction.status = AuctionStatus.FINISHED\n auction.save()\n try:\n winner_bet = auction.bet_set.reverse()[0]\n except IndexError:\n logger.warning(f\"Nobody participated in auction {auction.name}\")\n return\n\n winner = winner_bet.user\n participance_id = auction.bet_set.values_list(\"user\", flat=True)\n participance = list(\n User.objects.filter(pk__in=participance_id).values_list(\"email\", flat=True)\n )\n\n broadcast_emails(\n participance,\n f\"Hi there, Auction is ending now, winner is {winner.email}, last price is {winner_bet.price}\",\n )","def network_load_balancer_update(event, context):\n print(\"NLB update Time remaining (MS):\", context.get_remaining_time_in_millis()) \n logger.info('Running network load balancer update')\n fwcontext = lib.get_ssl_context()\n total_fw_az = len(fw_azs)\n\n\n #Search for COMMIT in firewall table\n try:\n response = lib.firewall_table_get_all_in_state(stackname, region, 'COMMIT')\n for fw in response['Items']:\n nlb_port_mask = []\n for i in range (0, (num_nlb_port)/64):\n nlb_port_mask.append(0)\n\n # Get firewall Availabilty Zone index\n fw_az_index = fw_azs.index(fw['AvailZone'])\n set_nat = True\n # Find all the nlb in commit state\n nlb_response=lib.nlb_table_get_all_in_state(stackname, region, 'COMMIT')\n \n for nlb in nlb_response['Items']:\n nlb_port = nlb['TCPPort']\n nlb_ip = nlb['NLBIp']\n rule_mask_index = int((nlb_port-start_nlb_port)/64)\n nlb_bit = int((nlb_port-start_nlb_port)%64)\n nlb_port_mask[rule_mask_index] |= 1<<nlb_bit\n fw_rule_mask = long(fw['NLBRuleMask'+str(rule_mask_index)], 0)\n \n # Skip if it's configured on firewall\n if fw_rule_mask & (1 << nlb_bit) != 0:\n continue\n nlb_az_index = nlb['AZIndex']\n total_nlb_azs = nlb['TotalAZ']\n # Skip if NLB and firewall availabilty zone doesn't match\n if nlb_az_index%total_fw_az != fw_az_index:\n continue\n\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[fw_az_index], False) == False:\n logger.error('Config firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\n set_nat = False\n break\n \n if set_nat == True:\n # Find all the nlb deleted\n for rule_mask_index,item in enumerate(nlb_port_mask):\n fw_rule_mask = long(fw['NLBRuleMask'+str(rule_mask_index)], 0)\n if item & fw_rule_mask != fw_rule_mask:\n #Found NLB entry has been deleted\n for bit in range(0,64):\n if (fw_rule_mask & 1<<bit) != 0 and (item & 1<<bit) == 0:\n nlb_port = rule_mask_index*64+bit+start_nlb_port\n if lib.config_firewall_delete_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, nlb_port, True, True) == False:\n logger.error('Delete firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\n set_nat = False\n if lib.config_firewall_commit(fwcontext, fw['MgmtIP'], KeyPANWFirewall) == False:\n logger.error('Commit firewall configuration failed for instance %s, IP %s', fw['InstanceID'], fw['MgmtIP'])\n else:\n for mask in nlb_port_mask:\n print('port mask committed in COMMIT: {}'.format(mask))\n lib.firewall_table_update_rule_mask(stackname, region, fw['InstanceID'], nlb_port_mask)\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'READY')\n except Exception as e:\n logger.exception(\"Exception occurred while processing firewalls in commit: {}\".format(e))\n\n #Retrieve message from NLB queue\n pre_port = -1\n fw_update = False\n for read in xrange(0, 10):\n try:\n logger.info('Calling to retrieve message from NLB queue..: {}'.format(NetworkLoadBalancerQueue))\n message_data_str, ts, rh = lib.get_from_nlb_queue(NetworkLoadBalancerQueue, 10, 0)\n if not message_data_str:\n logger.info('No message to retrieve from NLB queue.')\n break \n else:\n #Delete message from NLB queue\n lib.delete_message_from_queue(NetworkLoadBalancerQueue, rh)\n message_data = json.loads(message_data_str)\n logger.info(\"Data from sqs: {}\".format(message_data_str))\n if 'MSG-TYPE' not in message_data or 'DNS-NAME' not in message_data:\n logger.error(\"Found invalid message in NetworkLoadBalancerQueue: {}\".format(message_data_str))\n continue\n nlb_type = message_data['MSG-TYPE']\n dns_name = message_data['DNS-NAME']\n if nlb_type == 'ADD-NLB':\n nlb_vpc = message_data['VPC-ID']\n nlb_name = message_data['NLB-NAME']\n dns_name = message_data['DNS-NAME']\n nlb_azs = message_data['AVAIL-ZONES']\n total_nlb_az = len(nlb_azs)\n nlb_port = lib.nlb_table_get_next_avail_port(stackname, region)\n for wait in xrange(0, 20):\n if pre_port == nlb_port and pre_port != 0:\n time.sleep(0.05)\n else:\n pre_port = nlb_port\n break\n if wait == 20:\n logger.error(\"Get next available port returns the same port %d, skip adding nlb %s\", nlb_port, nlb_name)\n continue\n else:\n logger.info(\"Wait for syncing dynamodb sleep count %d\", wait)\n \n if nlb_port == 0:\n logger.error(\"All ports number(%d-%d) has been used. Please deleting old network load balancer before adding more, skip adding nlb %s\", \n start_nlb_port, num_nlb_port+start_nlb_port-1, nlb_name)\n continue\n if total_nlb_az >= total_fw_az:\n for index,item in enumerate(nlb_azs):\n if 'NLB-IP' in item:\n nlb_ip = item['NLB-IP']\n else:\n logger.error(\"NLB IP is missing in ADD-NLB msg, ignore this entry\")\n continue\n nlb_subnet_id = item['SUBNET-ID']\n nlb_zone_name = item['ZONE-NAME']\n #Push NAT rules to all firewall in the same az \n if index > total_fw_az:\n continue\n \n response=lib.firewall_table_get_all_in_az_state(stackname, region, 'READY', fw_azs[index])\n for fw in response['Items']:\n fw_update = True\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[index], False) == False:\n logger.error('Config firewall NAT rule failed for instance %s, ip %s, NLB-port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\n \n logger.info(\"Add NLB entry IP %s, Port %d in COMMIT state\", nlb_ip, nlb_port) \n lib.nlb_table_add_entry(stackname, region, nlb_ip, nlb_port, 'COMMIT', nlb_zone_name, nlb_subnet_id, total_nlb_az, index, dns_name, nlb_name)\n else:\n for index,item in enumerate(fw_azs):\n response=lib.firewall_table_get_all_in_az_state(stackname, region, 'READY', item)\n nlb_index = int(index%total_nlb_az)\n az = nlb_azs[nlb_index]\n nlb_ip = az['NLB-IP']\n nlb_subnet_id = az['SUBNET-ID']\n nlb_zone_name = az['ZONE-NAME']\n \n for fw in response['Items']:\n fw_update = True\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[index], False) == False:\n logger.error('Config firewall NAT rule failed for instance %s, ip %s, NLB-port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\n \n if index < total_nlb_az:\n lib.nlb_table_add_entry(stackname, region, nlb_ip, nlb_port, 'COMMIT', nlb_zone_name, nlb_subnet_id, total_nlb_az, index, dns_name, nlb_name)\n elif nlb_type == 'DEL-NLB':\n #Deleting all entries belong to same DNSName\n\n print('Receive DEL-NLB msg from nlb queue')\n response = lib.nlb_table_get_entry_by_dnsname(stackname, region, dns_name)\n #Not found the NLB IP in the NLB table\n if response['Count'] == 0:\n logger.error(\"Receive NLB msg to delete non-existing NLB. DNS Name: %s\", dns_name)\n continue\n for nlb in response['Items']:\n nlb_port = nlb['TCPPort']\n nlb_ip = nlb['NLBIp']\n fw_response = lib.firewall_table_get_all_in_state(stackname, region, 'READY')\n \n for fw in fw_response['Items']:\n fw_az_index=fw_azs.index(fw['AvailZone'])\n nlb_az_index = fw_az_index%nlb['TotalAZ']\n # if NLB az index doens't mach firewall az index, skip\n if nlb['AZIndex'] != nlb_az_index:\n continue \n\n fw_update = True\n if lib.config_firewall_delete_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, nlb_port, True, False) == False:\n logger.error('Delete firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\n \n lib.nlb_table_delete_entry_by_dnsname(stackname, region, dns_name)\n \n else:\n logger.error('Receive invalid NLB message type for Network load balancer queue')\n\n except Exception as e:\n logger.exception(\"Exception occurred while retrieving data from NLB queue: {}\".format(e))\n \n # Perform commit once for all firewalls in READY state\n if fw_update == True:\n try:\n nlb_port_mask = []\n for i in range (0, (num_nlb_port)/64):\n nlb_port_mask.append(0)\n\n # Find all the nlb in commit state\n nlb_response=lib.nlb_table_get_all_in_state(stackname, region, 'COMMIT')\n print('nlb_response count: {}'.format(nlb_response['Count']))\n\n for nlb in nlb_response['Items']:\n nlb_port = nlb['TCPPort']\n nlb_ip = nlb['NLBIp']\n rule_mask_index = int((nlb_port-start_nlb_port)/64)\n nlb_bit = int((nlb_port-start_nlb_port)%64)\n nlb_port_mask[rule_mask_index] |= 1<<nlb_bit\n\n response=lib.firewall_table_get_all_in_state(stackname, region, 'READY')\n for fw in response['Items']:\n if lib.config_firewall_commit(fwcontext, fw['MgmtIP'], KeyPANWFirewall) == False:\n logger.error('Commit firewall configuration failed for instance %s, IP %s', fw['InstanceID'], fw['MgmtIP'])\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\n else:\n for mask in nlb_port_mask:\n print('port mask commited in READY: {}'.format(mask))\n\n lib.firewall_table_update_rule_mask(stackname, region, fw['InstanceID'], nlb_port_mask)\n except Exception as e:\n logger.exception(\"Exception occurred while updating firewall rules: {}\".format(e))\n\n \n print(\"Time remaining return network_load_balancer_update (MS):\", context.get_remaining_time_in_millis())","def add_end_listener(self, listener):\n self.end_listeners.append(listener)","def on_epoch_end(self, epoch, logs: Optional[Dict] = None):\n pass","def end(self):\n pass","def end(self):\n pass","def end(self):\n pass","def on_end(self, resource_id, lease=None):\n\n network_reservation = db_api.network_reservation_get(resource_id)\n reservation_id = network_reservation['reservation_id']\n\n db_api.network_reservation_update(network_reservation['id'],\n {'status': 'completed'})\n allocations = db_api.network_allocation_get_all_by_values(\n reservation_id=reservation_id)\n for allocation in allocations:\n db_api.network_allocation_destroy(allocation['id'])\n network_id = network_reservation['network_id']\n\n # The call to delete must be done without trust_id so the admin role is\n # used\n self.delete_neutron_network(network_id, reservation_id)","def on_end(self, ctx):\n pass","def end_message(self):\r\n if self.cur_message is not None:\r\n self.cur_message.end_time = datetime.now()\r\n self.wait_message()","def end(update, context):\n query = update.callback_query\n bot = context.bot\n bot.edit_message_text(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\"See you next time!\"\n )\n return ConversationHandler.END","def on_leave(self):\n\n self.check_connection.cancel()","def end(self):\n winners = mafia.str_player_list(self.game.winners())\n logging.info(\"Game over! Winners: %s\" % winners)\n\n subject = \"%s: The End\" % self.name\n body = \"Game over!\\n\\nCongratulations to %s for a well \" \\\n \"(or poorly; I can't tell) played game!\" % winners\n self.send_message(mafia.events.PUBLIC, subject, body)","def on_epoch_end(self):\n pass","def on_subnet_set(self, response, subnet_id):\n LOG.debug(\"Subnet %s created or updated\", subnet_id)\n subnet_data = etcdutils.safe_decode_json(response.value, 'subnet')\n\n if subnet_data is None:\n LOG.warning(\"Invalid subnet data %s\", response.value)\n return\n\n if not (isinstance(subnet_data, dict) and\n 'cidr' in subnet_data and\n 'gateway_ip' in subnet_data):\n LOG.warning(\"Invalid subnet data: %s\", subnet_data)\n return\n\n self.subnets_by_id[subnet_id] = subnet_data\n return","def handleResponseEnd(self):\r\n try:\r\n if not self._finished:\r\n reactor.callInThread(\r\n self.resource.cacheContent,\r\n self.father,\r\n self._response,\r\n self.buffer\r\n )\r\n proxy.ProxyClient.handleResponseEnd(self)\r\n except RuntimeError:\r\n # because we don't care if the user hits\r\n # refresh before the request is done\r\n pass","def on_update(self, event, signal):\n t = ppb.get_time() - self.start_time\n if t >= self.duration:\n signal(ppb.events.Quit())","def subnet_create_event(self, subnet_info):\n\n subnet = subnet_info.get('subnet')\n if subnet:\n self.create_subnet(subnet)\n else:\n # Check whether request is for subnets.\n subnets = subnet_info.get('subnets')\n if subnets:\n for subnet in subnets:\n self.create_subnet(subnet)","def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def end(self):\n\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\n # 3 minutes before giving up.\n if self.device_details['device_type'] == 'PBR':\n pump_id = self.device_details['setup']['pump_id']\n counter = 0\n while counter < 60:\n try:\n result = True, self.device.set_pump_state(pump_id, False)\n except Exception as e:\n result = False, e\n\n response = [(datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")),\n self.device_details['node_id'],\n self.device_details['device_type'],\n 8,\n [pump_id, False],\n result,\n 'internal']\n\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\n\n # end the loop if the pump has been successfully turned off\n if result[0]:\n break\n else:\n counter += 1\n sleep(3)\n\n self.device.disconnect()","def SendEndScanSignal(self):\n pass","def end(self) -> None:","def epoch_end(self):\n pass","def processEnded(self, reason):\n if not self.empty:\n self.output.dataReceived('\\n')\n self.service.processEnded(self.name)","def handle_update(self, call):\n self.fire_event(EVENT_UPDATE)","def end():\n return EndBlock()","def end_call(self, request, identifier=None):\n call = self.get_object()\n timestamp = request.data.get('timestamp', None)\n\n try:\n if not call.has_ended:\n call.end_call(timestamp=timestamp)\n except ValueError as exc:\n raise serializers.ValidationError(str(exc))\n\n serializer = self.serializer_class(instance=call)\n return Response(serializer.data, status=status.HTTP_200_OK)","def onSlaveLost(self):","def do_end(end):\n if end:\n do_action(end)","def val_batch_end(self, batch_data, running_val_data):\n pass","def handle_event(self, event):\n if self.sub_event is not None:\n self.sub_event.handle_event(event)\n return\n\n elif not self.response_box.is_dead:\n self.response_box.handle_event(event)","def onMessageFrameEnd(self):","def handle_record_end():\n LOG.info(\"End Recording...\")\n context = {'client_name': 'mycroft_listener',\n 'source': 'audio',\n 'destination': [\"skills\"]}\n bus.emit(Message('recognizer_loop:record_end', context=context))","def leave_notify_event(self, widget, event):\n self.logger.debug(\"leaving widget...\")\n return self.make_callback('leave')","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def end(self) -> None:\n return","def stop(update: Update, context: CallbackContext) -> int:\n update.message.reply_text(\"Okay, bye.\")\n\n return END","def complete(self, update):\n self.reactor.callFromThread(self._complete, update)","def sendSslEnd(self, req):\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))","def post_subnet_read(self, resource_id, resource_dict):\n pass","def post_delete_subnet(self, sender, instance, **kwargs):\n RecurseNetworks.delete_entries(subnet=str(instance.ip_network), net_name=instance.name)","def _end_event(self):\n self.end_event_time = datetime.utcnow()\n self.event_properties[\"ElapsedTime\"] = str(\n self.end_event_time - self.start_event_time\n )","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def _on_recv_complete(self, handle, addr, flags, data, error):\n assert handle is self._handle\n if error:\n self._log.warning('pyuv error {} in recv callback', error)\n self._protocol.error_received(TransportError.from_errno(error))\n elif flags:\n assert flags & pyuv.UV_UDP_PARTIAL\n self._log.warning('ignoring partial datagram')\n elif data:\n self._protocol.datagram_received(data, addr)","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))"],"string":"[\n \"def update_subnet_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n subnet = self._get_subnet_info(context._subnet)\\n if subnet is not None:\\n try:\\n self.rpc_handler.update_subnet(subnet)\\n except:\\n pass\",\n \"def subnet_delete_end(self, payload):\\n subnet_id = payload['subnet_id']\\n network = self.cache.get_network_by_subnet_id(subnet_id)\\n if network:\\n self.refresh_dhcp_helper(network.id)\",\n \"def update_subnet_postcommit(self, mech_context):\\n LOG.debug(\\\"update_subnet_postcommit: called\\\")\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"def post_subnet_update(self, resource_id, resource_dict):\\n pass\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def test_update_subnet(self):\\n self.assertEqual(\\n type(self.the_client.update_subnet(subnet_id, 'test_update_name1',\\n 'test_update_description1')),\\n baidubce.bce_response.BceResponse)\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def onMessageEnd(self):\",\n \"def bcp_ball_end(self, **kwargs):\\n self.events.post('ball_ended', **kwargs)\",\n \"def endMessage(self):\",\n \"def end(update: Update, context: CallbackContext) -> int:\\n update.callback_query.answer()\\n\\n text = \\\"See you around!\\\"\\n update.callback_query.edit_message_text(text=text)\\n\\n return END\",\n \"def delete_subnet_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n try:\\n self.rpc_handler.delete_subnet({str(context._subnet.get('id', '')): {}})\\n except:\\n pass\",\n \"def handle_finish(self, dataset):\\n\\n logger.info('End of scheduling block message received.')\",\n \"def _notify_listeners_end_operation(self):\\n op = self.operation\\n for lstnr in self.listeners:\\n lstnr.end_operation(self, op)\\n self.operation = None\",\n \"def end(update, context) -> int:\\n update.callback_query.edit_message_text(\\n 'Bye! I hope we can talk again some day.')\\n\\n logger.info(\\\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\\\",\\n update.callback_query.message.chat.first_name)\\n return ConversationHandler.END\",\n \"def end(self, obs: AgentObservation):\\n self.brain.end(obs)\",\n \"def end_box(self):\\n self.end_hooks.fire(EndEvent())\",\n \"def on_epoch_end(self):\\n pass\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"def onEpochEnd(self, agent):\\n\\n pass\",\n \"def on_subnet_del(self, response, subnet_id):\\n LOG.info(\\\"Subnet %s deleted\\\", subnet_id)\\n if subnet_id in self.subnets_by_id:\\n del self.subnets_by_id[subnet_id]\\n return\",\n \"def on_chat_end(self, request, trigger_context):\\n raise NotImplementedError\",\n \"def round_end(event_var):\\r\\n debug.write(\\\"[SourceRPG] Round End Processing\\\", 1)\\r\\n if not currentTurboMode:\\r\\n debug.write(\\\"Normal mode is on\\\", 1)\\r\\n if str(saveType).lower() == \\\"round end\\\":\\r\\n debug.write(\\\"Attempting to save the database\\\", 1)\\r\\n saveDatabase()\\r\\n debug.write(\\\"Database successfully saved.\\\", 1)\\r\\n for userid in es.getUseridList():\\r\\n debug.write(\\\"Fetching player object for %s\\\" % es.getplayername(userid), 2)\\r\\n player = players[userid]\\r\\n if player is not None:\\r\\n debug.write(\\\"Object successfully fetched, delaying 4.8 seconds to reset default attributes\\\", 2)\\r\\n gamethread.delayedname(2, 'sourcerpg_reset_%s' % player, player.resetPlayerDefaultAttributes)\\r\\n debug.write(\\\"[SourceRPG] Round End has been processed\\\\n\\\", 1)\",\n \"def process_employee_exit(self):\\n if self.is_employee_serving():\\n self._end_date.append(datetime.now().isoformat())\\n\\n print(f\\\"Successfully processed exit for employee {self.name} on\\\" \\\\\\n f\\\"{self._end_date[-1]}\\\\nWe wish {self.name} for future endeavours\\\")\\n return\\n raise RejoiningException(\\\"Employee not in service. Cannot process exit.\\\")\",\n \"def api_end_game(self):\\n pass\",\n \"def _handle_service_end(node: int, system: System, params: Params,\\n records: Records):\\n time_now = system.time\\n server = system.get_server(node)\\n queue = system.get_queue(node)\\n\\n # Extract the packet from the server and mark it as served:\\n packet = server.pop()\\n packet.service_finished[node] = time_now\\n packet.was_served[node] = True\\n\\n # If this node is the target, then packet is delivered.\\n # Otherwise, pass it to the next node.\\n if packet.target == node:\\n packet.was_delivered = True\\n packet.delivery_time = time_now\\n elif node < params.num_stations - 1:\\n _process_packet(node + 1, packet, system, params, records)\\n else:\\n raise RuntimeError(\\\"shouldn't arrive here: packet reached network\\\"\\n \\\"end, and this is not its destination!!\\\")\\n\\n # Start serving next packet, if exists:\\n if (packet := queue.pop()) is not None:\\n server.serve(packet)\\n packet.service_started[node] = time_now\\n system.schedule(Event.SERVICE_END, params.services[node](), node)\\n\\n # Record new system size:\\n records.get_system_size(node).add(time_now, system.get_size(node))\",\n \"def End(self):\\n # PROTECTED REGION ID(CspSubElementSubarray.End) ENABLED START #\\n return self.GoToIdle()\\n # PROTECTED REGION END # // CspSubElementSubarray.End\",\n \"def notify_finish_event(self):\\n self.notify(self._finish_event_type())\",\n \"def delete_subnet_postcommit(self, mech_context):\\n LOG.debug(\\\"delete_subnetwork_postcommit: called\\\")\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"def on_epoch_end(self):\\n for callback in self.callbacks:\\n callback.on_epoch_end(self, self.get_model())\",\n \"def on_epoch_end(self):\",\n \"def end(self):\\n ...\",\n \"def _onEnd(self, name, completed):\\n logging.debug(\\\"onEnd...\\\")\",\n \"def onEnd(self, agent):\\n\\n pass\",\n \"def on_connection_closed(self):\",\n \"def state_end_sub(self, byte):\\n self.telnet_cmd.append(byte)\\n if byte == 240:\\n self.handle_telnet_cmd(self.telnet_cmd)\\n self.next_fn = self.state_text\\n else:\\n self.next_fn = self.state_sub\",\n \"def on_event_finished(self, event):\",\n \"async def end_object(self):\",\n \"def end(self) -> None:\\n self.process_event(\\n PipelineEvent(\\n PipelineEventType.RUN_END,\\n )\\n )\",\n \"def sync_end(self):\",\n \"def on_epoch_end(self, epoch, logs):\\n raise NotImplemented\",\n \"async def on_end(self, game_result: Result):\",\n \"def on_epoch_end(self):\\n self.epoch_bar.update()\",\n \"def epoch_end(self, epoch: int, logs: Dict):\\n val = logs.get(self.monitor)\\n\\n if self.improvement(val=val, best_val=self.best_val):\\n\\n self.checkpoint(epoch=epoch)\",\n \"def _telegram_stop_callback(self, update: Update, _: CallbackContext):\\n\\n rospy.loginfo(\\\"Stopping Telegram ROS bridge for chat id {}\\\".format(self._telegram_chat_id))\\n update.message.reply_text(\\n \\\"Disconnecting chat_id {}. So long and thanks for all the fish!\\\"\\n \\\" Type /start to reconnect\\\".format(self._telegram_chat_id)\\n )\\n self._telegram_chat_id = None\",\n \"def create_subnet_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n subnet = self._get_subnet_info(context._subnet)\\n if subnet is not None:\\n try:\\n self.rpc_handler.create_subnet(subnet)\\n except:\\n pass\",\n \"def end(event: EventType, widget: WidgetType) -> bool:\\n return event.key == _locals.K_END\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def end(self):\\n self.shutdown = True\",\n \"def bcp_game_end(self, **kwargs):\\n self.player = None\\n self.events.post('game_ended', **kwargs)\",\n \"def on_validation_epoch_end(self):\\n for callback in self.callbacks:\\n callback.on_validation_epoch_end(self, self.get_model())\",\n \"def auction_end(auction_id: int):\\n auction = Auction.objects.get(pk=auction_id)\\n auction.status = AuctionStatus.FINISHED\\n auction.save()\\n try:\\n winner_bet = auction.bet_set.reverse()[0]\\n except IndexError:\\n logger.warning(f\\\"Nobody participated in auction {auction.name}\\\")\\n return\\n\\n winner = winner_bet.user\\n participance_id = auction.bet_set.values_list(\\\"user\\\", flat=True)\\n participance = list(\\n User.objects.filter(pk__in=participance_id).values_list(\\\"email\\\", flat=True)\\n )\\n\\n broadcast_emails(\\n participance,\\n f\\\"Hi there, Auction is ending now, winner is {winner.email}, last price is {winner_bet.price}\\\",\\n )\",\n \"def network_load_balancer_update(event, context):\\n print(\\\"NLB update Time remaining (MS):\\\", context.get_remaining_time_in_millis()) \\n logger.info('Running network load balancer update')\\n fwcontext = lib.get_ssl_context()\\n total_fw_az = len(fw_azs)\\n\\n\\n #Search for COMMIT in firewall table\\n try:\\n response = lib.firewall_table_get_all_in_state(stackname, region, 'COMMIT')\\n for fw in response['Items']:\\n nlb_port_mask = []\\n for i in range (0, (num_nlb_port)/64):\\n nlb_port_mask.append(0)\\n\\n # Get firewall Availabilty Zone index\\n fw_az_index = fw_azs.index(fw['AvailZone'])\\n set_nat = True\\n # Find all the nlb in commit state\\n nlb_response=lib.nlb_table_get_all_in_state(stackname, region, 'COMMIT')\\n \\n for nlb in nlb_response['Items']:\\n nlb_port = nlb['TCPPort']\\n nlb_ip = nlb['NLBIp']\\n rule_mask_index = int((nlb_port-start_nlb_port)/64)\\n nlb_bit = int((nlb_port-start_nlb_port)%64)\\n nlb_port_mask[rule_mask_index] |= 1<<nlb_bit\\n fw_rule_mask = long(fw['NLBRuleMask'+str(rule_mask_index)], 0)\\n \\n # Skip if it's configured on firewall\\n if fw_rule_mask & (1 << nlb_bit) != 0:\\n continue\\n nlb_az_index = nlb['AZIndex']\\n total_nlb_azs = nlb['TotalAZ']\\n # Skip if NLB and firewall availabilty zone doesn't match\\n if nlb_az_index%total_fw_az != fw_az_index:\\n continue\\n\\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[fw_az_index], False) == False:\\n logger.error('Config firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\\n set_nat = False\\n break\\n \\n if set_nat == True:\\n # Find all the nlb deleted\\n for rule_mask_index,item in enumerate(nlb_port_mask):\\n fw_rule_mask = long(fw['NLBRuleMask'+str(rule_mask_index)], 0)\\n if item & fw_rule_mask != fw_rule_mask:\\n #Found NLB entry has been deleted\\n for bit in range(0,64):\\n if (fw_rule_mask & 1<<bit) != 0 and (item & 1<<bit) == 0:\\n nlb_port = rule_mask_index*64+bit+start_nlb_port\\n if lib.config_firewall_delete_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, nlb_port, True, True) == False:\\n logger.error('Delete firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\\n set_nat = False\\n if lib.config_firewall_commit(fwcontext, fw['MgmtIP'], KeyPANWFirewall) == False:\\n logger.error('Commit firewall configuration failed for instance %s, IP %s', fw['InstanceID'], fw['MgmtIP'])\\n else:\\n for mask in nlb_port_mask:\\n print('port mask committed in COMMIT: {}'.format(mask))\\n lib.firewall_table_update_rule_mask(stackname, region, fw['InstanceID'], nlb_port_mask)\\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'READY')\\n except Exception as e:\\n logger.exception(\\\"Exception occurred while processing firewalls in commit: {}\\\".format(e))\\n\\n #Retrieve message from NLB queue\\n pre_port = -1\\n fw_update = False\\n for read in xrange(0, 10):\\n try:\\n logger.info('Calling to retrieve message from NLB queue..: {}'.format(NetworkLoadBalancerQueue))\\n message_data_str, ts, rh = lib.get_from_nlb_queue(NetworkLoadBalancerQueue, 10, 0)\\n if not message_data_str:\\n logger.info('No message to retrieve from NLB queue.')\\n break \\n else:\\n #Delete message from NLB queue\\n lib.delete_message_from_queue(NetworkLoadBalancerQueue, rh)\\n message_data = json.loads(message_data_str)\\n logger.info(\\\"Data from sqs: {}\\\".format(message_data_str))\\n if 'MSG-TYPE' not in message_data or 'DNS-NAME' not in message_data:\\n logger.error(\\\"Found invalid message in NetworkLoadBalancerQueue: {}\\\".format(message_data_str))\\n continue\\n nlb_type = message_data['MSG-TYPE']\\n dns_name = message_data['DNS-NAME']\\n if nlb_type == 'ADD-NLB':\\n nlb_vpc = message_data['VPC-ID']\\n nlb_name = message_data['NLB-NAME']\\n dns_name = message_data['DNS-NAME']\\n nlb_azs = message_data['AVAIL-ZONES']\\n total_nlb_az = len(nlb_azs)\\n nlb_port = lib.nlb_table_get_next_avail_port(stackname, region)\\n for wait in xrange(0, 20):\\n if pre_port == nlb_port and pre_port != 0:\\n time.sleep(0.05)\\n else:\\n pre_port = nlb_port\\n break\\n if wait == 20:\\n logger.error(\\\"Get next available port returns the same port %d, skip adding nlb %s\\\", nlb_port, nlb_name)\\n continue\\n else:\\n logger.info(\\\"Wait for syncing dynamodb sleep count %d\\\", wait)\\n \\n if nlb_port == 0:\\n logger.error(\\\"All ports number(%d-%d) has been used. Please deleting old network load balancer before adding more, skip adding nlb %s\\\", \\n start_nlb_port, num_nlb_port+start_nlb_port-1, nlb_name)\\n continue\\n if total_nlb_az >= total_fw_az:\\n for index,item in enumerate(nlb_azs):\\n if 'NLB-IP' in item:\\n nlb_ip = item['NLB-IP']\\n else:\\n logger.error(\\\"NLB IP is missing in ADD-NLB msg, ignore this entry\\\")\\n continue\\n nlb_subnet_id = item['SUBNET-ID']\\n nlb_zone_name = item['ZONE-NAME']\\n #Push NAT rules to all firewall in the same az \\n if index > total_fw_az:\\n continue\\n \\n response=lib.firewall_table_get_all_in_az_state(stackname, region, 'READY', fw_azs[index])\\n for fw in response['Items']:\\n fw_update = True\\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[index], False) == False:\\n logger.error('Config firewall NAT rule failed for instance %s, ip %s, NLB-port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\\n \\n logger.info(\\\"Add NLB entry IP %s, Port %d in COMMIT state\\\", nlb_ip, nlb_port) \\n lib.nlb_table_add_entry(stackname, region, nlb_ip, nlb_port, 'COMMIT', nlb_zone_name, nlb_subnet_id, total_nlb_az, index, dns_name, nlb_name)\\n else:\\n for index,item in enumerate(fw_azs):\\n response=lib.firewall_table_get_all_in_az_state(stackname, region, 'READY', item)\\n nlb_index = int(index%total_nlb_az)\\n az = nlb_azs[nlb_index]\\n nlb_ip = az['NLB-IP']\\n nlb_subnet_id = az['SUBNET-ID']\\n nlb_zone_name = az['ZONE-NAME']\\n \\n for fw in response['Items']:\\n fw_update = True\\n if lib.config_firewall_add_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, fw['UntrustIP'], nlb_port, nlb_ip, True, trust_def_gw[index], False) == False:\\n logger.error('Config firewall NAT rule failed for instance %s, ip %s, NLB-port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\\n \\n if index < total_nlb_az:\\n lib.nlb_table_add_entry(stackname, region, nlb_ip, nlb_port, 'COMMIT', nlb_zone_name, nlb_subnet_id, total_nlb_az, index, dns_name, nlb_name)\\n elif nlb_type == 'DEL-NLB':\\n #Deleting all entries belong to same DNSName\\n\\n print('Receive DEL-NLB msg from nlb queue')\\n response = lib.nlb_table_get_entry_by_dnsname(stackname, region, dns_name)\\n #Not found the NLB IP in the NLB table\\n if response['Count'] == 0:\\n logger.error(\\\"Receive NLB msg to delete non-existing NLB. DNS Name: %s\\\", dns_name)\\n continue\\n for nlb in response['Items']:\\n nlb_port = nlb['TCPPort']\\n nlb_ip = nlb['NLBIp']\\n fw_response = lib.firewall_table_get_all_in_state(stackname, region, 'READY')\\n \\n for fw in fw_response['Items']:\\n fw_az_index=fw_azs.index(fw['AvailZone'])\\n nlb_az_index = fw_az_index%nlb['TotalAZ']\\n # if NLB az index doens't mach firewall az index, skip\\n if nlb['AZIndex'] != nlb_az_index:\\n continue \\n\\n fw_update = True\\n if lib.config_firewall_delete_nat_rule(fwcontext, fw['MgmtIP'], KeyPANWFirewall, nlb_port, True, False) == False:\\n logger.error('Delete firewall NAT rule failed for instance %s, IP %s, NLB-Port %d', fw['InstanceID'], fw['MgmtIP'], nlb_port)\\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\\n \\n lib.nlb_table_delete_entry_by_dnsname(stackname, region, dns_name)\\n \\n else:\\n logger.error('Receive invalid NLB message type for Network load balancer queue')\\n\\n except Exception as e:\\n logger.exception(\\\"Exception occurred while retrieving data from NLB queue: {}\\\".format(e))\\n \\n # Perform commit once for all firewalls in READY state\\n if fw_update == True:\\n try:\\n nlb_port_mask = []\\n for i in range (0, (num_nlb_port)/64):\\n nlb_port_mask.append(0)\\n\\n # Find all the nlb in commit state\\n nlb_response=lib.nlb_table_get_all_in_state(stackname, region, 'COMMIT')\\n print('nlb_response count: {}'.format(nlb_response['Count']))\\n\\n for nlb in nlb_response['Items']:\\n nlb_port = nlb['TCPPort']\\n nlb_ip = nlb['NLBIp']\\n rule_mask_index = int((nlb_port-start_nlb_port)/64)\\n nlb_bit = int((nlb_port-start_nlb_port)%64)\\n nlb_port_mask[rule_mask_index] |= 1<<nlb_bit\\n\\n response=lib.firewall_table_get_all_in_state(stackname, region, 'READY')\\n for fw in response['Items']:\\n if lib.config_firewall_commit(fwcontext, fw['MgmtIP'], KeyPANWFirewall) == False:\\n logger.error('Commit firewall configuration failed for instance %s, IP %s', fw['InstanceID'], fw['MgmtIP'])\\n lib.firewall_table_update_state(stackname, region, fw['InstanceID'], 'COMMIT')\\n else:\\n for mask in nlb_port_mask:\\n print('port mask commited in READY: {}'.format(mask))\\n\\n lib.firewall_table_update_rule_mask(stackname, region, fw['InstanceID'], nlb_port_mask)\\n except Exception as e:\\n logger.exception(\\\"Exception occurred while updating firewall rules: {}\\\".format(e))\\n\\n \\n print(\\\"Time remaining return network_load_balancer_update (MS):\\\", context.get_remaining_time_in_millis())\",\n \"def add_end_listener(self, listener):\\n self.end_listeners.append(listener)\",\n \"def on_epoch_end(self, epoch, logs: Optional[Dict] = None):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def on_end(self, resource_id, lease=None):\\n\\n network_reservation = db_api.network_reservation_get(resource_id)\\n reservation_id = network_reservation['reservation_id']\\n\\n db_api.network_reservation_update(network_reservation['id'],\\n {'status': 'completed'})\\n allocations = db_api.network_allocation_get_all_by_values(\\n reservation_id=reservation_id)\\n for allocation in allocations:\\n db_api.network_allocation_destroy(allocation['id'])\\n network_id = network_reservation['network_id']\\n\\n # The call to delete must be done without trust_id so the admin role is\\n # used\\n self.delete_neutron_network(network_id, reservation_id)\",\n \"def on_end(self, ctx):\\n pass\",\n \"def end_message(self):\\r\\n if self.cur_message is not None:\\r\\n self.cur_message.end_time = datetime.now()\\r\\n self.wait_message()\",\n \"def end(update, context):\\n query = update.callback_query\\n bot = context.bot\\n bot.edit_message_text(\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n text=\\\"See you next time!\\\"\\n )\\n return ConversationHandler.END\",\n \"def on_leave(self):\\n\\n self.check_connection.cancel()\",\n \"def end(self):\\n winners = mafia.str_player_list(self.game.winners())\\n logging.info(\\\"Game over! Winners: %s\\\" % winners)\\n\\n subject = \\\"%s: The End\\\" % self.name\\n body = \\\"Game over!\\\\n\\\\nCongratulations to %s for a well \\\" \\\\\\n \\\"(or poorly; I can't tell) played game!\\\" % winners\\n self.send_message(mafia.events.PUBLIC, subject, body)\",\n \"def on_epoch_end(self):\\n pass\",\n \"def on_subnet_set(self, response, subnet_id):\\n LOG.debug(\\\"Subnet %s created or updated\\\", subnet_id)\\n subnet_data = etcdutils.safe_decode_json(response.value, 'subnet')\\n\\n if subnet_data is None:\\n LOG.warning(\\\"Invalid subnet data %s\\\", response.value)\\n return\\n\\n if not (isinstance(subnet_data, dict) and\\n 'cidr' in subnet_data and\\n 'gateway_ip' in subnet_data):\\n LOG.warning(\\\"Invalid subnet data: %s\\\", subnet_data)\\n return\\n\\n self.subnets_by_id[subnet_id] = subnet_data\\n return\",\n \"def handleResponseEnd(self):\\r\\n try:\\r\\n if not self._finished:\\r\\n reactor.callInThread(\\r\\n self.resource.cacheContent,\\r\\n self.father,\\r\\n self._response,\\r\\n self.buffer\\r\\n )\\r\\n proxy.ProxyClient.handleResponseEnd(self)\\r\\n except RuntimeError:\\r\\n # because we don't care if the user hits\\r\\n # refresh before the request is done\\r\\n pass\",\n \"def on_update(self, event, signal):\\n t = ppb.get_time() - self.start_time\\n if t >= self.duration:\\n signal(ppb.events.Quit())\",\n \"def subnet_create_event(self, subnet_info):\\n\\n subnet = subnet_info.get('subnet')\\n if subnet:\\n self.create_subnet(subnet)\\n else:\\n # Check whether request is for subnets.\\n subnets = subnet_info.get('subnets')\\n if subnets:\\n for subnet in subnets:\\n self.create_subnet(subnet)\",\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def end(self):\\n\\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\\n # 3 minutes before giving up.\\n if self.device_details['device_type'] == 'PBR':\\n pump_id = self.device_details['setup']['pump_id']\\n counter = 0\\n while counter < 60:\\n try:\\n result = True, self.device.set_pump_state(pump_id, False)\\n except Exception as e:\\n result = False, e\\n\\n response = [(datetime.datetime.utcnow().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")),\\n self.device_details['node_id'],\\n self.device_details['device_type'],\\n 8,\\n [pump_id, False],\\n result,\\n 'internal']\\n\\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\\n\\n # end the loop if the pump has been successfully turned off\\n if result[0]:\\n break\\n else:\\n counter += 1\\n sleep(3)\\n\\n self.device.disconnect()\",\n \"def SendEndScanSignal(self):\\n pass\",\n \"def end(self) -> None:\",\n \"def epoch_end(self):\\n pass\",\n \"def processEnded(self, reason):\\n if not self.empty:\\n self.output.dataReceived('\\\\n')\\n self.service.processEnded(self.name)\",\n \"def handle_update(self, call):\\n self.fire_event(EVENT_UPDATE)\",\n \"def end():\\n return EndBlock()\",\n \"def end_call(self, request, identifier=None):\\n call = self.get_object()\\n timestamp = request.data.get('timestamp', None)\\n\\n try:\\n if not call.has_ended:\\n call.end_call(timestamp=timestamp)\\n except ValueError as exc:\\n raise serializers.ValidationError(str(exc))\\n\\n serializer = self.serializer_class(instance=call)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def onSlaveLost(self):\",\n \"def do_end(end):\\n if end:\\n do_action(end)\",\n \"def val_batch_end(self, batch_data, running_val_data):\\n pass\",\n \"def handle_event(self, event):\\n if self.sub_event is not None:\\n self.sub_event.handle_event(event)\\n return\\n\\n elif not self.response_box.is_dead:\\n self.response_box.handle_event(event)\",\n \"def onMessageFrameEnd(self):\",\n \"def handle_record_end():\\n LOG.info(\\\"End Recording...\\\")\\n context = {'client_name': 'mycroft_listener',\\n 'source': 'audio',\\n 'destination': [\\\"skills\\\"]}\\n bus.emit(Message('recognizer_loop:record_end', context=context))\",\n \"def leave_notify_event(self, widget, event):\\n self.logger.debug(\\\"leaving widget...\\\")\\n return self.make_callback('leave')\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def end(self) -> None:\\n return\",\n \"def stop(update: Update, context: CallbackContext) -> int:\\n update.message.reply_text(\\\"Okay, bye.\\\")\\n\\n return END\",\n \"def complete(self, update):\\n self.reactor.callFromThread(self._complete, update)\",\n \"def sendSslEnd(self, req):\\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))\",\n \"def post_subnet_read(self, resource_id, resource_dict):\\n pass\",\n \"def post_delete_subnet(self, sender, instance, **kwargs):\\n RecurseNetworks.delete_entries(subnet=str(instance.ip_network), net_name=instance.name)\",\n \"def _end_event(self):\\n self.end_event_time = datetime.utcnow()\\n self.event_properties[\\\"ElapsedTime\\\"] = str(\\n self.end_event_time - self.start_event_time\\n )\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def _on_recv_complete(self, handle, addr, flags, data, error):\\n assert handle is self._handle\\n if error:\\n self._log.warning('pyuv error {} in recv callback', error)\\n self._protocol.error_received(TransportError.from_errno(error))\\n elif flags:\\n assert flags & pyuv.UV_UDP_PARTIAL\\n self._log.warning('ignoring partial datagram')\\n elif data:\\n self._protocol.datagram_received(data, addr)\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\"\n]","isConversational":false},"negative_scores":{"kind":"list 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event."},"document":{"kind":"string","value":"def subnet_delete_end(self, payload):\n subnet_id = payload['subnet_id']\n network = self.cache.get_network_by_subnet_id(subnet_id)\n if network:\n self.refresh_dhcp_helper(network.id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def delete_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n try:\n self.rpc_handler.delete_subnet({str(context._subnet.get('id', '')): {}})\n except:\n pass","def delete_subnet_postcommit(self, mech_context):\n LOG.debug(\"delete_subnetwork_postcommit: called\")","def subnet_update_end(self, payload):\n network_id = payload['subnet']['network_id']\n self.refresh_dhcp_helper(network_id)","def post_subnet_delete(self, resource_id, resource_dict):\n pass","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def on_subnet_del(self, response, subnet_id):\n LOG.info(\"Subnet %s deleted\", subnet_id)\n if subnet_id in self.subnets_by_id:\n del self.subnets_by_id[subnet_id]\n return","def pre_subnet_delete(self, resource_id):\n pass","def post_delete_subnet(self, sender, instance, **kwargs):\n RecurseNetworks.delete_entries(subnet=str(instance.ip_network), net_name=instance.name)","def process_IN_DELETE(self, event):","def test_delete_subnet(self):\n self.assertEqual(\n type(self.the_client.delete_subnet(subnet_id)),\n baidubce.bce_response.BceResponse)","def test_delete_host_subnet(self):\n pass","def process_IN_DELETE_SELF(self, event):","def delete_subnet_precommit(self, mech_context):\n LOG.debug(\"delete_subnetwork_precommit: called\")","def _notify_delete(self, cuds_object):","def on_end(self, resource_id, lease=None):\n\n network_reservation = db_api.network_reservation_get(resource_id)\n reservation_id = network_reservation['reservation_id']\n\n db_api.network_reservation_update(network_reservation['id'],\n {'status': 'completed'})\n allocations = db_api.network_allocation_get_all_by_values(\n reservation_id=reservation_id)\n for allocation in allocations:\n db_api.network_allocation_destroy(allocation['id'])\n network_id = network_reservation['network_id']\n\n # The call to delete must be done without trust_id so the admin role is\n # used\n self.delete_neutron_network(network_id, reservation_id)","def delete_callback(self):\n pass","def delete_subnet(self, subnet):\r\n return self.delete(self.subnet_path % (subnet))","def endMessage(self):","def deserialize_guild_ban_remove_event(\n self, shard: gateway_shard.GatewayShard, payload: data_binding.JSONObject\n ) -> guild_events.BanDeleteEvent:","def on_delete(self, payload):\n pass","async def on_cancel(self, payload):\n\n await self._delete_message(0)\n self.stop()","def deletecollection_namespaced_host_subnet(self, **kwargs):\n\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method deletecollection_namespaced_host_subnet\" % key\n )\n params[key] = val\n del params['kwargs']\n\n\n resource_path = '/oapi/v1/hostsubnets'.replace('{format}', 'json')\n path_params = {}\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n if 'label_selector' in params:\n query_params['labelSelector'] = params['label_selector']\n if 'field_selector' in params:\n query_params['fieldSelector'] = params['field_selector']\n if 'watch' in params:\n query_params['watch'] = params['watch']\n if 'resource_version' in params:\n query_params['resourceVersion'] = params['resource_version']\n if 'timeout_seconds' in params:\n query_params['timeoutSeconds'] = params['timeout_seconds']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='UnversionedStatus',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def delete(self):\n\t\tself.eventStatus = 0\n\t\tself.save()\n\n\t\t# DEBUG\n\t\tcontext = {\n\t\t\t'call_stack': ''.join(traceback.format_stack()),\n\t\t\t'event': self,\n\t\t\t'server_addr': settings.SERVER_ADDRESS,\n\t\t}\n\t\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\n\t\tmail_admins(_('Event Deletion Attempt!'), body)","def network_delete_event(self, network_info):\n\n net_id = network_info['network_id']\n if net_id not in self.network:\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\n net_id)\n return\n\n segid = self.network[net_id].get('segmentation_id')\n tenant_id = self.network[net_id].get('tenant_id')\n tenant_name = self.get_project_name(tenant_id)\n net = utils.Dict2Obj(self.network[net_id])\n if not tenant_name:\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\n {'tenant_id': tenant_id})\n self.update_network_db(net.id, constants.DELETE_FAIL)\n return\n\n try:\n self.dcnm_client.delete_network(tenant_name, net)\n # Put back the segmentation id into the pool.\n self.seg_drvr.release_segmentation_id(segid)\n\n # Remove entry from database and cache.\n self.delete_network_db(net_id)\n del self.network[net_id]\n snets = [k for k in self.subnet if (\n self.subnet[k].get('network_id') == net_id)]\n [self.subnet.pop(s) for s in snets]\n except dexc.DfaClientRequestFailed:\n LOG.error(_LE('Failed to create network %(net)s.'),\n {'net': net.name})\n self.update_network_db(net_id, constants.DELETE_FAIL)\n # deleting all related VMs\n instances = self.get_vms()\n instances_related = [k for k in instances if k.network_id == net_id]\n for vm in instances_related:\n LOG.debug(\"deleting vm %s because network is deleted\", vm.name)\n self.delete_vm_function(vm.port_id, vm)\n self.network_del_notif(tenant_id, tenant_name, net_id)","def post_virtual_network_delete(self, resource_id, resource_dict):\n pass","def delete_handler(event, context):\n delete_endpoint_config(event)","def instance_deleted(sender, instance, **kwargs):\n try:\n if instance.is_registered and not instance.deregister(save=False):\n logger.warn(\"Could not unregister {0} on delete.\".format(\n sender\n ))\n except SNSException:\n # Avoid that invalid arn token cause error when deleting instance\n pass","def test_delete_collection_host_subnet(self):\n pass","def on_account_removal(self, address: ChecksumEthAddress) -> None:\n ...","def on_disconnect(self, raw_msg, server, port, **kwargs):","def delete_namespaced_host_subnet(self, body, name, **kwargs):\n\n all_params = ['body', 'name', 'pretty']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method delete_namespaced_host_subnet\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'body' is set\n if ('body' not in params) or (params['body'] is None):\n raise ValueError(\"Missing the required parameter `body` when calling `delete_namespaced_host_subnet`\")\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `delete_namespaced_host_subnet`\")\n\n resource_path = '/oapi/v1/hostsubnets/{name}'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'pretty' in params:\n query_params['pretty'] = params['pretty']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json', 'application/yaml'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='UnversionedStatus',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def pre_virtual_network_delete(self, resource_id):\n pass","def link_delete_callback(self):\n pass","def notify_end(self, status, objective):\n pass # pragma: no cover","def post_destroy(self) -> Any:\n raise NotImplementedError","def post_loadbalancer_member_delete(self, resource_id, resource_dict):\n pass","def update_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n subnet = self._get_subnet_info(context._subnet)\n if subnet is not None:\n try:\n self.rpc_handler.update_subnet(subnet)\n except:\n pass","def subnet_delete(auth=None, **kwargs):\n cloud = get_operator_cloud(auth)\n kwargs = _clean_kwargs(**kwargs)\n return cloud.delete_subnet(**kwargs)","def on_disconnect(mqttc, obj, rc):\n if rc == 0:\n logger.debug(\"MQTT DISCONNECTED: rc: \" + str(rc))\n logger.debug(\"Disconnected Successfully from MQTT Broker\")","def on_disconnect( client, userdata, rc ):\n logging.info( \"Disconnected from Broker. Returned code: %s\\n\" %rc )\n client.connected_flag = False\n client.disconnect_flag = True","def on_terminate(self):\n pass","def delete(event, context):\n\n if type(event) == str:\n event = json.loads(event)\n\n for record in event['Records']:\n data = json.loads(record['Sns']['Message'])\n\n item = table.get_item(\n Key={\n 'id':data['id']\n }\n )\n\n if 'Item' in item:\n result = table.update_item(\n Key={ 'id': data['id']},\n ExpressionAttributeNames={\n '#is_deleted': 'deleted'\n },\n ExpressionAttributeValues={\n ':delete_flg': True,\n ':updatedAt': str(dt.datetime.utcnow())\n },\n UpdateExpression='SET #is_deleted = :delete_flg, modifiedAt = :updatedAt',\n ReturnValues='ALL_NEW'\n )\n\n\n response = {\n 'statusCode': 200\n }\n\n return response","def on_deleted(self, event):\n\n # the absolute path of the event file/folder\n abs_path = event.src_path\n # replace the root path with a '.' to build a relative path to be sent to server\n relative_event_path = abs_path.replace(self.root_path, \".\")\n\n # retrieve event type and the flag for directory/folder\n event_type = event.event_type\n is_directory = event.is_directory\n\n # only propagate changes if there is a connection with the server\n if self.protocol.connected:\n self.protocol.send_event(event_type, is_directory, relative_event_path)\n else:\n logging.info(\"Connection with server has not been established, 'delete' changes will not be propagated.\")","def onMessageEnd(self):","def listen_del(self, f):\n self._coms.register_leaving_callback(f)","def delete_subnet(self, subnet_id):\n\n LOG_OBJ.debug(\"Deleting subnet %s\" % subnet_id)\n\n _url = \"http://\" + self.host_ip + \":9696/v2.0/subnets/\" + \\\n subnet_id + \".json\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n response = self.request(\"DELETE\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\"No response from Server while deleting subnet:%s\" %\n subnet_id)\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Deletion of subnet Failed with status %s \" %\n response.status)\n return response.status\n\n LOG_OBJ.info(\"Deleted the subnet : %s \" % subnet_id)\n return True","def delete(env, identifier, listener):\n\n mgr = SoftLayer.LoadBalancerManager(env.client)\n uuid, _ = mgr.get_lbaas_uuid_id(identifier)\n try:\n mgr.remove_lb_listener(uuid, listener)\n click.secho(\"Success\", fg='green')\n except SoftLayerAPIError as exception:\n click.secho(f\"ERROR: {exception.faultString}\", fg='red')","def _on_disconnect(self, *args, **kwargs):\n logger.info('Client disconnected %r', self.address)\n self.server.client_disconnected(self)\n # self.unregister()","def after_delete(self, obj, st):\n pass","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def on_delete(self, status_id, user_id):\n log.debug(\"Received status deletion notice: %d\", status_id)","def handle_finish(self, dataset):\n\n logger.info('End of scheduling block message received.')","def dcnm_network_delete_event(self, network_info):\n seg_id = network_info.get('segmentation_id')\n if not seg_id:\n LOG.error(_LE('Failed to delete network. Invalid network '\n 'info %s.'), network_info)\n query_net = self.get_network_by_segid(seg_id)\n if not query_net:\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\n 'does not exist.'), {'segid': seg_id})\n return\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\n LOG.info(_LI(\"Service network %s, returning\"), query_net.name)\n return\n # Send network delete request to neutron\n try:\n del_net = self.network.pop(query_net.network_id)\n self.neutronclient.delete_network(query_net.network_id)\n self.delete_network_db(query_net.network_id)\n except Exception as exc:\n # Failed to delete network.\n # Put back the entry to the local cache???\n self.network[query_net.network_id] = del_net\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\n '%(network)s. Reason %(err)s.'),\n {'network': query_net.name, 'err': str(exc)})","def delete_network_postcommit(self, context):\n if self.rpc_handler is None:\n return\n network = self._get_network_info(context._network)\n for _, _network in network.items():\n network_type = _network.get('network_type', '')\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\n return\n if network is not None:\n try:\n self.rpc_handler.delete_network(network)\n except:\n pass","def unsubscribe(receiver):","def unsubscribe(receiver):","def unsubscribe(receiver):","def unsubscribe(receiver):","def unsubscribe(receiver):","def post_routing_instance_delete(self, resource_id, resource_dict):\n pass","def m_ts_OrderDeleted(self, sender, e):\r\n print(\"Order was deleted.\")","def obj_delete(self, bundle, **kwargs):\n logger.info(\"Deleting acknowledgement...\")\n super(AcknowledgementResource, self).obj_delete(bundle, **kwargs)","def process_quit(message):\n try:\n Resident.objects.get(phone_number=message.sender).delete()\n except Resident.DoesNotExist:\n pass\n \n # TODO - wording...\n message.respond('You have been removed from our system and will no longer get text messages.')\n \n return TropoOkResponse()","def test_delete_on_background_response_descriptor_subscriptions_subscription_subscription_resource(self):\n pass","def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)","async def end_object(self):","def _callback(self, func_end, func_status):\n\n # Check if audit was finished\n if self.__task_id in self.__manager.get_tasks_ids_by_status(status=\"Done\").values():\n\n # Task is finished. Stop the callback interval\n self.__function_handle.set()\n\n # Then, remove the target\n #try:\n #self.delete_target(self.__target_id)\n #except Exception, e:\n #raise VulnscanException(\"Error while try to delete the target %s. Error: %s\" % (self.__target_id, e.message))\n\n # Call the callback function\n if func_end:\n func_end()\n\n if func_status:\n t = self.get_progress(self.__task_id)\n func_status(t)","def delete_entity(self, context, listener):\n resource_path = \"%s/%s/%s\" % (RESOURCE_PREFIX, LISTENERS_RESOURCE,\n listener.id)\n msg = _(\"NetScaler driver listener removal: %s\") % listener.id\n LOG.debug(msg)\n self.client.remove_resource(context.tenant_id, resource_path)","def feed_entry_post_delete(sender, instance, **kwargs):\n del kwargs\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\n try:\n if instance.command_group and instance.command_group.id is not None:\n instance.command_group.delete()\n except CommandGroupEntry.DoesNotExist:\n pass\n\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)","def delete_event(self,widget=None):\n self.on_device_dialog_cancel_clicked()\n return True","def __on_group_deleted(self, logger, *args):","def after_delete(self, record):\n debug = logging.getLogger(__name__).debug\n debug('deleted group %r (%r)', record['name'], record['group_id'])\n audit('delete group', record['name'])","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def event_destroy(tenant_id, user_id=None):","def delete_network_postcommit(self, mech_context):\n\n LOG.debug(\"delete_network_postcommit: called\")\n network = mech_context.current\n network_id = network['id']\n vlan_id = network['provider:segmentation_id']\n tenant_id = network['tenant_id']\n\n for switch_ip in self._switch:\n try:\n system = self.client[switch_ip].system.list()\n system[0].remove_segment(vlan_id)\n except seamicro_client_exception.ClientException as ex:\n LOG.exception(_LE(\"SeaMicr driver: failed to delete network\"\n \" with the following error: %(error)s\"),\n {'error': ex.message})\n raise Exception(\n _(\"Seamicro switch exception, delete_network_postcommit\"\n \" failed\"))\n\n LOG.info(_LI(\"delete network (postcommit): %(network_id)s\"\n \" with vlan = %(vlan_id)s\"\n \" for tenant %(tenant_id)s on switch %(switch_ip)s\"),\n {'network_id': network_id,\n 'vlan_id': vlan_id,\n 'tenant_id': tenant_id,\n 'switch_ip': switch_ip})","def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\n if rc == mqtt.MQTT_ERR_SUCCESS:\n logging.info(\n f\"Successfully disconnected from the broker {self.config.broker}\"\n )\n sys.exit(0)\n else:\n logging.critical(\n f\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\"\n )\n sys.exit(1)","def update_subnet_postcommit(self, mech_context):\n LOG.debug(\"update_subnet_postcommit: called\")","async def on_disconnect(self) -> None:","def END(self):\n log.debug(\"All packets successfully transmitted!\")","def on_disconnect(self):\n # self.node_worker_ref.socketIO.disconnect()\n self.log.info('Disconnected from the server')","def callback_disconnect():\n # if Networking.get_instance().is_host:\n logger.warning(\"It seems that client is not connected...\")\n Networking.get_instance().disconnect()\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))","async def delete_sub(self, sub: TSub) -> None:","def on_quit(self, raw_msg, source, **kwargs):","def event_delete(req):\n event_id = req.match_dict['event_id']\n try:\n db_conn.event_delete(event_id)\n json = {'deleted': True}\n except Exception as e:\n json = {'errors': [str(e)]}\n return req.Response(json=json)","def run(self):\n tenant_id = self.context[\"tenant\"][\"id\"]\n users = self.context[\"tenants\"][tenant_id][\"users\"]\n number = users.index(self.context[\"user\"])\n for network in self.context[\"tenants\"][tenant_id][\"networks\"]:\n # delete one of subnets based on the user sequential number\n subnet_id = network[\"subnets\"][number]\n self.neutron.delete_subnet(subnet_id)","def endTransaction(self, transactionID: int) -> None:\n ...","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","def handler(self):\n\t\tself.exitClient()","def handle_delf_conf(self, job):\n print(\"User deleted a file.\")\n # Delete the file from all servers\n self.put_job_in_all_queues(job)\n\n list_job_results = self.get_internal_results_from_all_servers()\n\n if len(list_job_results) == 0:\n # We got no responses back, there are probably no servers active\n self.put_external_result(self.generate_failure_job(\"Unsuccessful, no servers responded\"))\n return\n\n # Check all the servers had success\n for result in list_job_results:\n if result.result[\"outcome\"] != \"success\":\n self.put_external_result(\n self.generate_failure_job(\"Unsuccessful, one of the servers did not have success\"))\n return\n print(\"All servers deleted file '{}'.\".format(job.data[\"file_name\"]))\n\n # Tell the client we successfully uploaded to all servers\n response_result = copy.deepcopy(list_job_results[0])\n response_result.processed_by = None\n self.put_external_result(response_result)","def post_subnet_create(self, resource_dict):\n pass","def delete(self, downstream = None):\n # emit delete message\n self.deleteSignal.emit(self)","def on_disconnect(client, userdata, rc):\n if rc != 0:\n print(\"Error: Mqtt server disconnection.\")","def on_deleted(self, event):\n\t\tfileInfo = event.src_path.split('/')\n\n\t\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]","def on_leave(self):\n\n self.check_connection.cancel()","def _handle_delete(self):\n\n cookbookapp_functions.delete_chosen_recipe()\n self._handle_return()","def on_chat_end(self, request, trigger_context):\n raise NotImplementedError","def on_disconnect(self):\n print('Client disconnected!')","def delete_listener(self, context, listener):\n LOG.info(\"Received request 'Delete Listener' for LB:%(lb)s \",\n {'lb': listener['loadbalancer_id']})\n arg_dict = {'context': context,\n lb_const.LISTENER: listener,\n }\n self._send_event(lb_const.EVENT_DELETE_LISTENER_V2, arg_dict,\n serialize=True,\n binding_key=listener['loadbalancer_id'],\n key=listener['id'])","def delete(ctx, iface, resource_config, **_):\n vpn_connection = ctx.instance.runtime_properties.get('VPN_CONNECTION_ID')\n cider_block = ctx.instance.runtime_properties.get('DESTINATION_CIDR_BLOCK')\n\n params = dict(VpnConnectionId=vpn_connection,\n DestinationCidrBlock=cider_block) \\\n if not resource_config else resource_config.copy()\n iface.delete(params)"],"string":"[\n \"def delete_subnet_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n try:\\n self.rpc_handler.delete_subnet({str(context._subnet.get('id', '')): {}})\\n except:\\n pass\",\n \"def delete_subnet_postcommit(self, mech_context):\\n LOG.debug(\\\"delete_subnetwork_postcommit: called\\\")\",\n \"def subnet_update_end(self, payload):\\n network_id = payload['subnet']['network_id']\\n self.refresh_dhcp_helper(network_id)\",\n \"def post_subnet_delete(self, resource_id, resource_dict):\\n pass\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def on_subnet_del(self, response, subnet_id):\\n LOG.info(\\\"Subnet %s deleted\\\", subnet_id)\\n if subnet_id in self.subnets_by_id:\\n del self.subnets_by_id[subnet_id]\\n return\",\n \"def pre_subnet_delete(self, resource_id):\\n pass\",\n \"def post_delete_subnet(self, sender, instance, **kwargs):\\n RecurseNetworks.delete_entries(subnet=str(instance.ip_network), net_name=instance.name)\",\n \"def process_IN_DELETE(self, event):\",\n \"def test_delete_subnet(self):\\n self.assertEqual(\\n type(self.the_client.delete_subnet(subnet_id)),\\n baidubce.bce_response.BceResponse)\",\n \"def test_delete_host_subnet(self):\\n pass\",\n \"def process_IN_DELETE_SELF(self, event):\",\n \"def delete_subnet_precommit(self, mech_context):\\n LOG.debug(\\\"delete_subnetwork_precommit: called\\\")\",\n \"def _notify_delete(self, cuds_object):\",\n \"def on_end(self, resource_id, lease=None):\\n\\n network_reservation = db_api.network_reservation_get(resource_id)\\n reservation_id = network_reservation['reservation_id']\\n\\n db_api.network_reservation_update(network_reservation['id'],\\n {'status': 'completed'})\\n allocations = db_api.network_allocation_get_all_by_values(\\n reservation_id=reservation_id)\\n for allocation in allocations:\\n db_api.network_allocation_destroy(allocation['id'])\\n network_id = network_reservation['network_id']\\n\\n # The call to delete must be done without trust_id so the admin role is\\n # used\\n self.delete_neutron_network(network_id, reservation_id)\",\n \"def delete_callback(self):\\n pass\",\n \"def delete_subnet(self, subnet):\\r\\n return self.delete(self.subnet_path % (subnet))\",\n \"def endMessage(self):\",\n \"def deserialize_guild_ban_remove_event(\\n self, shard: gateway_shard.GatewayShard, payload: data_binding.JSONObject\\n ) -> guild_events.BanDeleteEvent:\",\n \"def on_delete(self, payload):\\n pass\",\n \"async def on_cancel(self, payload):\\n\\n await self._delete_message(0)\\n self.stop()\",\n \"def deletecollection_namespaced_host_subnet(self, **kwargs):\\n\\n all_params = ['pretty', 'label_selector', 'field_selector', 'watch', 'resource_version', 'timeout_seconds']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method deletecollection_namespaced_host_subnet\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n\\n resource_path = '/oapi/v1/hostsubnets'.replace('{format}', 'json')\\n path_params = {}\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n if 'label_selector' in params:\\n query_params['labelSelector'] = params['label_selector']\\n if 'field_selector' in params:\\n query_params['fieldSelector'] = params['field_selector']\\n if 'watch' in params:\\n query_params['watch'] = params['watch']\\n if 'resource_version' in params:\\n query_params['resourceVersion'] = params['resource_version']\\n if 'timeout_seconds' in params:\\n query_params['timeoutSeconds'] = params['timeout_seconds']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='UnversionedStatus',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def delete(self):\\n\\t\\tself.eventStatus = 0\\n\\t\\tself.save()\\n\\n\\t\\t# DEBUG\\n\\t\\tcontext = {\\n\\t\\t\\t'call_stack': ''.join(traceback.format_stack()),\\n\\t\\t\\t'event': self,\\n\\t\\t\\t'server_addr': settings.SERVER_ADDRESS,\\n\\t\\t}\\n\\t\\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\\n\\t\\tmail_admins(_('Event Deletion Attempt!'), body)\",\n \"def network_delete_event(self, network_info):\\n\\n net_id = network_info['network_id']\\n if net_id not in self.network:\\n LOG.error(_LE('network_delete_event: net_id %s does not exist.'),\\n net_id)\\n return\\n\\n segid = self.network[net_id].get('segmentation_id')\\n tenant_id = self.network[net_id].get('tenant_id')\\n tenant_name = self.get_project_name(tenant_id)\\n net = utils.Dict2Obj(self.network[net_id])\\n if not tenant_name:\\n LOG.error(_LE('Project %(tenant_id)s does not exist.'),\\n {'tenant_id': tenant_id})\\n self.update_network_db(net.id, constants.DELETE_FAIL)\\n return\\n\\n try:\\n self.dcnm_client.delete_network(tenant_name, net)\\n # Put back the segmentation id into the pool.\\n self.seg_drvr.release_segmentation_id(segid)\\n\\n # Remove entry from database and cache.\\n self.delete_network_db(net_id)\\n del self.network[net_id]\\n snets = [k for k in self.subnet if (\\n self.subnet[k].get('network_id') == net_id)]\\n [self.subnet.pop(s) for s in snets]\\n except dexc.DfaClientRequestFailed:\\n LOG.error(_LE('Failed to create network %(net)s.'),\\n {'net': net.name})\\n self.update_network_db(net_id, constants.DELETE_FAIL)\\n # deleting all related VMs\\n instances = self.get_vms()\\n instances_related = [k for k in instances if k.network_id == net_id]\\n for vm in instances_related:\\n LOG.debug(\\\"deleting vm %s because network is deleted\\\", vm.name)\\n self.delete_vm_function(vm.port_id, vm)\\n self.network_del_notif(tenant_id, tenant_name, net_id)\",\n \"def post_virtual_network_delete(self, resource_id, resource_dict):\\n pass\",\n \"def delete_handler(event, context):\\n delete_endpoint_config(event)\",\n \"def instance_deleted(sender, instance, **kwargs):\\n try:\\n if instance.is_registered and not instance.deregister(save=False):\\n logger.warn(\\\"Could not unregister {0} on delete.\\\".format(\\n sender\\n ))\\n except SNSException:\\n # Avoid that invalid arn token cause error when deleting instance\\n pass\",\n \"def test_delete_collection_host_subnet(self):\\n pass\",\n \"def on_account_removal(self, address: ChecksumEthAddress) -> None:\\n ...\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def delete_namespaced_host_subnet(self, body, name, **kwargs):\\n\\n all_params = ['body', 'name', 'pretty']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method delete_namespaced_host_subnet\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'body' is set\\n if ('body' not in params) or (params['body'] is None):\\n raise ValueError(\\\"Missing the required parameter `body` when calling `delete_namespaced_host_subnet`\\\")\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `delete_namespaced_host_subnet`\\\")\\n\\n resource_path = '/oapi/v1/hostsubnets/{name}'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'pretty' in params:\\n query_params['pretty'] = params['pretty']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'body' in params:\\n body_params = params['body']\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json', 'application/yaml'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='UnversionedStatus',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def pre_virtual_network_delete(self, resource_id):\\n pass\",\n \"def link_delete_callback(self):\\n pass\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def post_destroy(self) -> Any:\\n raise NotImplementedError\",\n \"def post_loadbalancer_member_delete(self, resource_id, resource_dict):\\n pass\",\n \"def update_subnet_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n subnet = self._get_subnet_info(context._subnet)\\n if subnet is not None:\\n try:\\n self.rpc_handler.update_subnet(subnet)\\n except:\\n pass\",\n \"def subnet_delete(auth=None, **kwargs):\\n cloud = get_operator_cloud(auth)\\n kwargs = _clean_kwargs(**kwargs)\\n return cloud.delete_subnet(**kwargs)\",\n \"def on_disconnect(mqttc, obj, rc):\\n if rc == 0:\\n logger.debug(\\\"MQTT DISCONNECTED: rc: \\\" + str(rc))\\n logger.debug(\\\"Disconnected Successfully from MQTT Broker\\\")\",\n \"def on_disconnect( client, userdata, rc ):\\n logging.info( \\\"Disconnected from Broker. Returned code: %s\\\\n\\\" %rc )\\n client.connected_flag = False\\n client.disconnect_flag = True\",\n \"def on_terminate(self):\\n pass\",\n \"def delete(event, context):\\n\\n if type(event) == str:\\n event = json.loads(event)\\n\\n for record in event['Records']:\\n data = json.loads(record['Sns']['Message'])\\n\\n item = table.get_item(\\n Key={\\n 'id':data['id']\\n }\\n )\\n\\n if 'Item' in item:\\n result = table.update_item(\\n Key={ 'id': data['id']},\\n ExpressionAttributeNames={\\n '#is_deleted': 'deleted'\\n },\\n ExpressionAttributeValues={\\n ':delete_flg': True,\\n ':updatedAt': str(dt.datetime.utcnow())\\n },\\n UpdateExpression='SET #is_deleted = :delete_flg, modifiedAt = :updatedAt',\\n ReturnValues='ALL_NEW'\\n )\\n\\n\\n response = {\\n 'statusCode': 200\\n }\\n\\n return response\",\n \"def on_deleted(self, event):\\n\\n # the absolute path of the event file/folder\\n abs_path = event.src_path\\n # replace the root path with a '.' to build a relative path to be sent to server\\n relative_event_path = abs_path.replace(self.root_path, \\\".\\\")\\n\\n # retrieve event type and the flag for directory/folder\\n event_type = event.event_type\\n is_directory = event.is_directory\\n\\n # only propagate changes if there is a connection with the server\\n if self.protocol.connected:\\n self.protocol.send_event(event_type, is_directory, relative_event_path)\\n else:\\n logging.info(\\\"Connection with server has not been established, 'delete' changes will not be propagated.\\\")\",\n \"def onMessageEnd(self):\",\n \"def listen_del(self, f):\\n self._coms.register_leaving_callback(f)\",\n \"def delete_subnet(self, subnet_id):\\n\\n LOG_OBJ.debug(\\\"Deleting subnet %s\\\" % subnet_id)\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/subnets/\\\" + \\\\\\n subnet_id + \\\".json\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.project_info[\\\"token_project\\\"]}\\n _body = None\\n\\n response = self.request(\\\"DELETE\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while deleting subnet:%s\\\" %\\n subnet_id)\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Deletion of subnet Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n LOG_OBJ.info(\\\"Deleted the subnet : %s \\\" % subnet_id)\\n return True\",\n \"def delete(env, identifier, listener):\\n\\n mgr = SoftLayer.LoadBalancerManager(env.client)\\n uuid, _ = mgr.get_lbaas_uuid_id(identifier)\\n try:\\n mgr.remove_lb_listener(uuid, listener)\\n click.secho(\\\"Success\\\", fg='green')\\n except SoftLayerAPIError as exception:\\n click.secho(f\\\"ERROR: {exception.faultString}\\\", fg='red')\",\n \"def _on_disconnect(self, *args, **kwargs):\\n logger.info('Client disconnected %r', self.address)\\n self.server.client_disconnected(self)\\n # self.unregister()\",\n \"def after_delete(self, obj, st):\\n pass\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def on_delete(self, status_id, user_id):\\n log.debug(\\\"Received status deletion notice: %d\\\", status_id)\",\n \"def handle_finish(self, dataset):\\n\\n logger.info('End of scheduling block message received.')\",\n \"def dcnm_network_delete_event(self, network_info):\\n seg_id = network_info.get('segmentation_id')\\n if not seg_id:\\n LOG.error(_LE('Failed to delete network. Invalid network '\\n 'info %s.'), network_info)\\n query_net = self.get_network_by_segid(seg_id)\\n if not query_net:\\n LOG.info(_LI('dcnm_network_delete_event: network %(segid)s '\\n 'does not exist.'), {'segid': seg_id})\\n return\\n if self.fw_api.is_network_source_fw(query_net, query_net.name):\\n LOG.info(_LI(\\\"Service network %s, returning\\\"), query_net.name)\\n return\\n # Send network delete request to neutron\\n try:\\n del_net = self.network.pop(query_net.network_id)\\n self.neutronclient.delete_network(query_net.network_id)\\n self.delete_network_db(query_net.network_id)\\n except Exception as exc:\\n # Failed to delete network.\\n # Put back the entry to the local cache???\\n self.network[query_net.network_id] = del_net\\n LOG.exception(_LE('dcnm_network_delete_event: Failed to delete '\\n '%(network)s. Reason %(err)s.'),\\n {'network': query_net.name, 'err': str(exc)})\",\n \"def delete_network_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n network = self._get_network_info(context._network)\\n for _, _network in network.items():\\n network_type = _network.get('network_type', '')\\n if network_type not in CentecConstant.SUPPORTED_NETWORK_TYPES and len(CentecConstant.SUPPORTED_NETWORK_TYPES) > 0:\\n return\\n if network is not None:\\n try:\\n self.rpc_handler.delete_network(network)\\n except:\\n pass\",\n \"def unsubscribe(receiver):\",\n \"def unsubscribe(receiver):\",\n \"def unsubscribe(receiver):\",\n \"def unsubscribe(receiver):\",\n \"def unsubscribe(receiver):\",\n \"def post_routing_instance_delete(self, resource_id, resource_dict):\\n pass\",\n \"def m_ts_OrderDeleted(self, sender, e):\\r\\n print(\\\"Order was deleted.\\\")\",\n \"def obj_delete(self, bundle, **kwargs):\\n logger.info(\\\"Deleting acknowledgement...\\\")\\n super(AcknowledgementResource, self).obj_delete(bundle, **kwargs)\",\n \"def process_quit(message):\\n try:\\n Resident.objects.get(phone_number=message.sender).delete()\\n except Resident.DoesNotExist:\\n pass\\n \\n # TODO - wording...\\n message.respond('You have been removed from our system and will no longer get text messages.')\\n \\n return TropoOkResponse()\",\n \"def test_delete_on_background_response_descriptor_subscriptions_subscription_subscription_resource(self):\\n pass\",\n \"def network_update_end(self, payload):\\n network_id = payload['network']['id']\\n if payload['network']['admin_state_up']:\\n self.enable_dhcp_helper(network_id)\\n else:\\n self.disable_dhcp_helper(network_id)\",\n \"async def end_object(self):\",\n \"def _callback(self, func_end, func_status):\\n\\n # Check if audit was finished\\n if self.__task_id in self.__manager.get_tasks_ids_by_status(status=\\\"Done\\\").values():\\n\\n # Task is finished. Stop the callback interval\\n self.__function_handle.set()\\n\\n # Then, remove the target\\n #try:\\n #self.delete_target(self.__target_id)\\n #except Exception, e:\\n #raise VulnscanException(\\\"Error while try to delete the target %s. Error: %s\\\" % (self.__target_id, e.message))\\n\\n # Call the callback function\\n if func_end:\\n func_end()\\n\\n if func_status:\\n t = self.get_progress(self.__task_id)\\n func_status(t)\",\n \"def delete_entity(self, context, listener):\\n resource_path = \\\"%s/%s/%s\\\" % (RESOURCE_PREFIX, LISTENERS_RESOURCE,\\n listener.id)\\n msg = _(\\\"NetScaler driver listener removal: %s\\\") % listener.id\\n LOG.debug(msg)\\n self.client.remove_resource(context.tenant_id, resource_path)\",\n \"def feed_entry_post_delete(sender, instance, **kwargs):\\n del kwargs\\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\\n try:\\n if instance.command_group and instance.command_group.id is not None:\\n instance.command_group.delete()\\n except CommandGroupEntry.DoesNotExist:\\n pass\\n\\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)\",\n \"def delete_event(self,widget=None):\\n self.on_device_dialog_cancel_clicked()\\n return True\",\n \"def __on_group_deleted(self, logger, *args):\",\n \"def after_delete(self, record):\\n debug = logging.getLogger(__name__).debug\\n debug('deleted group %r (%r)', record['name'], record['group_id'])\\n audit('delete group', record['name'])\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def event_destroy(tenant_id, user_id=None):\",\n \"def delete_network_postcommit(self, mech_context):\\n\\n LOG.debug(\\\"delete_network_postcommit: called\\\")\\n network = mech_context.current\\n network_id = network['id']\\n vlan_id = network['provider:segmentation_id']\\n tenant_id = network['tenant_id']\\n\\n for switch_ip in self._switch:\\n try:\\n system = self.client[switch_ip].system.list()\\n system[0].remove_segment(vlan_id)\\n except seamicro_client_exception.ClientException as ex:\\n LOG.exception(_LE(\\\"SeaMicr driver: failed to delete network\\\"\\n \\\" with the following error: %(error)s\\\"),\\n {'error': ex.message})\\n raise Exception(\\n _(\\\"Seamicro switch exception, delete_network_postcommit\\\"\\n \\\" failed\\\"))\\n\\n LOG.info(_LI(\\\"delete network (postcommit): %(network_id)s\\\"\\n \\\" with vlan = %(vlan_id)s\\\"\\n \\\" for tenant %(tenant_id)s on switch %(switch_ip)s\\\"),\\n {'network_id': network_id,\\n 'vlan_id': vlan_id,\\n 'tenant_id': tenant_id,\\n 'switch_ip': switch_ip})\",\n \"def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\\n if rc == mqtt.MQTT_ERR_SUCCESS:\\n logging.info(\\n f\\\"Successfully disconnected from the broker {self.config.broker}\\\"\\n )\\n sys.exit(0)\\n else:\\n logging.critical(\\n f\\\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\\\"\\n )\\n sys.exit(1)\",\n \"def update_subnet_postcommit(self, mech_context):\\n LOG.debug(\\\"update_subnet_postcommit: called\\\")\",\n \"async def on_disconnect(self) -> None:\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"def on_disconnect(self):\\n # self.node_worker_ref.socketIO.disconnect()\\n self.log.info('Disconnected from the server')\",\n \"def callback_disconnect():\\n # if Networking.get_instance().is_host:\\n logger.warning(\\\"It seems that client is not connected...\\\")\\n Networking.get_instance().disconnect()\\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))\",\n \"async def delete_sub(self, sub: TSub) -> None:\",\n \"def on_quit(self, raw_msg, source, **kwargs):\",\n \"def event_delete(req):\\n event_id = req.match_dict['event_id']\\n try:\\n db_conn.event_delete(event_id)\\n json = {'deleted': True}\\n except Exception as e:\\n json = {'errors': [str(e)]}\\n return req.Response(json=json)\",\n \"def run(self):\\n tenant_id = self.context[\\\"tenant\\\"][\\\"id\\\"]\\n users = self.context[\\\"tenants\\\"][tenant_id][\\\"users\\\"]\\n number = users.index(self.context[\\\"user\\\"])\\n for network in self.context[\\\"tenants\\\"][tenant_id][\\\"networks\\\"]:\\n # delete one of subnets based on the user sequential number\\n subnet_id = network[\\\"subnets\\\"][number]\\n self.neutron.delete_subnet(subnet_id)\",\n \"def endTransaction(self, transactionID: int) -> None:\\n ...\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"def handler(self):\\n\\t\\tself.exitClient()\",\n \"def handle_delf_conf(self, job):\\n print(\\\"User deleted a file.\\\")\\n # Delete the file from all servers\\n self.put_job_in_all_queues(job)\\n\\n list_job_results = self.get_internal_results_from_all_servers()\\n\\n if len(list_job_results) == 0:\\n # We got no responses back, there are probably no servers active\\n self.put_external_result(self.generate_failure_job(\\\"Unsuccessful, no servers responded\\\"))\\n return\\n\\n # Check all the servers had success\\n for result in list_job_results:\\n if result.result[\\\"outcome\\\"] != \\\"success\\\":\\n self.put_external_result(\\n self.generate_failure_job(\\\"Unsuccessful, one of the servers did not have success\\\"))\\n return\\n print(\\\"All servers deleted file '{}'.\\\".format(job.data[\\\"file_name\\\"]))\\n\\n # Tell the client we successfully uploaded to all servers\\n response_result = copy.deepcopy(list_job_results[0])\\n response_result.processed_by = None\\n self.put_external_result(response_result)\",\n \"def post_subnet_create(self, resource_dict):\\n pass\",\n \"def delete(self, downstream = None):\\n # emit delete message\\n self.deleteSignal.emit(self)\",\n \"def on_disconnect(client, userdata, rc):\\n if rc != 0:\\n print(\\\"Error: Mqtt server disconnection.\\\")\",\n \"def on_deleted(self, event):\\n\\t\\tfileInfo = event.src_path.split('/')\\n\\n\\t\\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]\",\n \"def on_leave(self):\\n\\n self.check_connection.cancel()\",\n \"def _handle_delete(self):\\n\\n cookbookapp_functions.delete_chosen_recipe()\\n self._handle_return()\",\n \"def on_chat_end(self, request, trigger_context):\\n raise NotImplementedError\",\n \"def on_disconnect(self):\\n print('Client disconnected!')\",\n \"def delete_listener(self, context, listener):\\n LOG.info(\\\"Received request 'Delete Listener' for LB:%(lb)s \\\",\\n {'lb': listener['loadbalancer_id']})\\n arg_dict = {'context': context,\\n lb_const.LISTENER: listener,\\n }\\n self._send_event(lb_const.EVENT_DELETE_LISTENER_V2, arg_dict,\\n serialize=True,\\n binding_key=listener['loadbalancer_id'],\\n key=listener['id'])\",\n \"def delete(ctx, iface, resource_config, **_):\\n vpn_connection = ctx.instance.runtime_properties.get('VPN_CONNECTION_ID')\\n cider_block = ctx.instance.runtime_properties.get('DESTINATION_CIDR_BLOCK')\\n\\n params = dict(VpnConnectionId=vpn_connection,\\n DestinationCidrBlock=cider_block) \\\\\\n if not resource_config else resource_config.copy()\\n iface.delete(params)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6993552","0.6756371","0.67003113","0.6559159","0.647308","0.6463808","0.6403783","0.63142115","0.6309298","0.62003213","0.57914406","0.5777481","0.57660466","0.57313883","0.57229215","0.565757","0.5645164","0.55317897","0.55291295","0.5499232","0.54850763","0.54587185","0.5454341","0.5414097","0.54126537","0.54118073","0.54042643","0.53923815","0.53854066","0.5384291","0.53560466","0.53293824","0.5312569","0.5294307","0.52889013","0.5285065","0.5282995","0.524918","0.52268386","0.52121675","0.51987755","0.5197878","0.5197026","0.51956284","0.5192058","0.5171192","0.5163615","0.51503843","0.5146506","0.51393545","0.51370096","0.5134573","0.51270527","0.5117514","0.5116489","0.5112","0.5112","0.5112","0.5112","0.5112","0.510747","0.5096558","0.509382","0.5087956","0.5076523","0.50597996","0.5056331","0.5042653","0.5036142","0.50353396","0.5000952","0.49921075","0.4988681","0.498154","0.497979","0.4975545","0.4972992","0.49723774","0.49711657","0.4971115","0.4966214","0.49641246","0.4957848","0.49572214","0.4954829","0.49495777","0.49472773","0.49444246","0.49427325","0.4941485","0.49366835","0.49357596","0.492713","0.4925707","0.49256948","0.49215412","0.4920911","0.4914764","0.49110404","0.49108696"],"string":"[\n \"0.6993552\",\n \"0.6756371\",\n \"0.67003113\",\n \"0.6559159\",\n \"0.647308\",\n \"0.6463808\",\n \"0.6403783\",\n \"0.63142115\",\n \"0.6309298\",\n \"0.62003213\",\n \"0.57914406\",\n \"0.5777481\",\n \"0.57660466\",\n \"0.57313883\",\n \"0.57229215\",\n \"0.565757\",\n \"0.5645164\",\n \"0.55317897\",\n \"0.55291295\",\n \"0.5499232\",\n \"0.54850763\",\n \"0.54587185\",\n \"0.5454341\",\n \"0.5414097\",\n \"0.54126537\",\n \"0.54118073\",\n \"0.54042643\",\n \"0.53923815\",\n \"0.53854066\",\n \"0.5384291\",\n \"0.53560466\",\n \"0.53293824\",\n \"0.5312569\",\n \"0.5294307\",\n \"0.52889013\",\n \"0.5285065\",\n \"0.5282995\",\n \"0.524918\",\n \"0.52268386\",\n \"0.52121675\",\n \"0.51987755\",\n \"0.5197878\",\n \"0.5197026\",\n \"0.51956284\",\n \"0.5192058\",\n \"0.5171192\",\n \"0.5163615\",\n \"0.51503843\",\n \"0.5146506\",\n \"0.51393545\",\n \"0.51370096\",\n \"0.5134573\",\n \"0.51270527\",\n \"0.5117514\",\n \"0.5116489\",\n \"0.5112\",\n \"0.5112\",\n \"0.5112\",\n \"0.5112\",\n \"0.5112\",\n \"0.510747\",\n \"0.5096558\",\n \"0.509382\",\n \"0.5087956\",\n \"0.5076523\",\n \"0.50597996\",\n \"0.5056331\",\n \"0.5042653\",\n \"0.5036142\",\n \"0.50353396\",\n \"0.5000952\",\n \"0.49921075\",\n \"0.4988681\",\n \"0.498154\",\n \"0.497979\",\n \"0.4975545\",\n \"0.4972992\",\n \"0.49723774\",\n \"0.49711657\",\n \"0.4971115\",\n \"0.4966214\",\n \"0.49641246\",\n \"0.4957848\",\n \"0.49572214\",\n \"0.4954829\",\n \"0.49495777\",\n \"0.49472773\",\n \"0.49444246\",\n \"0.49427325\",\n \"0.4941485\",\n \"0.49366835\",\n \"0.49357596\",\n \"0.492713\",\n \"0.4925707\",\n \"0.49256948\",\n \"0.49215412\",\n \"0.4920911\",\n \"0.4914764\",\n \"0.49110404\",\n \"0.49108696\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7865546"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79795,"cells":{"query":{"kind":"string","value":"Handle the port.update.end notification event."},"document":{"kind":"string","value":"def port_update_end(self, payload):\n port = DictModel(payload['port'])\n network = self.cache.get_network_by_id(port.network_id)\n if network:\n self.cache.put_port(port)\n self.call_driver('reload_allocations', network)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def onMessageEnd(self):","def end(update: Update, context: CallbackContext) -> int:\n update.callback_query.answer()\n\n text = \"See you around!\"\n update.callback_query.edit_message_text(text=text)\n\n return END","def end(update, context) -> int:\n update.callback_query.edit_message_text(\n 'Bye! I hope we can talk again some day.')\n\n logger.info(\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\",\n update.callback_query.message.chat.first_name)\n return ConversationHandler.END","def notify_end(self, status, objective):\n pass # pragma: no cover","def on_update(self, event, signal):\n t = ppb.get_time() - self.start_time\n if t >= self.duration:\n signal(ppb.events.Quit())","def bcp_ball_end(self, **kwargs):\n self.events.post('ball_ended', **kwargs)","def notify_finish_event(self):\n self.notify(self._finish_event_type())","def processEnded(self, reason):\n self._processEnded = True\n if self.onProcessEnd:\n d, self.onProcessEnd = self.onProcessEnd, None\n d.callback(None)","def processEnded(self, reason):\n if not self.empty:\n self.output.dataReceived('\\n')\n self.service.processEnded(self.name)","def endMessage(self):","def api_end_game(self):\n pass","def handle_port_update_event(sel, asic_context, stop_event, logger, port_change_event_handler):\n if not stop_event.is_set():\n (state, _) = sel.select(SELECT_TIMEOUT_MSECS)\n if state == swsscommon.Select.TIMEOUT:\n return\n if state != swsscommon.Select.OBJECT:\n logger.log_warning('sel.select() did not return swsscommon.Select.OBJECT')\n return\n for port_tbl in asic_context.keys():\n while True:\n (key, op, fvp) = port_tbl.pop()\n if not key:\n break\n if not validate_port(key):\n continue\n fvp = dict(fvp) if fvp is not None else {}\n if 'index' not in fvp:\n fvp['index'] = '-1'\n port_index = int(fvp['index'])\n port_change_event = None\n if op == swsscommon.SET_COMMAND:\n port_change_event = PortChangeEvent(key,\n port_index,\n asic_context[port_tbl],\n PortChangeEvent.PORT_SET,\n fvp)\n elif op == swsscommon.DEL_COMMAND:\n port_change_event = PortChangeEvent(key,\n port_index,\n asic_context[port_tbl],\n PortChangeEvent.PORT_DEL,\n fvp)\n if port_change_event is not None:\n port_change_event_handler(port_change_event)","def handle_updates(self, update):\r\n self.__manage_pump()","def touch_ended(self, touch):\n\t\tpass","def update_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.update_port(port)\n except:\n pass","def process_update_port(self, context, data, result):\n\n orginal_exten = copy.deepcopy(result)\n # Process extension data\n self._find_port_dict_extensions(\n result, None, session=context.session)\n\n port_ext = self._update_port_ext(\n result, data, session=context.session)\n switchports = self._update_switchports(\n result, data, session=context.session)\n self._find_port_dict_extensions(\n result, None, port_ext=port_ext,\n switchports=switchports, session=context.session)\n\n # We only want to commit on a state change\n if orginal_exten.get(\"commit\") != result[\"commit\"]:\n # If we are transitioning to active, validate\n if not orginal_exten.get(\"commit\") and result[\"commit\"]:\n self._validate_port_can_commit(\n result, None, session=context.session)","def _end(self):\n\n self.logger.msg1(\"Done\")","def on_epoch_end(self):\n pass","def sync_end(self):","def _onEnd(self, name, completed):\n logging.debug(\"onEnd...\")","def end(update, context):\n query = update.callback_query\n bot = context.bot\n bot.edit_message_text(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\"See you next time!\"\n )\n return ConversationHandler.END","def onMessageFrameEnd(self):","def after_update(self, *args):\n raise NotImplementedError","def end(self):\n ...","def on_end(self, ctx):\n pass","def stop(update: Update, context: CallbackContext) -> int:\n update.message.reply_text(\"Okay, bye.\")\n\n return END","def on_connection_end() -> None:\r\n print(\"Connection lost with G-Earth\")\r\n print()","def onTimeStepEnd(self, timeStep):\n pass","def _notify_listeners_end_operation(self):\n op = self.operation\n for lstnr in self.listeners:\n lstnr.end_operation(self, op)\n self.operation = None","def end(self):\n\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\n # 3 minutes before giving up.\n if self.device_details['device_type'] == 'PBR':\n pump_id = self.device_details['setup']['pump_id']\n counter = 0\n while counter < 60:\n try:\n result = True, self.device.set_pump_state(pump_id, False)\n except Exception as e:\n result = False, e\n\n response = [(datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")),\n self.device_details['node_id'],\n self.device_details['device_type'],\n 8,\n [pump_id, False],\n result,\n 'internal']\n\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\n\n # end the loop if the pump has been successfully turned off\n if result[0]:\n break\n else:\n counter += 1\n sleep(3)\n\n self.device.disconnect()","def port_end(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"port_end\")","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def bcp_game_end(self, **kwargs):\n self.player = None\n self.events.post('game_ended', **kwargs)","def on_epoch_end(self):","def complete(self, update):\n self.reactor.callFromThread(self._complete, update)","def end(event: EventType, widget: WidgetType) -> bool:\n return event.key == _locals.K_END","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","def onEnd(self, agent):\n\n pass","async def on_end(self, game_result: Result):","def on_session_ended():\n #print(\"on_session_ended\")","def on_session_ended():\n #print(\"on_session_ended\")","def ev_quit(self, event: Quit) -> None:","def end(self):\n self.my_print(\"\\t[DONE]\", msg_types.INFO)\n self.in_progress = False","def end_box(self):\n self.end_hooks.fire(EndEvent())","def on_epoch_end(self):\n self.epoch_bar.update()","def end(self):\n pass","def end(self):\n pass","def end(self):\n pass","def on_connection_closed(self):","def state_finish_do(cfg, app, win, events):","def on_chat_end(self, request, trigger_context):\n raise NotImplementedError","def _telegram_stop_callback(self, update: Update, _: CallbackContext):\n\n rospy.loginfo(\"Stopping Telegram ROS bridge for chat id {}\".format(self._telegram_chat_id))\n update.message.reply_text(\n \"Disconnecting chat_id {}. So long and thanks for all the fish!\"\n \" Type /start to reconnect\".format(self._telegram_chat_id)\n )\n self._telegram_chat_id = None","async def callback_homeassistant_stop(self, event: \"Event\") -> NoReturn:\n _LOGGER.debug(\"Hekr system is shutting down\")\n for device_id, device in self.devices.items():\n connector = device.connector\n listener = connector.listener\n if listener is not None and listener.is_running:\n _LOGGER.debug('Shutting down listener for device ID \"%s\"' % device_id)\n listener.stop()\n\n if connector.is_connected:\n _LOGGER.debug('Shutting down connector for device ID \"%s\"' % device_id)\n await connector.close_connection()","def on_event_finished(self, event):","def SendEndScanSignal(self):\n pass","def handle_update(self, call):\n self.fire_event(EVENT_UPDATE)","def end(self):\n self.shutdown = True","def end(self) -> None:","def _async_update_callback(self):\n self._async_update_device_data()\n self.async_write_ha_state()","def update_port_postcommit(self, mech_context):\n LOG.debug(\"update_port_postcommit: called\")","def close_event(device_int):\n command = \"\"\"\n SELECT * FROM `FYP Data`.interface_events\n where interface_id = {} and state ='active' and issue = 'out utilization'\"\"\".format(device_int[\"db_id\"])\n event = get_data(command)\n command = \"\"\"\n UPDATE `FYP Data`.`interface_events`\n SET `state` = 'resolved'\n WHERE event_id = {} ;\"\"\".format(event[0][0])\n set_data_mysql(command)\n update_interface_and_history(device_int)","def onEpochEnd(self, agent):\n\n pass","def _on_stop(self, _):\n self.unit.status = MaintenanceStatus(\"Pod is terminating.\")\n logger.info(\"Pod is terminating.\")","def process_IN_CLOSE_WRITE(self, event):","def processEnded(self, reason):\n return self.runAppCallbacks(reason)","def end(self, won, reason):\n pass\n # replace with your end logic","async def after_update_callback(self, _: XknxConnectionState) -> None:\n self.async_write_ha_state()","def end(self):\n if self.flowComponent:\n self.flowComponent.end()\n pass","def end_message(self):\r\n if self.cur_message is not None:\r\n self.cur_message.end_time = datetime.now()\r\n self.wait_message()","def TTAPI_ShutdownCompleted(self, sender, e):\r\n # Shutdown the Dispatcher\r\n if self._m_disp != None:\r\n self._m_disp.BeginInvokeShutdown()\r\n self._m_disp = None","def episode_end(self):\n return self.game.is_episode_finished()","def OnQuit(self, e):\n\t\tself.EndRun()","def end(self) -> None:\n self.process_event(\n PipelineEvent(\n PipelineEventType.RUN_END,\n )\n )","def end(self):\n self.send_all(\"SHUTDOWN\") #On indique a tout le monde qu'on ferme\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()","def on_epoch_end(self):\n pass","def set_update_received_callback(self, callback):\n self.__update_received = callback","def end(self):\n self._log.debug('doing ..')\n super().end()\n\n self._servo.end()\n self._mtr.end()\n self._log.debug('done')","def on_disconnect(self, raw_msg, server, port, **kwargs):","def set_end(self, on = True):\n self._end = on","def tellIfEnded(self):\n self.congratulate()","def _handle_service_end(node: int, system: System, params: Params,\n records: Records):\n time_now = system.time\n server = system.get_server(node)\n queue = system.get_queue(node)\n\n # Extract the packet from the server and mark it as served:\n packet = server.pop()\n packet.service_finished[node] = time_now\n packet.was_served[node] = True\n\n # If this node is the target, then packet is delivered.\n # Otherwise, pass it to the next node.\n if packet.target == node:\n packet.was_delivered = True\n packet.delivery_time = time_now\n elif node < params.num_stations - 1:\n _process_packet(node + 1, packet, system, params, records)\n else:\n raise RuntimeError(\"shouldn't arrive here: packet reached network\"\n \"end, and this is not its destination!!\")\n\n # Start serving next packet, if exists:\n if (packet := queue.pop()) is not None:\n server.serve(packet)\n packet.service_started[node] = time_now\n system.schedule(Event.SERVICE_END, params.services[node](), node)\n\n # Record new system size:\n records.get_system_size(node).add(time_now, system.get_size(node))","def END(self):\n log.debug(\"All packets successfully transmitted!\")","def sendEnd(self):\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))","def TTAPI_ShutdownCompleted(self, sender, e):\r\n # Shutdown the Dispatcher\r\n if self.m_disp != None:\r\n self.m_disp.BeginInvokeShutdown()\r\n self.m_disp = None","def episode_finish(self, reason: str) -> None:\n pass","def _termination_handler(self, signum, frame):\n print '[i] Shutting down...'\n self.sensor.close()\n sys.exit(1)","def do_end(end):\n if end:\n do_action(end)","def on_endpoint_final(self, data):\n logger.debug('Final data from endpoint')","def handleResponseEnd(self):\r\n try:\r\n if not self._finished:\r\n reactor.callInThread(\r\n self.resource.cacheContent,\r\n self.father,\r\n self._response,\r\n self.buffer\r\n )\r\n proxy.ProxyClient.handleResponseEnd(self)\r\n except RuntimeError:\r\n # because we don't care if the user hits\r\n # refresh before the request is done\r\n pass","def update_port_postcommit(self, port_context):\n # TODO(ijw): optimisation: the update port may leave the\n # binding state the same as before if someone updated\n # something other than the binding on the port, but this\n # way we always send it out and it's the far end's job to\n # ignore it. Doing less work is nevertheless good, so we\n # should in future avoid the send.\n\n if port_context.binding_levels is not None:\n current_bind = port_context.binding_levels[-1]\n if port_context.original_binding_levels is None:\n prev_bind = None\n else:\n prev_bind = port_context.original_binding_levels[-1]\n\n if (current_bind is not None and\n current_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\n self.communicator.kick()\n elif (prev_bind is not None and\n prev_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\n self.communicator.kick()","def process_employee_exit(self):\n if self.is_employee_serving():\n self._end_date.append(datetime.now().isoformat())\n\n print(f\"Successfully processed exit for employee {self.name} on\" \\\n f\"{self._end_date[-1]}\\nWe wish {self.name} for future endeavours\")\n return\n raise RejoiningException(\"Employee not in service. Cannot process exit.\")","def update_callback(self, output_id=0):\n if output_id == 0 | output_id == self._output_id:\n _LOGGER.info(\"SmartVideoHub sent a status update for output %i\", output_id)\n self.update()\n self.schedule_update_ha_state(False)","def end():\n logging.info(\"Execution Ended\")","def on_epoch_end(self, epoch, logs=None):","def on_epoch_end(self, epoch, logs=None):","def sendSslEnd(self, req):\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))","def done_action(self) -> None:\n self.end = datetime.now()","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def on_epoch_end(self):\n for callback in self.callbacks:\n callback.on_epoch_end(self, self.get_model())"],"string":"[\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def onMessageEnd(self):\",\n \"def end(update: Update, context: CallbackContext) -> int:\\n update.callback_query.answer()\\n\\n text = \\\"See you around!\\\"\\n update.callback_query.edit_message_text(text=text)\\n\\n return END\",\n \"def end(update, context) -> int:\\n update.callback_query.edit_message_text(\\n 'Bye! I hope we can talk again some day.')\\n\\n logger.info(\\\"User [%s] exited the conversation, [Exit], from [Main Menu / Results State].\\\",\\n update.callback_query.message.chat.first_name)\\n return ConversationHandler.END\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def on_update(self, event, signal):\\n t = ppb.get_time() - self.start_time\\n if t >= self.duration:\\n signal(ppb.events.Quit())\",\n \"def bcp_ball_end(self, **kwargs):\\n self.events.post('ball_ended', **kwargs)\",\n \"def notify_finish_event(self):\\n self.notify(self._finish_event_type())\",\n \"def processEnded(self, reason):\\n self._processEnded = True\\n if self.onProcessEnd:\\n d, self.onProcessEnd = self.onProcessEnd, None\\n d.callback(None)\",\n \"def processEnded(self, reason):\\n if not self.empty:\\n self.output.dataReceived('\\\\n')\\n self.service.processEnded(self.name)\",\n \"def endMessage(self):\",\n \"def api_end_game(self):\\n pass\",\n \"def handle_port_update_event(sel, asic_context, stop_event, logger, port_change_event_handler):\\n if not stop_event.is_set():\\n (state, _) = sel.select(SELECT_TIMEOUT_MSECS)\\n if state == swsscommon.Select.TIMEOUT:\\n return\\n if state != swsscommon.Select.OBJECT:\\n logger.log_warning('sel.select() did not return swsscommon.Select.OBJECT')\\n return\\n for port_tbl in asic_context.keys():\\n while True:\\n (key, op, fvp) = port_tbl.pop()\\n if not key:\\n break\\n if not validate_port(key):\\n continue\\n fvp = dict(fvp) if fvp is not None else {}\\n if 'index' not in fvp:\\n fvp['index'] = '-1'\\n port_index = int(fvp['index'])\\n port_change_event = None\\n if op == swsscommon.SET_COMMAND:\\n port_change_event = PortChangeEvent(key,\\n port_index,\\n asic_context[port_tbl],\\n PortChangeEvent.PORT_SET,\\n fvp)\\n elif op == swsscommon.DEL_COMMAND:\\n port_change_event = PortChangeEvent(key,\\n port_index,\\n asic_context[port_tbl],\\n PortChangeEvent.PORT_DEL,\\n fvp)\\n if port_change_event is not None:\\n port_change_event_handler(port_change_event)\",\n \"def handle_updates(self, update):\\r\\n self.__manage_pump()\",\n \"def touch_ended(self, touch):\\n\\t\\tpass\",\n \"def update_port_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n port = self._get_port_info(context)\\n if port is not None:\\n try:\\n self.rpc_handler.update_port(port)\\n except:\\n pass\",\n \"def process_update_port(self, context, data, result):\\n\\n orginal_exten = copy.deepcopy(result)\\n # Process extension data\\n self._find_port_dict_extensions(\\n result, None, session=context.session)\\n\\n port_ext = self._update_port_ext(\\n result, data, session=context.session)\\n switchports = self._update_switchports(\\n result, data, session=context.session)\\n self._find_port_dict_extensions(\\n result, None, port_ext=port_ext,\\n switchports=switchports, session=context.session)\\n\\n # We only want to commit on a state change\\n if orginal_exten.get(\\\"commit\\\") != result[\\\"commit\\\"]:\\n # If we are transitioning to active, validate\\n if not orginal_exten.get(\\\"commit\\\") and result[\\\"commit\\\"]:\\n self._validate_port_can_commit(\\n result, None, session=context.session)\",\n \"def _end(self):\\n\\n self.logger.msg1(\\\"Done\\\")\",\n \"def on_epoch_end(self):\\n pass\",\n \"def sync_end(self):\",\n \"def _onEnd(self, name, completed):\\n logging.debug(\\\"onEnd...\\\")\",\n \"def end(update, context):\\n query = update.callback_query\\n bot = context.bot\\n bot.edit_message_text(\\n chat_id=query.message.chat_id,\\n message_id=query.message.message_id,\\n text=\\\"See you next time!\\\"\\n )\\n return ConversationHandler.END\",\n \"def onMessageFrameEnd(self):\",\n \"def after_update(self, *args):\\n raise NotImplementedError\",\n \"def end(self):\\n ...\",\n \"def on_end(self, ctx):\\n pass\",\n \"def stop(update: Update, context: CallbackContext) -> int:\\n update.message.reply_text(\\\"Okay, bye.\\\")\\n\\n return END\",\n \"def on_connection_end() -> None:\\r\\n print(\\\"Connection lost with G-Earth\\\")\\r\\n print()\",\n \"def onTimeStepEnd(self, timeStep):\\n pass\",\n \"def _notify_listeners_end_operation(self):\\n op = self.operation\\n for lstnr in self.listeners:\\n lstnr.end_operation(self, op)\\n self.operation = None\",\n \"def end(self):\\n\\n # if the device is a PBR, try to turn off the pump before ending. In case of problems, keep trying for\\n # 3 minutes before giving up.\\n if self.device_details['device_type'] == 'PBR':\\n pump_id = self.device_details['setup']['pump_id']\\n counter = 0\\n while counter < 60:\\n try:\\n result = True, self.device.set_pump_state(pump_id, False)\\n except Exception as e:\\n result = False, e\\n\\n response = [(datetime.datetime.utcnow().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")),\\n self.device_details['node_id'],\\n self.device_details['device_type'],\\n 8,\\n [pump_id, False],\\n result,\\n 'internal']\\n\\n self.log.update_log(*response) # log the change in pump state (or the cause of failure)\\n\\n # end the loop if the pump has been successfully turned off\\n if result[0]:\\n break\\n else:\\n counter += 1\\n sleep(3)\\n\\n self.device.disconnect()\",\n \"def port_end(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"port_end\\\")\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def bcp_game_end(self, **kwargs):\\n self.player = None\\n self.events.post('game_ended', **kwargs)\",\n \"def on_epoch_end(self):\",\n \"def complete(self, update):\\n self.reactor.callFromThread(self._complete, update)\",\n \"def end(event: EventType, widget: WidgetType) -> bool:\\n return event.key == _locals.K_END\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"def onEnd(self, agent):\\n\\n pass\",\n \"async def on_end(self, game_result: Result):\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def on_session_ended():\\n #print(\\\"on_session_ended\\\")\",\n \"def ev_quit(self, event: Quit) -> None:\",\n \"def end(self):\\n self.my_print(\\\"\\\\t[DONE]\\\", msg_types.INFO)\\n self.in_progress = False\",\n \"def end_box(self):\\n self.end_hooks.fire(EndEvent())\",\n \"def on_epoch_end(self):\\n self.epoch_bar.update()\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def end(self):\\n pass\",\n \"def on_connection_closed(self):\",\n \"def state_finish_do(cfg, app, win, events):\",\n \"def on_chat_end(self, request, trigger_context):\\n raise NotImplementedError\",\n \"def _telegram_stop_callback(self, update: Update, _: CallbackContext):\\n\\n rospy.loginfo(\\\"Stopping Telegram ROS bridge for chat id {}\\\".format(self._telegram_chat_id))\\n update.message.reply_text(\\n \\\"Disconnecting chat_id {}. So long and thanks for all the fish!\\\"\\n \\\" Type /start to reconnect\\\".format(self._telegram_chat_id)\\n )\\n self._telegram_chat_id = None\",\n \"async def callback_homeassistant_stop(self, event: \\\"Event\\\") -> NoReturn:\\n _LOGGER.debug(\\\"Hekr system is shutting down\\\")\\n for device_id, device in self.devices.items():\\n connector = device.connector\\n listener = connector.listener\\n if listener is not None and listener.is_running:\\n _LOGGER.debug('Shutting down listener for device ID \\\"%s\\\"' % device_id)\\n listener.stop()\\n\\n if connector.is_connected:\\n _LOGGER.debug('Shutting down connector for device ID \\\"%s\\\"' % device_id)\\n await connector.close_connection()\",\n \"def on_event_finished(self, event):\",\n \"def SendEndScanSignal(self):\\n pass\",\n \"def handle_update(self, call):\\n self.fire_event(EVENT_UPDATE)\",\n \"def end(self):\\n self.shutdown = True\",\n \"def end(self) -> None:\",\n \"def _async_update_callback(self):\\n self._async_update_device_data()\\n self.async_write_ha_state()\",\n \"def update_port_postcommit(self, mech_context):\\n LOG.debug(\\\"update_port_postcommit: called\\\")\",\n \"def close_event(device_int):\\n command = \\\"\\\"\\\"\\n SELECT * FROM `FYP Data`.interface_events\\n where interface_id = {} and state ='active' and issue = 'out utilization'\\\"\\\"\\\".format(device_int[\\\"db_id\\\"])\\n event = get_data(command)\\n command = \\\"\\\"\\\"\\n UPDATE `FYP Data`.`interface_events`\\n SET `state` = 'resolved'\\n WHERE event_id = {} ;\\\"\\\"\\\".format(event[0][0])\\n set_data_mysql(command)\\n update_interface_and_history(device_int)\",\n \"def onEpochEnd(self, agent):\\n\\n pass\",\n \"def _on_stop(self, _):\\n self.unit.status = MaintenanceStatus(\\\"Pod is terminating.\\\")\\n logger.info(\\\"Pod is terminating.\\\")\",\n \"def process_IN_CLOSE_WRITE(self, event):\",\n \"def processEnded(self, reason):\\n return self.runAppCallbacks(reason)\",\n \"def end(self, won, reason):\\n pass\\n # replace with your end logic\",\n \"async def after_update_callback(self, _: XknxConnectionState) -> None:\\n self.async_write_ha_state()\",\n \"def end(self):\\n if self.flowComponent:\\n self.flowComponent.end()\\n pass\",\n \"def end_message(self):\\r\\n if self.cur_message is not None:\\r\\n self.cur_message.end_time = datetime.now()\\r\\n self.wait_message()\",\n \"def TTAPI_ShutdownCompleted(self, sender, e):\\r\\n # Shutdown the Dispatcher\\r\\n if self._m_disp != None:\\r\\n self._m_disp.BeginInvokeShutdown()\\r\\n self._m_disp = None\",\n \"def episode_end(self):\\n return self.game.is_episode_finished()\",\n \"def OnQuit(self, e):\\n\\t\\tself.EndRun()\",\n \"def end(self) -> None:\\n self.process_event(\\n PipelineEvent(\\n PipelineEventType.RUN_END,\\n )\\n )\",\n \"def end(self):\\n self.send_all(\\\"SHUTDOWN\\\") #On indique a tout le monde qu'on ferme\\n self.socket.shutdown(socket.SHUT_RDWR)\\n self.socket.close()\",\n \"def on_epoch_end(self):\\n pass\",\n \"def set_update_received_callback(self, callback):\\n self.__update_received = callback\",\n \"def end(self):\\n self._log.debug('doing ..')\\n super().end()\\n\\n self._servo.end()\\n self._mtr.end()\\n self._log.debug('done')\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def set_end(self, on = True):\\n self._end = on\",\n \"def tellIfEnded(self):\\n self.congratulate()\",\n \"def _handle_service_end(node: int, system: System, params: Params,\\n records: Records):\\n time_now = system.time\\n server = system.get_server(node)\\n queue = system.get_queue(node)\\n\\n # Extract the packet from the server and mark it as served:\\n packet = server.pop()\\n packet.service_finished[node] = time_now\\n packet.was_served[node] = True\\n\\n # If this node is the target, then packet is delivered.\\n # Otherwise, pass it to the next node.\\n if packet.target == node:\\n packet.was_delivered = True\\n packet.delivery_time = time_now\\n elif node < params.num_stations - 1:\\n _process_packet(node + 1, packet, system, params, records)\\n else:\\n raise RuntimeError(\\\"shouldn't arrive here: packet reached network\\\"\\n \\\"end, and this is not its destination!!\\\")\\n\\n # Start serving next packet, if exists:\\n if (packet := queue.pop()) is not None:\\n server.serve(packet)\\n packet.service_started[node] = time_now\\n system.schedule(Event.SERVICE_END, params.services[node](), node)\\n\\n # Record new system size:\\n records.get_system_size(node).add(time_now, system.get_size(node))\",\n \"def END(self):\\n log.debug(\\\"All packets successfully transmitted!\\\")\",\n \"def sendEnd(self):\\n self._sendCommonMessage(self.SONY_MSG_Common_Bye, self.ThreeValueMsg.pack(a=0, b=0, c=0))\",\n \"def TTAPI_ShutdownCompleted(self, sender, e):\\r\\n # Shutdown the Dispatcher\\r\\n if self.m_disp != None:\\r\\n self.m_disp.BeginInvokeShutdown()\\r\\n self.m_disp = None\",\n \"def episode_finish(self, reason: str) -> None:\\n pass\",\n \"def _termination_handler(self, signum, frame):\\n print '[i] Shutting down...'\\n self.sensor.close()\\n sys.exit(1)\",\n \"def do_end(end):\\n if end:\\n do_action(end)\",\n \"def on_endpoint_final(self, data):\\n logger.debug('Final data from endpoint')\",\n \"def handleResponseEnd(self):\\r\\n try:\\r\\n if not self._finished:\\r\\n reactor.callInThread(\\r\\n self.resource.cacheContent,\\r\\n self.father,\\r\\n self._response,\\r\\n self.buffer\\r\\n )\\r\\n proxy.ProxyClient.handleResponseEnd(self)\\r\\n except RuntimeError:\\r\\n # because we don't care if the user hits\\r\\n # refresh before the request is done\\r\\n pass\",\n \"def update_port_postcommit(self, port_context):\\n # TODO(ijw): optimisation: the update port may leave the\\n # binding state the same as before if someone updated\\n # something other than the binding on the port, but this\\n # way we always send it out and it's the far end's job to\\n # ignore it. Doing less work is nevertheless good, so we\\n # should in future avoid the send.\\n\\n if port_context.binding_levels is not None:\\n current_bind = port_context.binding_levels[-1]\\n if port_context.original_binding_levels is None:\\n prev_bind = None\\n else:\\n prev_bind = port_context.original_binding_levels[-1]\\n\\n if (current_bind is not None and\\n current_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\\n self.communicator.kick()\\n elif (prev_bind is not None and\\n prev_bind.get(api.BOUND_DRIVER) == self.MECH_NAME):\\n self.communicator.kick()\",\n \"def process_employee_exit(self):\\n if self.is_employee_serving():\\n self._end_date.append(datetime.now().isoformat())\\n\\n print(f\\\"Successfully processed exit for employee {self.name} on\\\" \\\\\\n f\\\"{self._end_date[-1]}\\\\nWe wish {self.name} for future endeavours\\\")\\n return\\n raise RejoiningException(\\\"Employee not in service. Cannot process exit.\\\")\",\n \"def update_callback(self, output_id=0):\\n if output_id == 0 | output_id == self._output_id:\\n _LOGGER.info(\\\"SmartVideoHub sent a status update for output %i\\\", output_id)\\n self.update()\\n self.schedule_update_ha_state(False)\",\n \"def end():\\n logging.info(\\\"Execution Ended\\\")\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def on_epoch_end(self, epoch, logs=None):\",\n \"def sendSslEnd(self, req):\\n self._sendTcpMessage(self.SONY_MSG_Tcp_ProxyEnd, req, self.ThreeValueMsg.pack(a=1, b=1, c=0))\",\n \"def done_action(self) -> None:\\n self.end = datetime.now()\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def on_epoch_end(self):\\n for callback in self.callbacks:\\n callback.on_epoch_end(self, self.get_model())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6465052","0.6250917","0.6206949","0.6200631","0.6087534","0.60749346","0.60135967","0.59975713","0.5883709","0.58819026","0.58666104","0.5856382","0.58052033","0.57543814","0.5744977","0.5744686","0.57377493","0.57021797","0.56638414","0.5663316","0.56605446","0.5658989","0.56571656","0.56438154","0.56428653","0.56344104","0.56276053","0.5601103","0.5593664","0.55808663","0.5578874","0.5574212","0.55652535","0.55651987","0.55476904","0.55286884","0.5522512","0.5521659","0.55143064","0.55113584","0.5506068","0.5506068","0.5504014","0.550058","0.54957753","0.5484544","0.5484157","0.5484157","0.5484157","0.54808253","0.5475552","0.54653704","0.545935","0.54306006","0.5425826","0.54233503","0.54183865","0.5406171","0.540172","0.5398689","0.53953505","0.5389153","0.5373375","0.53640485","0.5359283","0.53556055","0.5354924","0.5348103","0.5346287","0.5345793","0.5345019","0.5344829","0.53448206","0.5337586","0.5335029","0.53347266","0.5331926","0.53317785","0.5330911","0.53308487","0.5328607","0.532659","0.53203964","0.5318623","0.5314124","0.531284","0.52952284","0.5291345","0.52894104","0.5278549","0.5275904","0.52720356","0.526427","0.52616745","0.5255482","0.5255482","0.525002","0.5247865","0.52422553","0.52386755"],"string":"[\n \"0.6465052\",\n \"0.6250917\",\n \"0.6206949\",\n \"0.6200631\",\n \"0.6087534\",\n \"0.60749346\",\n \"0.60135967\",\n \"0.59975713\",\n \"0.5883709\",\n \"0.58819026\",\n \"0.58666104\",\n \"0.5856382\",\n \"0.58052033\",\n \"0.57543814\",\n \"0.5744977\",\n \"0.5744686\",\n \"0.57377493\",\n \"0.57021797\",\n \"0.56638414\",\n \"0.5663316\",\n \"0.56605446\",\n \"0.5658989\",\n \"0.56571656\",\n \"0.56438154\",\n \"0.56428653\",\n \"0.56344104\",\n \"0.56276053\",\n \"0.5601103\",\n \"0.5593664\",\n \"0.55808663\",\n \"0.5578874\",\n \"0.5574212\",\n \"0.55652535\",\n \"0.55651987\",\n \"0.55476904\",\n \"0.55286884\",\n \"0.5522512\",\n \"0.5521659\",\n \"0.55143064\",\n \"0.55113584\",\n \"0.5506068\",\n \"0.5506068\",\n \"0.5504014\",\n \"0.550058\",\n \"0.54957753\",\n \"0.5484544\",\n \"0.5484157\",\n \"0.5484157\",\n \"0.5484157\",\n \"0.54808253\",\n \"0.5475552\",\n \"0.54653704\",\n \"0.545935\",\n \"0.54306006\",\n \"0.5425826\",\n \"0.54233503\",\n \"0.54183865\",\n \"0.5406171\",\n \"0.540172\",\n \"0.5398689\",\n \"0.53953505\",\n \"0.5389153\",\n \"0.5373375\",\n \"0.53640485\",\n \"0.5359283\",\n \"0.53556055\",\n \"0.5354924\",\n \"0.5348103\",\n \"0.5346287\",\n \"0.5345793\",\n \"0.5345019\",\n \"0.5344829\",\n \"0.53448206\",\n \"0.5337586\",\n \"0.5335029\",\n \"0.53347266\",\n \"0.5331926\",\n \"0.53317785\",\n \"0.5330911\",\n \"0.53308487\",\n \"0.5328607\",\n \"0.532659\",\n \"0.53203964\",\n \"0.5318623\",\n \"0.5314124\",\n \"0.531284\",\n \"0.52952284\",\n \"0.5291345\",\n \"0.52894104\",\n \"0.5278549\",\n \"0.5275904\",\n \"0.52720356\",\n \"0.526427\",\n \"0.52616745\",\n \"0.5255482\",\n \"0.5255482\",\n \"0.525002\",\n \"0.5247865\",\n \"0.52422553\",\n \"0.52386755\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65497047"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79796,"cells":{"query":{"kind":"string","value":"Handle the port.delete.end notification event."},"document":{"kind":"string","value":"def port_delete_end(self, payload):\n port = self.cache.get_port_by_id(payload['port_id'])\n if port:\n network = self.cache.get_network_by_id(port.network_id)\n self.cache.remove_port(port)\n self.call_driver('reload_allocations', network)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def process_IN_DELETE(self, event):","def delete_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.delete_port(port)\n except:\n pass","def delete(self):\n self.log.info('Deleting')\n self._state = PonPort.State.DELETING\n self._cancel_deferred()","def on_disconnect(self, raw_msg, server, port, **kwargs):","def processEnded(self, reason):\n if self._killTimeout is not None:\n self._killTimeout.cancel()\n self.protocol.connectionLost(reason)\n self.service.removeBridge(self)","def delete_callback(self):\n pass","def port_delete(switch, port):\n client.port.delete(switch, port)","def _notify_delete(self, cuds_object):","def on_delete(self, payload):\n pass","def post_destroy(self) -> Any:\n raise NotImplementedError","def delete_event(self,widget=None):\n self.on_device_dialog_cancel_clicked()\n return True","def on_deleted(self, event):\n\n # the absolute path of the event file/folder\n abs_path = event.src_path\n # replace the root path with a '.' to build a relative path to be sent to server\n relative_event_path = abs_path.replace(self.root_path, \".\")\n\n # retrieve event type and the flag for directory/folder\n event_type = event.event_type\n is_directory = event.is_directory\n\n # only propagate changes if there is a connection with the server\n if self.protocol.connected:\n self.protocol.send_event(event_type, is_directory, relative_event_path)\n else:\n logging.info(\"Connection with server has not been established, 'delete' changes will not be propagated.\")","def endMessage(self):","def process_IN_DELETE_SELF(self, event):","def onMessageEnd(self):","def on_del(self, callback):\n self._del_callback = callback if callable(callback) else _void","def on_del(self, callback):\n self._del_callback = callback if callable(callback) else _void","def device_disconnect(self):\n pass","def subports_deleted(self, context, subports):\n self._resource_rpc.push(context, subports, events.DELETED)","def listen_del(self, f):\n self._coms.register_leaving_callback(f)","def close(self):\n self._udp_handler.send('exit'.encode(encoding='utf-8'))","def delete_event(self, widget, event, data=None): \r\n\t\t# XXX: Are you sure you want to quit?\r\n\t\tpreferences.save()\r\n\t\treturn False","def on_disconnect(mqttc, obj, rc):\n if rc == 0:\n logger.debug(\"MQTT DISCONNECTED: rc: \" + str(rc))\n logger.debug(\"Disconnected Successfully from MQTT Broker\")","def port_update_end(self, payload):\n port = DictModel(payload['port'])\n network = self.cache.get_network_by_id(port.network_id)\n if network:\n self.cache.put_port(port)\n self.call_driver('reload_allocations', network)","def ev_quit(self, event: Quit) -> None:","def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()","def delete_port(self, port):\r\n return self.delete(self.port_path % (port))","def on_delete(key):\n pass","def link_delete_callback(self):\n pass","def delete(clients, context):\n port_id = context['port_id']\n logger.info(\"Taking action port.delete {}\".format(port_id))\n neutron = clients.get_neutron()\n neutron.delete_port(port_id)","def delete_port_postcommit(self, mech_context):\n\n LOG.debug(\"delete_port_postcommit: called\")\n port = mech_context.current\n port_id = port['id']\n network_id = port['network_id']\n tenant_id = port['tenant_id']\n host_id = mech_context._binding.host\n context = mech_context._plugin_context\n\n try:\n network = seamicro_db.get_network(context, network_id)\n except Exception:\n LOG.exception(\n _LE(\"SeaMicro Mechanism: failed to get network %s from db\"),\n network_id)\n raise Exception(\n _(\"SeaMicro Mechanism: failed to get network %s from db\"),\n network_id)\n\n vlan_id = network['vlan']\n\n switch_ip, server_id, nics = _get_switch_info(self._switch, host_id)\n if switch_ip is not None and server_id is not None and nics is not None:\n try:\n interfaces = self.client[switch_ip].interfaces.list()\n for interface in interfaces:\n interface.remove_tagged_vlan(vlan_id)\n\n server = self.client[switch_ip].servers.get(server_id)\n if nics:\n server.unset_tagged_vlan(vlan_id, nics=nics)\n else:\n server.unset_tagged_vlan(vlan_id)\n except seamicro_client_exception.ClientException as ex:\n LOG.exception(\n _LE(\"SeaMicro driver: failed to delete port\"\n \" with the following error: %(error)s\"),\n {'error': ex.message})\n raise Exception(\n _(\"SeaMicro Mechanism: delete_port_postcommit failed\"))\n\n LOG.info(\n _LI(\"delete port (postcommit): port_id=%(port_id)s\"\n \" network_id=%(network_id)s tenant_id=%(tenant_id)s\"\n \" switch_ip=%(switch_ip)s server_id=%(server_id)s\"),\n {'port_id': port_id,\n 'network_id': network_id, 'tenant_id': tenant_id,\n 'switch_ip': switch_ip, 'server_id': server_id})","def on_terminate(self):\n pass","def after_delete(self, obj, st):\n pass","def on_endpoint_delete(self, response_ignored, name):\n try:\n hostname, orchestrator, workload_id, endpoint_id = \\\n split_endpoint_name(name)\n except ValueError:\n # For some reason this endpoint's name does not have the expected\n # form. Ignore it.\n LOG.warning(\"Unexpected form for endpoint name: %s\", name)\n return\n\n # Remove endpoint ID from our cache. Note, it might not be\n # there because we haven't checked whether the endpoint just\n # deleted is a local one; hence 'discard' instead of 'remove'.\n self.local_endpoint_ids.discard(endpoint_id)\n\n # Find the corresponding port in the DHCP agent's cache.\n port = self.agent.cache.get_port_by_id(endpoint_id)\n if port:\n LOG.debug(\"deleted port: %s\", port)\n self.mtu_watcher.unwatch_port(endpoint_id, port.device_id)\n self.agent.cache.remove_port(port)\n self._update_dnsmasq(port.network_id)","def m_ts_OrderDeleted(self, sender, e):\r\n print(\"Order was deleted.\")","def on_disconnect( client, userdata, rc ):\n logging.info( \"Disconnected from Broker. Returned code: %s\\n\" %rc )\n client.connected_flag = False\n client.disconnect_flag = True","def on_disconnect(self):\n # self.node_worker_ref.socketIO.disconnect()\n self.log.info('Disconnected from the server')","async def on_disconnect(self) -> None:","def on_disconnect(self):\n print('Client disconnected!')","async def callback_homeassistant_stop(self, event: \"Event\") -> NoReturn:\n _LOGGER.debug(\"Hekr system is shutting down\")\n for device_id, device in self.devices.items():\n connector = device.connector\n listener = connector.listener\n if listener is not None and listener.is_running:\n _LOGGER.debug('Shutting down listener for device ID \"%s\"' % device_id)\n listener.stop()\n\n if connector.is_connected:\n _LOGGER.debug('Shutting down connector for device ID \"%s\"' % device_id)\n await connector.close_connection()","def delete(self, downstream = None):\n # emit delete message\n self.deleteSignal.emit(self)","def on_eod(self, oid, body):\n\t\tself._pbar.update(self._pbar.total - self._pbar.n)\n\t\tself._pbar.close()\n\n\t\tself.basic_publish('dereg-feed', sender=self.id)\n\t\tself.basic_publish('dereg-exe', sender=self.id)\n\n\t\tself._stop()","def delete_event(self, widget, event, Data=None):\n\t\tself.todolist.save()\n\t\tgtk.main_quit()\n\t\treturn False","def on_disconnect(self, mqtt_client, userdata, rc):\n logging.debug('DEBUG - Disconnected from broker')","def stopcomm(self,handle):\r\n # Convert handle back to tuple, unpack it\r\n (ip, port) = deserialize(handle)\r\n \r\n # Deletes the callback function on the specified port\r\n if ip in self.callbackFunction and port in self.callbackFunction[ip]:\r\n del self.callbackFunction[ip][port]","def delete_device(self):\n # PROTECTED REGION ID(AsyncTabata.delete_device) ENABLED START #\n # PROTECTED REGION END # // AsyncTabata.delete_device","def callback_disconnect():\n # if Networking.get_instance().is_host:\n logger.warning(\"It seems that client is not connected...\")\n Networking.get_instance().disconnect()\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))","def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])","def flow_removed_handler(self, ev):\n msg = ev.msg\n datapath = msg.datapath\n ofp = datapath.ofproto\n if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n reason = 'IDLE TIMEOUT'\n elif msg.reason == ofp.OFPRR_HARD_TIMEOUT:\n reason = 'HARD TIMEOUT'\n elif msg.reason == ofp.OFPRR_DELETE:\n reason = 'DELETE'\n elif msg.reason == ofp.OFPRR_GROUP_DELETE:\n reason = 'GROUP DELETE'\n else:\n reason = 'unknown'\n self.logger.info('Flow removed msg '\n 'cookie=%d priority=%d reason=%s table_id=%d '\n 'duration_sec=%d '\n 'idle_timeout=%d hard_timeout=%d '\n 'packets=%d bytes=%d match=%s',\n msg.cookie, msg.priority, reason, msg.table_id,\n msg.duration_sec,\n msg.idle_timeout, msg.hard_timeout,\n msg.packet_count, msg.byte_count, msg.match)\n # Is it a MAC learning suppression FE idle timeout?\n if msg.table_id == self.ft_iim and \\\n msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n switch = self.switches[datapath.id]\n #*** Extract the MAC from the match:\n mac = msg.match['eth_src']\n in_port = msg.match['in_port']\n #*** TBD, deal with context:\n context = self.context_default\n #*** Call method to delete FEs:\n switch.mactable.delete(mac, in_port, context)","def childDidExit(self, processProtocol, reason):\n for child in self.children:\n if child.protocol == processProtocol:\n self.log.info(\n u\"Child process ({child.transport.pid}) exited: \"\n u\"{reason}\",\n child=child, reason=reason,\n )\n self.children.remove(child)\n break\n else:\n self.log.error(\n u\"No child for for process protocol\",\n processProtocol=processProtocol\n )\n\n try:\n self.dispatcher.removeSocket(processProtocol.inheritedSocket)\n except ValueError:\n self.log.error(\n u\"No socket found for process protocol\",\n processProtocol=processProtocol\n )","def onShutdown(self, connection:MQTTConnection) -> None:","def stop_cb(evt):\n print('CLOSING on {}'.format(evt))\n nonlocal done\n done = True","def notify_end(self, status, objective):\n pass # pragma: no cover","def on_disconnect(client, userdata, rc):\n if rc != 0:\n print(\"Error: Mqtt server disconnection.\")","def handle_link_down (self, port):\n for dest in self.hosts.keys():\n currPort = self.hosts[dest][0]\n if currPort == port:\n del self.hosts[dest]\n \n deleteDests = set()\n for dest in self.routesToDest:\n currPort = self.routesToDest[dest][0]\n \n if currPort == port:\n\n if dest in self.hosts:\n self.routesToDest[dest] = self.hosts[dest]\n packet = basics.RoutePacket(dest, self.routesToDest[dest][1])\n self.send(packet, self.routesToDest[dest][0], True)\n else:\n self.sendPoison(dest)\n deleteDests.add(dest)\n\n\n for dest in deleteDests:\n del self.routesToDest[dest]\n\n del self.neighbours[port]","def processEnded(self, reason):\n self._processEnded = True\n if self.onProcessEnd:\n d, self.onProcessEnd = self.onProcessEnd, None\n d.callback(None)","async def delete(self, delete: TPayload) -> None:","def on_session_destroyed(session_context):\n if data.AUTO_SHUTDOWN:\n import sys\n\n sys.exit(\n \"\\033[1;31mThe session has ended - tab closed or timeout. \\n\\n --- Terminating the Forest progam and relinquishing control of port. ---\\033[1;00m\"\n )","def on_quit(self, raw_msg, source, **kwargs):","def process_IN_CLOSE_WRITE(self, event):","def event_delete(req):\n event_id = req.match_dict['event_id']\n try:\n db_conn.event_delete(event_id)\n json = {'deleted': True}\n except Exception as e:\n json = {'errors': [str(e)]}\n return req.Response(json=json)","def delete_handler(event, context):\n delete_endpoint_config(event)","def delete(self):\n\t\tself.eventStatus = 0\n\t\tself.save()\n\n\t\t# DEBUG\n\t\tcontext = {\n\t\t\t'call_stack': ''.join(traceback.format_stack()),\n\t\t\t'event': self,\n\t\t\t'server_addr': settings.SERVER_ADDRESS,\n\t\t}\n\t\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\n\t\tmail_admins(_('Event Deletion Attempt!'), body)","def __del__(self):\n self.logger.info(\"Finishing\")\n self.__disconnect()","def processEnded(self, reason):\n if not self.empty:\n self.output.dataReceived('\\n')\n self.service.processEnded(self.name)","def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\n if rc == mqtt.MQTT_ERR_SUCCESS:\n logging.info(\n f\"Successfully disconnected from the broker {self.config.broker}\"\n )\n sys.exit(0)\n else:\n logging.critical(\n f\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\"\n )\n sys.exit(1)","def connectionLost(self, reason):\r\n _Protocol.remote_destroy(self)","def on_quit (self):","def on_connection_closed(self):","def on_deleted(self, event):\n\t\tfileInfo = event.src_path.split('/')\n\n\t\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]","def disconnected(self):\n\n self.fire(connect(self.host, self.port))","def stop_cb(evt):\n print('CLOSING on {}'.format(evt))\n nonlocal done\n done = True","def handle_close(self):\n print(self.addr, \"bye\")\n self.close()","def _close(self):\n \n # Close device\n logger.debug(\"%s: UDP port closing started...\" % \\\n self.__class__.__name__)\n self._udp_socket.close()\n self._socket = None\n logger.debug(\"%s: ...UDP port closing complete.\" % \\\n self.__class__.__name__)","def end(self):\n self.send_all(\"SHUTDOWN\") #On indique a tout le monde qu'on ferme\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()","async def on_cancel(self, payload):\n\n await self._delete_message(0)\n self.stop()","def handler(self):\n\t\tself.exitClient()","def stop_cb(evt: speechsdk.SessionEventArgs):\n print('CLOSING on {}'.format(evt))\n nonlocal done\n done = True","def disconnect(self):","def _delete_subports_handler(self, *args, **kwargs):\n payload = kwargs['payload']\n trunk = payload.current_trunk\n subports = payload.subports\n self._delete_subports(trunk, subports)","def feed_entry_post_delete(sender, instance, **kwargs):\n del kwargs\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\n try:\n if instance.command_group and instance.command_group.id is not None:\n instance.command_group.delete()\n except CommandGroupEntry.DoesNotExist:\n pass\n\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)","def ticket_deleted(self, ticket):\n if 'ticket' not in self.sources:\n return\n gnp = GrowlNotificationPacket(notification='ticket',\n title='Ticket #%d deleted' % ticket.id,\n description=self._ticket_repr(ticket))\n gs = GrowlSender(self.env)\n gs.notify(self._get_hosts('ticket'), gnp)","def _on_disconnect(self, *args, **kwargs):\n logger.info('Client disconnected %r', self.address)\n self.server.client_disconnected(self)\n # self.unregister()","def handle_delf_conf(self, job):\n print(\"User deleted a file.\")\n # Delete the file from all servers\n self.put_job_in_all_queues(job)\n\n list_job_results = self.get_internal_results_from_all_servers()\n\n if len(list_job_results) == 0:\n # We got no responses back, there are probably no servers active\n self.put_external_result(self.generate_failure_job(\"Unsuccessful, no servers responded\"))\n return\n\n # Check all the servers had success\n for result in list_job_results:\n if result.result[\"outcome\"] != \"success\":\n self.put_external_result(\n self.generate_failure_job(\"Unsuccessful, one of the servers did not have success\"))\n return\n print(\"All servers deleted file '{}'.\".format(job.data[\"file_name\"]))\n\n # Tell the client we successfully uploaded to all servers\n response_result = copy.deepcopy(list_job_results[0])\n response_result.processed_by = None\n self.put_external_result(response_result)","def closeEvent(self, event):\n self.device.disconnect()\n event.accept()","def _unplug_interface(self, context, tenant_id, net_id, port_id):\n LOG.debug(_(\"QuantumRestProxyV2: _unplug_interface() called\"))\n\n # delete from network ctrl. Remote error on delete is ignored\n try:\n resource = ATTACHMENT_PATH % (tenant_id, net_id, port_id)\n ret = self.servers.delete(resource)\n if not self.servers.action_success(ret):\n raise RemoteRestError(ret[2])\n except RemoteRestError as e:\n LOG.error(_(\"QuantumRestProxyV2: Unable to update remote port: \"\n \"%s\"), e.message)","def on_disconnect():\n print(\"User disconnected!\")","def end(self):\n ...","def delete(self, mac, in_port, context):\n nmeta = self._nmeta\n dpid = self.dpid\n\n #*** Check if MAC known in database for this switch/context:\n db_result = nmeta.dbidmac.delete_many({'dpid': dpid, 'mac': mac,\n 'context': context})\n self.logger.debug(\"Deleted MAC/port from DB: dpid=%s mac=%s port=%s \"\n \"context=%s entries=%s\", dpid, mac, in_port,\n context, db_result.deleted_count)\n self.logger.debug(\"Deleting IIM FE from switch: dpid=%s mac=%s port=%s\"\n \" context=%s\", dpid, mac, in_port, context)\n #*** Delete IIM FE from switch:\n ofproto = self.datapath.ofproto\n parser = self.datapath.ofproto_parser\n match = parser.OFPMatch(in_port=in_port, eth_src=mac)\n actions = []\n inst = [parser.OFPInstructionActions(\n ofproto.OFPIT_APPLY_ACTIONS, actions),\n parser.OFPInstructionGotoTable(self.ft_iim + 1)]\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\n cookie_mask=0,\n table_id=self.ft_iim,\n command=ofproto.OFPFC_DELETE,\n idle_timeout=0, hard_timeout=0,\n priority=1,\n buffer_id=ofproto.OFP_NO_BUFFER,\n out_port=ofproto.OFPP_ANY,\n out_group=ofproto.OFPG_ANY,\n flags=ofproto.OFPFF_SEND_FLOW_REM,\n match=match,\n instructions=inst)\n self.datapath.send_msg(mod)\n #*** Delete AMF FE from switch:\n self.logger.debug(\"Deleting AMF FE from switch: dpid=%s mac=%s port=%s\"\n \" context=%s\", dpid, mac, in_port, context)\n match = parser.OFPMatch(eth_dst=mac)\n actions = [parser.OFPActionOutput(in_port)]\n inst = [parser.OFPInstructionActions(\n ofproto.OFPIT_APPLY_ACTIONS, actions)]\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\n cookie_mask=0,\n table_id=self.ft_amf,\n command=ofproto.OFPFC_DELETE,\n idle_timeout=0, hard_timeout=0,\n priority=2,\n buffer_id=ofproto.OFP_NO_BUFFER,\n out_port=in_port,\n out_group=ofproto.OFPG_ANY,\n flags=ofproto.OFPFF_SEND_FLOW_REM,\n match=match,\n instructions=inst)\n self.datapath.send_msg(mod)\n\n #*** Delete FWD FE from switch:\n self.logger.debug(\"Deleting FWD FE from switch: dpid=%s mac=%s port=%s\"\n \" context=%s\", dpid, mac, in_port, context)\n match = parser.OFPMatch(eth_dst=mac)\n actions = [parser.OFPActionOutput(in_port)]\n inst = [parser.OFPInstructionActions(\n ofproto.OFPIT_APPLY_ACTIONS, actions)]\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\n cookie_mask=0,\n table_id=self.ft_fwd,\n command=ofproto.OFPFC_DELETE,\n idle_timeout=0, hard_timeout=0,\n priority=2,\n buffer_id=ofproto.OFP_NO_BUFFER,\n out_port=in_port,\n out_group=ofproto.OFPG_ANY,\n flags=ofproto.OFPFF_SEND_FLOW_REM,\n match=match,\n instructions=inst)\n self.datapath.send_msg(mod)","def delete(event: EventType, widget: WidgetType) -> bool:\n return event.key == _locals.K_DELETE","def _termination_handler(self, signum, frame):\n print '[i] Shutting down...'\n self.sensor.close()\n sys.exit(1)","def tcp_end(self, flow: mitmproxy.tcp.TCPFlow):","def ticket_deleted(self, ticket):","def cb_quit(event):\n sys.exit()","def __del__(self):\n if self.connection_obj:\n self.logger.info('Disconnecting from host {0}:{1}'.format(self.host, self.port))\n Disconnect(self.connection_obj)","def _close(self):\n \n # Close device\n logger.debug(\"%s: Serial port closing started...\" % \\\n self.__class__.__name__)\n self._serial_port.close()\n logger.debug(\"%s: ...serial port closing complete.\" % \\\n self.__class__.__name__)","def _disconnected(self):\n MeshInterface._disconnected(self)\n\n logging.debug(\"Closing our port\")\n if not self.stream is None:\n self.stream.close()","def exited(event=None):\n clientSock.close()\n GUI.quit()","def finished(self, reply):\n pass","async def on_terncy_svc_remove(event):\n dev_id = event.data[\"dev_id\"]\n _LOGGER.info(\"terncy svc remove %s\", dev_id)\n if not tern.is_connected():\n await tern.stop()"],"string":"[\n \"def process_IN_DELETE(self, event):\",\n \"def delete_port_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n port = self._get_port_info(context)\\n if port is not None:\\n try:\\n self.rpc_handler.delete_port(port)\\n except:\\n pass\",\n \"def delete(self):\\n self.log.info('Deleting')\\n self._state = PonPort.State.DELETING\\n self._cancel_deferred()\",\n \"def on_disconnect(self, raw_msg, server, port, **kwargs):\",\n \"def processEnded(self, reason):\\n if self._killTimeout is not None:\\n self._killTimeout.cancel()\\n self.protocol.connectionLost(reason)\\n self.service.removeBridge(self)\",\n \"def delete_callback(self):\\n pass\",\n \"def port_delete(switch, port):\\n client.port.delete(switch, port)\",\n \"def _notify_delete(self, cuds_object):\",\n \"def on_delete(self, payload):\\n pass\",\n \"def post_destroy(self) -> Any:\\n raise NotImplementedError\",\n \"def delete_event(self,widget=None):\\n self.on_device_dialog_cancel_clicked()\\n return True\",\n \"def on_deleted(self, event):\\n\\n # the absolute path of the event file/folder\\n abs_path = event.src_path\\n # replace the root path with a '.' to build a relative path to be sent to server\\n relative_event_path = abs_path.replace(self.root_path, \\\".\\\")\\n\\n # retrieve event type and the flag for directory/folder\\n event_type = event.event_type\\n is_directory = event.is_directory\\n\\n # only propagate changes if there is a connection with the server\\n if self.protocol.connected:\\n self.protocol.send_event(event_type, is_directory, relative_event_path)\\n else:\\n logging.info(\\\"Connection with server has not been established, 'delete' changes will not be propagated.\\\")\",\n \"def endMessage(self):\",\n \"def process_IN_DELETE_SELF(self, event):\",\n \"def onMessageEnd(self):\",\n \"def on_del(self, callback):\\n self._del_callback = callback if callable(callback) else _void\",\n \"def on_del(self, callback):\\n self._del_callback = callback if callable(callback) else _void\",\n \"def device_disconnect(self):\\n pass\",\n \"def subports_deleted(self, context, subports):\\n self._resource_rpc.push(context, subports, events.DELETED)\",\n \"def listen_del(self, f):\\n self._coms.register_leaving_callback(f)\",\n \"def close(self):\\n self._udp_handler.send('exit'.encode(encoding='utf-8'))\",\n \"def delete_event(self, widget, event, data=None): \\r\\n\\t\\t# XXX: Are you sure you want to quit?\\r\\n\\t\\tpreferences.save()\\r\\n\\t\\treturn False\",\n \"def on_disconnect(mqttc, obj, rc):\\n if rc == 0:\\n logger.debug(\\\"MQTT DISCONNECTED: rc: \\\" + str(rc))\\n logger.debug(\\\"Disconnected Successfully from MQTT Broker\\\")\",\n \"def port_update_end(self, payload):\\n port = DictModel(payload['port'])\\n network = self.cache.get_network_by_id(port.network_id)\\n if network:\\n self.cache.put_port(port)\\n self.call_driver('reload_allocations', network)\",\n \"def ev_quit(self, event: Quit) -> None:\",\n \"def send_finish_event(self):\\n self.status['type'] = '__end__'\\n self._send()\",\n \"def delete_port(self, port):\\r\\n return self.delete(self.port_path % (port))\",\n \"def on_delete(key):\\n pass\",\n \"def link_delete_callback(self):\\n pass\",\n \"def delete(clients, context):\\n port_id = context['port_id']\\n logger.info(\\\"Taking action port.delete {}\\\".format(port_id))\\n neutron = clients.get_neutron()\\n neutron.delete_port(port_id)\",\n \"def delete_port_postcommit(self, mech_context):\\n\\n LOG.debug(\\\"delete_port_postcommit: called\\\")\\n port = mech_context.current\\n port_id = port['id']\\n network_id = port['network_id']\\n tenant_id = port['tenant_id']\\n host_id = mech_context._binding.host\\n context = mech_context._plugin_context\\n\\n try:\\n network = seamicro_db.get_network(context, network_id)\\n except Exception:\\n LOG.exception(\\n _LE(\\\"SeaMicro Mechanism: failed to get network %s from db\\\"),\\n network_id)\\n raise Exception(\\n _(\\\"SeaMicro Mechanism: failed to get network %s from db\\\"),\\n network_id)\\n\\n vlan_id = network['vlan']\\n\\n switch_ip, server_id, nics = _get_switch_info(self._switch, host_id)\\n if switch_ip is not None and server_id is not None and nics is not None:\\n try:\\n interfaces = self.client[switch_ip].interfaces.list()\\n for interface in interfaces:\\n interface.remove_tagged_vlan(vlan_id)\\n\\n server = self.client[switch_ip].servers.get(server_id)\\n if nics:\\n server.unset_tagged_vlan(vlan_id, nics=nics)\\n else:\\n server.unset_tagged_vlan(vlan_id)\\n except seamicro_client_exception.ClientException as ex:\\n LOG.exception(\\n _LE(\\\"SeaMicro driver: failed to delete port\\\"\\n \\\" with the following error: %(error)s\\\"),\\n {'error': ex.message})\\n raise Exception(\\n _(\\\"SeaMicro Mechanism: delete_port_postcommit failed\\\"))\\n\\n LOG.info(\\n _LI(\\\"delete port (postcommit): port_id=%(port_id)s\\\"\\n \\\" network_id=%(network_id)s tenant_id=%(tenant_id)s\\\"\\n \\\" switch_ip=%(switch_ip)s server_id=%(server_id)s\\\"),\\n {'port_id': port_id,\\n 'network_id': network_id, 'tenant_id': tenant_id,\\n 'switch_ip': switch_ip, 'server_id': server_id})\",\n \"def on_terminate(self):\\n pass\",\n \"def after_delete(self, obj, st):\\n pass\",\n \"def on_endpoint_delete(self, response_ignored, name):\\n try:\\n hostname, orchestrator, workload_id, endpoint_id = \\\\\\n split_endpoint_name(name)\\n except ValueError:\\n # For some reason this endpoint's name does not have the expected\\n # form. Ignore it.\\n LOG.warning(\\\"Unexpected form for endpoint name: %s\\\", name)\\n return\\n\\n # Remove endpoint ID from our cache. Note, it might not be\\n # there because we haven't checked whether the endpoint just\\n # deleted is a local one; hence 'discard' instead of 'remove'.\\n self.local_endpoint_ids.discard(endpoint_id)\\n\\n # Find the corresponding port in the DHCP agent's cache.\\n port = self.agent.cache.get_port_by_id(endpoint_id)\\n if port:\\n LOG.debug(\\\"deleted port: %s\\\", port)\\n self.mtu_watcher.unwatch_port(endpoint_id, port.device_id)\\n self.agent.cache.remove_port(port)\\n self._update_dnsmasq(port.network_id)\",\n \"def m_ts_OrderDeleted(self, sender, e):\\r\\n print(\\\"Order was deleted.\\\")\",\n \"def on_disconnect( client, userdata, rc ):\\n logging.info( \\\"Disconnected from Broker. Returned code: %s\\\\n\\\" %rc )\\n client.connected_flag = False\\n client.disconnect_flag = True\",\n \"def on_disconnect(self):\\n # self.node_worker_ref.socketIO.disconnect()\\n self.log.info('Disconnected from the server')\",\n \"async def on_disconnect(self) -> None:\",\n \"def on_disconnect(self):\\n print('Client disconnected!')\",\n \"async def callback_homeassistant_stop(self, event: \\\"Event\\\") -> NoReturn:\\n _LOGGER.debug(\\\"Hekr system is shutting down\\\")\\n for device_id, device in self.devices.items():\\n connector = device.connector\\n listener = connector.listener\\n if listener is not None and listener.is_running:\\n _LOGGER.debug('Shutting down listener for device ID \\\"%s\\\"' % device_id)\\n listener.stop()\\n\\n if connector.is_connected:\\n _LOGGER.debug('Shutting down connector for device ID \\\"%s\\\"' % device_id)\\n await connector.close_connection()\",\n \"def delete(self, downstream = None):\\n # emit delete message\\n self.deleteSignal.emit(self)\",\n \"def on_eod(self, oid, body):\\n\\t\\tself._pbar.update(self._pbar.total - self._pbar.n)\\n\\t\\tself._pbar.close()\\n\\n\\t\\tself.basic_publish('dereg-feed', sender=self.id)\\n\\t\\tself.basic_publish('dereg-exe', sender=self.id)\\n\\n\\t\\tself._stop()\",\n \"def delete_event(self, widget, event, Data=None):\\n\\t\\tself.todolist.save()\\n\\t\\tgtk.main_quit()\\n\\t\\treturn False\",\n \"def on_disconnect(self, mqtt_client, userdata, rc):\\n logging.debug('DEBUG - Disconnected from broker')\",\n \"def stopcomm(self,handle):\\r\\n # Convert handle back to tuple, unpack it\\r\\n (ip, port) = deserialize(handle)\\r\\n \\r\\n # Deletes the callback function on the specified port\\r\\n if ip in self.callbackFunction and port in self.callbackFunction[ip]:\\r\\n del self.callbackFunction[ip][port]\",\n \"def delete_device(self):\\n # PROTECTED REGION ID(AsyncTabata.delete_device) ENABLED START #\\n # PROTECTED REGION END # // AsyncTabata.delete_device\",\n \"def callback_disconnect():\\n # if Networking.get_instance().is_host:\\n logger.warning(\\\"It seems that client is not connected...\\\")\\n Networking.get_instance().disconnect()\\n EventQueue.post(CustomEvent(ChangeSceneEnum.DISCONNECT))\",\n \"def network_delete_end(self, payload):\\n self.disable_dhcp_helper(payload['network_id'])\",\n \"def flow_removed_handler(self, ev):\\n msg = ev.msg\\n datapath = msg.datapath\\n ofp = datapath.ofproto\\n if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\\n reason = 'IDLE TIMEOUT'\\n elif msg.reason == ofp.OFPRR_HARD_TIMEOUT:\\n reason = 'HARD TIMEOUT'\\n elif msg.reason == ofp.OFPRR_DELETE:\\n reason = 'DELETE'\\n elif msg.reason == ofp.OFPRR_GROUP_DELETE:\\n reason = 'GROUP DELETE'\\n else:\\n reason = 'unknown'\\n self.logger.info('Flow removed msg '\\n 'cookie=%d priority=%d reason=%s table_id=%d '\\n 'duration_sec=%d '\\n 'idle_timeout=%d hard_timeout=%d '\\n 'packets=%d bytes=%d match=%s',\\n msg.cookie, msg.priority, reason, msg.table_id,\\n msg.duration_sec,\\n msg.idle_timeout, msg.hard_timeout,\\n msg.packet_count, msg.byte_count, msg.match)\\n # Is it a MAC learning suppression FE idle timeout?\\n if msg.table_id == self.ft_iim and \\\\\\n msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\\n switch = self.switches[datapath.id]\\n #*** Extract the MAC from the match:\\n mac = msg.match['eth_src']\\n in_port = msg.match['in_port']\\n #*** TBD, deal with context:\\n context = self.context_default\\n #*** Call method to delete FEs:\\n switch.mactable.delete(mac, in_port, context)\",\n \"def childDidExit(self, processProtocol, reason):\\n for child in self.children:\\n if child.protocol == processProtocol:\\n self.log.info(\\n u\\\"Child process ({child.transport.pid}) exited: \\\"\\n u\\\"{reason}\\\",\\n child=child, reason=reason,\\n )\\n self.children.remove(child)\\n break\\n else:\\n self.log.error(\\n u\\\"No child for for process protocol\\\",\\n processProtocol=processProtocol\\n )\\n\\n try:\\n self.dispatcher.removeSocket(processProtocol.inheritedSocket)\\n except ValueError:\\n self.log.error(\\n u\\\"No socket found for process protocol\\\",\\n processProtocol=processProtocol\\n )\",\n \"def onShutdown(self, connection:MQTTConnection) -> None:\",\n \"def stop_cb(evt):\\n print('CLOSING on {}'.format(evt))\\n nonlocal done\\n done = True\",\n \"def notify_end(self, status, objective):\\n pass # pragma: no cover\",\n \"def on_disconnect(client, userdata, rc):\\n if rc != 0:\\n print(\\\"Error: Mqtt server disconnection.\\\")\",\n \"def handle_link_down (self, port):\\n for dest in self.hosts.keys():\\n currPort = self.hosts[dest][0]\\n if currPort == port:\\n del self.hosts[dest]\\n \\n deleteDests = set()\\n for dest in self.routesToDest:\\n currPort = self.routesToDest[dest][0]\\n \\n if currPort == port:\\n\\n if dest in self.hosts:\\n self.routesToDest[dest] = self.hosts[dest]\\n packet = basics.RoutePacket(dest, self.routesToDest[dest][1])\\n self.send(packet, self.routesToDest[dest][0], True)\\n else:\\n self.sendPoison(dest)\\n deleteDests.add(dest)\\n\\n\\n for dest in deleteDests:\\n del self.routesToDest[dest]\\n\\n del self.neighbours[port]\",\n \"def processEnded(self, reason):\\n self._processEnded = True\\n if self.onProcessEnd:\\n d, self.onProcessEnd = self.onProcessEnd, None\\n d.callback(None)\",\n \"async def delete(self, delete: TPayload) -> None:\",\n \"def on_session_destroyed(session_context):\\n if data.AUTO_SHUTDOWN:\\n import sys\\n\\n sys.exit(\\n \\\"\\\\033[1;31mThe session has ended - tab closed or timeout. \\\\n\\\\n --- Terminating the Forest progam and relinquishing control of port. ---\\\\033[1;00m\\\"\\n )\",\n \"def on_quit(self, raw_msg, source, **kwargs):\",\n \"def process_IN_CLOSE_WRITE(self, event):\",\n \"def event_delete(req):\\n event_id = req.match_dict['event_id']\\n try:\\n db_conn.event_delete(event_id)\\n json = {'deleted': True}\\n except Exception as e:\\n json = {'errors': [str(e)]}\\n return req.Response(json=json)\",\n \"def delete_handler(event, context):\\n delete_endpoint_config(event)\",\n \"def delete(self):\\n\\t\\tself.eventStatus = 0\\n\\t\\tself.save()\\n\\n\\t\\t# DEBUG\\n\\t\\tcontext = {\\n\\t\\t\\t'call_stack': ''.join(traceback.format_stack()),\\n\\t\\t\\t'event': self,\\n\\t\\t\\t'server_addr': settings.SERVER_ADDRESS,\\n\\t\\t}\\n\\t\\tbody = render_to_string('MHLCallGroups/Scheduler/email_delete_event.txt', context)\\n\\t\\tmail_admins(_('Event Deletion Attempt!'), body)\",\n \"def __del__(self):\\n self.logger.info(\\\"Finishing\\\")\\n self.__disconnect()\",\n \"def processEnded(self, reason):\\n if not self.empty:\\n self.output.dataReceived('\\\\n')\\n self.service.processEnded(self.name)\",\n \"def on_disconnect(self, client: mqtt.Client, userdata: Any, rc: int) -> None:\\n if rc == mqtt.MQTT_ERR_SUCCESS:\\n logging.info(\\n f\\\"Successfully disconnected from the broker {self.config.broker}\\\"\\n )\\n sys.exit(0)\\n else:\\n logging.critical(\\n f\\\"Unexpected disconnection from the broker {self.config.broker}: error code={rc}\\\"\\n )\\n sys.exit(1)\",\n \"def connectionLost(self, reason):\\r\\n _Protocol.remote_destroy(self)\",\n \"def on_quit (self):\",\n \"def on_connection_closed(self):\",\n \"def on_deleted(self, event):\\n\\t\\tfileInfo = event.src_path.split('/')\\n\\n\\t\\tprint fileInfo[2] + ' deleted from ' + fileInfo[1]\",\n \"def disconnected(self):\\n\\n self.fire(connect(self.host, self.port))\",\n \"def stop_cb(evt):\\n print('CLOSING on {}'.format(evt))\\n nonlocal done\\n done = True\",\n \"def handle_close(self):\\n print(self.addr, \\\"bye\\\")\\n self.close()\",\n \"def _close(self):\\n \\n # Close device\\n logger.debug(\\\"%s: UDP port closing started...\\\" % \\\\\\n self.__class__.__name__)\\n self._udp_socket.close()\\n self._socket = None\\n logger.debug(\\\"%s: ...UDP port closing complete.\\\" % \\\\\\n self.__class__.__name__)\",\n \"def end(self):\\n self.send_all(\\\"SHUTDOWN\\\") #On indique a tout le monde qu'on ferme\\n self.socket.shutdown(socket.SHUT_RDWR)\\n self.socket.close()\",\n \"async def on_cancel(self, payload):\\n\\n await self._delete_message(0)\\n self.stop()\",\n \"def handler(self):\\n\\t\\tself.exitClient()\",\n \"def stop_cb(evt: speechsdk.SessionEventArgs):\\n print('CLOSING on {}'.format(evt))\\n nonlocal done\\n done = True\",\n \"def disconnect(self):\",\n \"def _delete_subports_handler(self, *args, **kwargs):\\n payload = kwargs['payload']\\n trunk = payload.current_trunk\\n subports = payload.subports\\n self._delete_subports(trunk, subports)\",\n \"def feed_entry_post_delete(sender, instance, **kwargs):\\n del kwargs\\n if isinstance(instance, FeedEntry) and (sender == FeedEntry):\\n signals.post_delete.disconnect(feed_entry_post_delete, sender=FeedEntry)\\n try:\\n if instance.command_group and instance.command_group.id is not None:\\n instance.command_group.delete()\\n except CommandGroupEntry.DoesNotExist:\\n pass\\n\\n signals.post_delete.connect(feed_entry_post_delete, sender=FeedEntry)\",\n \"def ticket_deleted(self, ticket):\\n if 'ticket' not in self.sources:\\n return\\n gnp = GrowlNotificationPacket(notification='ticket',\\n title='Ticket #%d deleted' % ticket.id,\\n description=self._ticket_repr(ticket))\\n gs = GrowlSender(self.env)\\n gs.notify(self._get_hosts('ticket'), gnp)\",\n \"def _on_disconnect(self, *args, **kwargs):\\n logger.info('Client disconnected %r', self.address)\\n self.server.client_disconnected(self)\\n # self.unregister()\",\n \"def handle_delf_conf(self, job):\\n print(\\\"User deleted a file.\\\")\\n # Delete the file from all servers\\n self.put_job_in_all_queues(job)\\n\\n list_job_results = self.get_internal_results_from_all_servers()\\n\\n if len(list_job_results) == 0:\\n # We got no responses back, there are probably no servers active\\n self.put_external_result(self.generate_failure_job(\\\"Unsuccessful, no servers responded\\\"))\\n return\\n\\n # Check all the servers had success\\n for result in list_job_results:\\n if result.result[\\\"outcome\\\"] != \\\"success\\\":\\n self.put_external_result(\\n self.generate_failure_job(\\\"Unsuccessful, one of the servers did not have success\\\"))\\n return\\n print(\\\"All servers deleted file '{}'.\\\".format(job.data[\\\"file_name\\\"]))\\n\\n # Tell the client we successfully uploaded to all servers\\n response_result = copy.deepcopy(list_job_results[0])\\n response_result.processed_by = None\\n self.put_external_result(response_result)\",\n \"def closeEvent(self, event):\\n self.device.disconnect()\\n event.accept()\",\n \"def _unplug_interface(self, context, tenant_id, net_id, port_id):\\n LOG.debug(_(\\\"QuantumRestProxyV2: _unplug_interface() called\\\"))\\n\\n # delete from network ctrl. Remote error on delete is ignored\\n try:\\n resource = ATTACHMENT_PATH % (tenant_id, net_id, port_id)\\n ret = self.servers.delete(resource)\\n if not self.servers.action_success(ret):\\n raise RemoteRestError(ret[2])\\n except RemoteRestError as e:\\n LOG.error(_(\\\"QuantumRestProxyV2: Unable to update remote port: \\\"\\n \\\"%s\\\"), e.message)\",\n \"def on_disconnect():\\n print(\\\"User disconnected!\\\")\",\n \"def end(self):\\n ...\",\n \"def delete(self, mac, in_port, context):\\n nmeta = self._nmeta\\n dpid = self.dpid\\n\\n #*** Check if MAC known in database for this switch/context:\\n db_result = nmeta.dbidmac.delete_many({'dpid': dpid, 'mac': mac,\\n 'context': context})\\n self.logger.debug(\\\"Deleted MAC/port from DB: dpid=%s mac=%s port=%s \\\"\\n \\\"context=%s entries=%s\\\", dpid, mac, in_port,\\n context, db_result.deleted_count)\\n self.logger.debug(\\\"Deleting IIM FE from switch: dpid=%s mac=%s port=%s\\\"\\n \\\" context=%s\\\", dpid, mac, in_port, context)\\n #*** Delete IIM FE from switch:\\n ofproto = self.datapath.ofproto\\n parser = self.datapath.ofproto_parser\\n match = parser.OFPMatch(in_port=in_port, eth_src=mac)\\n actions = []\\n inst = [parser.OFPInstructionActions(\\n ofproto.OFPIT_APPLY_ACTIONS, actions),\\n parser.OFPInstructionGotoTable(self.ft_iim + 1)]\\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\\n cookie_mask=0,\\n table_id=self.ft_iim,\\n command=ofproto.OFPFC_DELETE,\\n idle_timeout=0, hard_timeout=0,\\n priority=1,\\n buffer_id=ofproto.OFP_NO_BUFFER,\\n out_port=ofproto.OFPP_ANY,\\n out_group=ofproto.OFPG_ANY,\\n flags=ofproto.OFPFF_SEND_FLOW_REM,\\n match=match,\\n instructions=inst)\\n self.datapath.send_msg(mod)\\n #*** Delete AMF FE from switch:\\n self.logger.debug(\\\"Deleting AMF FE from switch: dpid=%s mac=%s port=%s\\\"\\n \\\" context=%s\\\", dpid, mac, in_port, context)\\n match = parser.OFPMatch(eth_dst=mac)\\n actions = [parser.OFPActionOutput(in_port)]\\n inst = [parser.OFPInstructionActions(\\n ofproto.OFPIT_APPLY_ACTIONS, actions)]\\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\\n cookie_mask=0,\\n table_id=self.ft_amf,\\n command=ofproto.OFPFC_DELETE,\\n idle_timeout=0, hard_timeout=0,\\n priority=2,\\n buffer_id=ofproto.OFP_NO_BUFFER,\\n out_port=in_port,\\n out_group=ofproto.OFPG_ANY,\\n flags=ofproto.OFPFF_SEND_FLOW_REM,\\n match=match,\\n instructions=inst)\\n self.datapath.send_msg(mod)\\n\\n #*** Delete FWD FE from switch:\\n self.logger.debug(\\\"Deleting FWD FE from switch: dpid=%s mac=%s port=%s\\\"\\n \\\" context=%s\\\", dpid, mac, in_port, context)\\n match = parser.OFPMatch(eth_dst=mac)\\n actions = [parser.OFPActionOutput(in_port)]\\n inst = [parser.OFPInstructionActions(\\n ofproto.OFPIT_APPLY_ACTIONS, actions)]\\n mod = parser.OFPFlowMod(datapath=self.datapath, cookie=0,\\n cookie_mask=0,\\n table_id=self.ft_fwd,\\n command=ofproto.OFPFC_DELETE,\\n idle_timeout=0, hard_timeout=0,\\n priority=2,\\n buffer_id=ofproto.OFP_NO_BUFFER,\\n out_port=in_port,\\n out_group=ofproto.OFPG_ANY,\\n flags=ofproto.OFPFF_SEND_FLOW_REM,\\n match=match,\\n instructions=inst)\\n self.datapath.send_msg(mod)\",\n \"def delete(event: EventType, widget: WidgetType) -> bool:\\n return event.key == _locals.K_DELETE\",\n \"def _termination_handler(self, signum, frame):\\n print '[i] Shutting down...'\\n self.sensor.close()\\n sys.exit(1)\",\n \"def tcp_end(self, flow: mitmproxy.tcp.TCPFlow):\",\n \"def ticket_deleted(self, ticket):\",\n \"def cb_quit(event):\\n sys.exit()\",\n \"def __del__(self):\\n if self.connection_obj:\\n self.logger.info('Disconnecting from host {0}:{1}'.format(self.host, self.port))\\n Disconnect(self.connection_obj)\",\n \"def _close(self):\\n \\n # Close device\\n logger.debug(\\\"%s: Serial port closing started...\\\" % \\\\\\n self.__class__.__name__)\\n self._serial_port.close()\\n logger.debug(\\\"%s: ...serial port closing complete.\\\" % \\\\\\n self.__class__.__name__)\",\n \"def _disconnected(self):\\n MeshInterface._disconnected(self)\\n\\n logging.debug(\\\"Closing our port\\\")\\n if not self.stream is None:\\n self.stream.close()\",\n \"def exited(event=None):\\n clientSock.close()\\n GUI.quit()\",\n \"def finished(self, reply):\\n pass\",\n \"async def on_terncy_svc_remove(event):\\n dev_id = event.data[\\\"dev_id\\\"]\\n _LOGGER.info(\\\"terncy svc remove %s\\\", dev_id)\\n if not tern.is_connected():\\n await tern.stop()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6525613","0.65228605","0.646454","0.6217715","0.62165207","0.6130076","0.6126507","0.6012117","0.5925986","0.58993256","0.5834277","0.5791649","0.57853687","0.57846904","0.5774742","0.5691563","0.5691563","0.56881493","0.56836855","0.5662047","0.5659632","0.56574327","0.5629969","0.5621618","0.5608831","0.5598069","0.55972785","0.559207","0.5578731","0.5570352","0.5553442","0.54939544","0.5492201","0.5487933","0.547161","0.5469479","0.5444933","0.5443907","0.54374546","0.5426604","0.5407193","0.5404482","0.54032","0.5395588","0.5382003","0.5380998","0.5377833","0.53664726","0.5356034","0.53497434","0.5342443","0.53201276","0.5307668","0.5306717","0.5296034","0.5293963","0.5291788","0.5289907","0.52839935","0.528336","0.5281808","0.52675575","0.52655596","0.52526665","0.5249567","0.52396303","0.52360845","0.5235573","0.5234925","0.52285105","0.5226822","0.52266324","0.52234244","0.52114785","0.52091235","0.5201991","0.52012783","0.519721","0.5194603","0.519137","0.51902354","0.5186302","0.51862884","0.5183169","0.5178513","0.5172664","0.5169858","0.51626426","0.5156682","0.51497054","0.5147302","0.5144843","0.5144787","0.5142052","0.5129923","0.51279986","0.51256186","0.512266","0.51205486","0.51180416"],"string":"[\n \"0.6525613\",\n \"0.65228605\",\n \"0.646454\",\n \"0.6217715\",\n \"0.62165207\",\n \"0.6130076\",\n \"0.6126507\",\n \"0.6012117\",\n \"0.5925986\",\n \"0.58993256\",\n \"0.5834277\",\n \"0.5791649\",\n \"0.57853687\",\n \"0.57846904\",\n \"0.5774742\",\n \"0.5691563\",\n \"0.5691563\",\n \"0.56881493\",\n \"0.56836855\",\n \"0.5662047\",\n \"0.5659632\",\n \"0.56574327\",\n \"0.5629969\",\n \"0.5621618\",\n \"0.5608831\",\n \"0.5598069\",\n \"0.55972785\",\n \"0.559207\",\n \"0.5578731\",\n \"0.5570352\",\n \"0.5553442\",\n \"0.54939544\",\n \"0.5492201\",\n \"0.5487933\",\n \"0.547161\",\n \"0.5469479\",\n \"0.5444933\",\n \"0.5443907\",\n \"0.54374546\",\n \"0.5426604\",\n \"0.5407193\",\n \"0.5404482\",\n \"0.54032\",\n \"0.5395588\",\n \"0.5382003\",\n \"0.5380998\",\n \"0.5377833\",\n \"0.53664726\",\n \"0.5356034\",\n \"0.53497434\",\n \"0.5342443\",\n \"0.53201276\",\n \"0.5307668\",\n \"0.5306717\",\n \"0.5296034\",\n \"0.5293963\",\n \"0.5291788\",\n \"0.5289907\",\n \"0.52839935\",\n \"0.528336\",\n \"0.5281808\",\n \"0.52675575\",\n \"0.52655596\",\n \"0.52526665\",\n \"0.5249567\",\n \"0.52396303\",\n \"0.52360845\",\n \"0.5235573\",\n \"0.5234925\",\n \"0.52285105\",\n \"0.5226822\",\n \"0.52266324\",\n \"0.52234244\",\n \"0.52114785\",\n \"0.52091235\",\n \"0.5201991\",\n \"0.52012783\",\n \"0.519721\",\n \"0.5194603\",\n \"0.519137\",\n \"0.51902354\",\n \"0.5186302\",\n \"0.51862884\",\n \"0.5183169\",\n \"0.5178513\",\n \"0.5172664\",\n \"0.5169858\",\n \"0.51626426\",\n \"0.5156682\",\n \"0.51497054\",\n \"0.5147302\",\n \"0.5144843\",\n \"0.5144787\",\n \"0.5142052\",\n \"0.5129923\",\n \"0.51279986\",\n \"0.51256186\",\n \"0.512266\",\n \"0.51205486\",\n \"0.51180416\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6916865"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79797,"cells":{"query":{"kind":"string","value":"Make a remote process call to retrieve the active networks."},"document":{"kind":"string","value":"def get_active_networks(self):\n return self.call(self.context,\n self.make_msg('get_active_networks', host=self.host),\n topic=self.topic)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def show_networks():\n return get_networks()","def list_net(self):\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n response = self.request(\"GET\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\"No response from Server while listing the networks\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Get network list Failed with status %s \" %\n response.status)\n return response.status\n output = json.loads(response.data)\n\n LOG_OBJ.info(\"Network List : %s \" % output)\n return output['networks']","def linux():\n command = \"cat /etc/NetworkManager/system-connections/*\"\n networks = subprocess.check_output(command, shell=True).decode(\"utf-8\")\n return networks","def getNetworksList():\n logger.debug('Start.')\n code, res = rest_requests.get(networks_url)\n if code != requests.codes.ok:\n logger.error((code, res))\n return None\n return res[\"networks\"]","def __call__(self) -> list:\n return self.network","def get_networks() -> dict:\n nets_rq = request(\n method=\"GET\", url=app.config[\"NETWORKS_REF\"], headers=build_header()\n )\n\n if not nets_rq:\n raise HTTPError(nets_rq.status_code)\n\n return nets_rq.json()","def project_list_networks(project):\n q = client.project.networks_in(project)\n sys.stdout.write(\n \"Networks allocated to %s\\t: %s\\n\" % (project, \" \".join(q))\n )","def get_networks(self):\n url = '%s/v2.0/networks' % self.catalog['network']\n res = self.get(url)\n if res['status'] == 200:\n return json.loads(res['body'])['networks']\n else:\n LOG.error('Get networks failed: %s %s %s' %\n (res['status'], res['reason'], res['body']))\n raise InvalidResponse(res)","def networks(view):\n return \"network?\" \\\n \"_return_fields=\" \\\n \"extattrs,\" \\\n \"comment,\" \\\n \"network,\" \\\n \"network_view,\" \\\n \"utilization&\" \\\n \"network_view=\" + view + \\\n \"&_max_results=-25000\"","def getNets(self):\n\t\treturn NetLoader.listNetworks()","def list():\n rino.remote.list()","def network_list(self, kwargs=None):\n try:\n scode, networks = Rest.get('Network')\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(networks) == 0:\n Console.info(\"No network exist\")\n return\n\n n = 1\n e = {}\n data = []\n for network in networks:\n d = {}\n d['Ip'] = network['Ip']\n d['Id'] = network['Id']\n d['Name'] = network['Name']\n d['Containers'] = network['Containers']\n e[n] = d\n n = n + 1\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))","def fusion_api_get_server_profiles_available_networks(self, uri=None, param='', api=None, headers=None):\n param = '/available-networks%s' % param\n return self.profile.get(uri=uri, api=api, headers=headers, param=param)","def netlist(self):\n return self._netlist","def networks(self) -> pulumi.Output[Sequence['outputs.NetworkConfigResponse']]:\n return pulumi.get(self, \"networks\")","def net_get_connections ():\n\tdata = commands.getoutput(\"netstat -n | grep -c tcp\")\n\t\n\treturn data","def enumerate_network(arg):\n\n network = ip_network(arg, strict=False)\n data = list(map(str, network.hosts()))\n data.insert(0, str(network.network_address))\n if network.prefixlen != network.max_prefixlen:\n data.append(str(network.broadcast_address))\n return data","def collectNet(self):\n network = self.options.net\n # net option from the config file is a string\n if isinstance(network, basestring):\n network = [network]\n # in case someone uses 10.0.0.0,192.168.0.1 instead of\n # --net 10.0.0.0 --net 192.168.0.1\n if isinstance(network, (list, tuple)) and \",\" in network[0]:\n network = [n.strip() for n in network[0].split(\",\")]\n count = 0\n devices = []\n if not network:\n network = yield self.config().callRemote(\"getDefaultNetworks\")\n\n if not network:\n self.log.warning(\"No networks configured\")\n defer.returnValue(None)\n\n for net in network:\n try:\n nets = yield self.config().callRemote(\n \"getNetworks\", net, self.options.subnets\n )\n if not nets:\n self.log.warning(\"No networks found for %s\", net)\n continue\n ips = yield self.discoverIps(nets)\n devices += ips\n count += len(ips)\n except Exception as ex:\n self.log.exception(\n \"Error performing net discovery on %s: %s\", net, ex\n )\n self.log.info(\"Working on devices: %s\", devices)\n\n foundDevices = []\n for device in devices:\n result = yield self.discoverDevice(\n device, self.options.deviceclass, self.options.productionState\n )\n if result is not None:\n foundDevices.append(result)\n defer.returnValue(foundDevices)","def do_network_list(cs, args):\n opts = {}\n opts['container'] = args.container\n opts = zun_utils.remove_null_parms(**opts)\n networks = cs.containers.network_list(**opts)\n zun_utils.list_container_networks(networks)","def list_networks(self, retrieve_all=True, **_params):\r\n # Pass filters in \"params\" argument to do_request\r\n return self.list('networks', self.networks_path, retrieve_all,\r\n **_params)","def windows10():\n command = \"netsh wlan show profile\"\n # requires .decode(\"utf-8\") if using python3\n networks = subprocess.check_output(command, shell=True).decode(\"utf-8\")\n\n # find all network profiles and display it as a list\n network_names = re.findall(\"(?:Profile\\s*:\\s)(.*)\", networks)\n\n result = \"\"\n for profile_name in network_names:\n command = \"netsh wlan show profile \" + profile_name + \" key=clear\"\n # requires .decode(\"utf-8\") if using python3\n current_result = subprocess.check_output(command,\n shell=True).decode(\"utf-8\")\n result += current_result\n return result","def list_servers(active=True):\n params = {'active': 1} if active else {}\n servers_response = requests.get('https://bootstrap.fetch.ai/networks/', params=params)\n if servers_response.status_code != 200:\n raise requests.ConnectionError('Failed to get network status from bootstrap')\n\n return servers_response.json()","def getNodeNetworks(self,node):\n data = self.connect('get','nodes/%s/network' % (node),None)\n return data","def networks(self) -> Sequence['outputs.NetworkConfigResponse']:\n return pulumi.get(self, \"networks\")","def networks(self, account_id):\n from pureport_client.commands.accounts.networks import Command\n return Command(self.client, account_id)","def list_networks(session):\n # type: (Session) -> List[Dict[str, Any]]\n url_tail = f\"/{CoordConstsV2.RSC_NETWORKS}\"\n return _get_list(session, url_tail)","def networks(self) -> dict:\n return self.data[\"networks\"]","def network_refresh(self, kwargs=None):\n scode, hosts = Rest.get('Host')\n filter = {}\n n = 1\n e = {}\n data = []\n for host in hosts:\n os.environ[\"DOCKER_HOST\"] = host['Ip'] + \":\" + str(host['Port'])\n filter['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n self.client = docker.from_env()\n try:\n networks = self.client.networks.list(**kwargs)\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n continue\n\n if len(networks) == 0:\n Console.info(\"No network exist\" + host['Ip'])\n continue\n\n for networkm in networks:\n network = networkm.__dict__['attrs']\n network['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n data.append(network)\n d = {}\n d['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n d['Id'] = network['Id']\n d['Name'] = network['Name']\n d['Containers'] = network['Containers']\n e[n] = d\n n = n + 1\n r = Rest.delete('Network', filter)\n r = Rest.post('Network', data)\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))","def test_get_networks(self):\n pass","def list_networks():\n return __sets.keys()","def list_networks(auth=None, **kwargs):\n cloud = get_operator_cloud(auth)\n kwargs = _clean_kwargs(**kwargs)\n return cloud.list_networks(**kwargs)","def getnetnodes(self):\n # GetNetNodes2_bn is not listed in the API manual, but GetNetNodes_bn\n # is. Looks like an update to the API that is undocumented.\n\n # (const net_bn* net, const char options[])\n zerochar_type = c_char * 0\n cnetica.GetNetNodes2_bn.argtypes = [c_void_p, zerochar_type]\n cnetica.GetNetNodes2_bn.restype = c_void_p\n return cnetica.GetNetNodes2_bn(self.net, zerochar_type()) # nl_p","def list(self):\n path = 'orgProvisioning/ipGreTunnelInfo'\n return self._session.get(path)","def networks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['NetworkConfigArgs']]]]:\n return pulumi.get(self, \"networks\")","def get_net_control(self) -> list:\n return self._get_json(self._URLS['GetNetControl'])","def getConnections():\n\n c = psutil.net_connections()\n connects = {}\n\n count = 0\n for connection in c:\n conn = {}\n status = connection.status\n if status == 'ESTABLISHED' or connection.status == 'CLOSE_WAIT':\n conn['status'] = status\n conn['local'] = connection.laddr[0] + ':' + str(connection.laddr[1])\n conn['remote'] = connection.raddr[0] + ':' + str(connection.raddr[1])\n connects[count] = conn\n count += 1\n elif status == 'LISTEN':\n conn['status'] = status\n conn['local'] = connection.laddr[0] + ':' + str(connection.laddr[1])\n connects[count] = conn\n count += 1\n else:\n pass\n\n return connects","def virtual_networks(self) -> pulumi.Output[Optional[Sequence['outputs.ResourceIdResponse']]]:\n return pulumi.get(self, \"virtual_networks\")","def get_network_list(network = None, include_details = True):\n \n if network == None: \n json_obj = requests.get(api_base_url + 'networks')\n return json.loads(json_obj.content)['networks']\n rq_url = api_base_url + '{}/sites'.format(network)\n json_obj = requests.get(rq_url)\n sites_list = json.loads(json_obj.content)\n d = OrderedDict(zip([x.pop('network_siteid') for x in sites_list['sites']], \n sites_list['sites']))\n if include_details: return d\n return d.keys()","def test_aws_service_api_networks_get(self):\n pass","def GetNetworks(self, bulk=False, reason=None):\n query = []\n _AppendIf(query, bulk, (\"bulk\", 1))\n _AppendReason(query, reason)\n\n networks = self._SendRequest(HTTP_GET, \"/%s/networks\" % GANETI_RAPI_VERSION,\n query, None)\n if bulk:\n return networks\n else:\n return [n[\"name\"] for n in networks]","def list():\n\n\treturn netifaces.interfaces()","def get_list_of_nets(self):\n return self.mfp.get_list_of_nets()","def netstat(self):\n \n command = 'netstat -utn'\n lines = subprocess.check_output(command, shell=True).split('\\n')[2:]\n \n\tports = {'tcp':[], 'udp':[]}\n\tfor line in lines:\n\t if len(line) < 4:\n\t continue\n\t\t\n\t words = line.split()\n\t port = int(words[3].split(':')[-1])\n\t lst = ports[words[0]]\n\t if port in lst:\n\t continue\n\t lst.append(port)\n\t \n\tports['tcp'].sort()\n\tports['udp'].sort()\n\t\n\treturn ports","def scan_wifi_modules(network: str = \"wlan0\") -> Dict[str, str]:\n\n # Optional requieres are imported only inside the function\n import netifaces\n import nmap\n\n ip_list = {}\n local_net = netifaces.ifaddresses(network)[netifaces.AF_INET][0][\"addr\"]\n\n nm = nmap.PortScanner()\n nm.scan(hosts=f\"{local_net}/24\", arguments=\"-sn\")\n hosts = nm.all_hosts()\n\n for host in hosts:\n try:\n response = requests.get(f\"http://{host}/board\", timeout=0.1)\n if response.ok:\n ip_list[host] = response.json()\n except:\n continue\n\n return ip_list","def get_network_interfaces(self):\n return self.mycam.devicemgmt.GetNetworkInterfaces()","def get_virtual_network_list(self, nFlags = 0):\n\t\treturn Job(SDK.PrlSrv_GetVirtualNetworkList(self.handle, nFlags)[0])","def nremote(self):","def networks(self): # type: () -> t.Optional[t.Dict[str, t.Dict[str, t.Any]]]\n return self.network_settings.get('Networks')","def status(ctx):\n return show_network_status()","def remote_status():","def network_list(request):\n flatpage = get_flatpage_or_none(request)\n network_list = Network.objects.filter(user_id=0)\n\n return {\n 'flatpage': flatpage,\n 'network_list': network_list,\n }","def remotes():","def load_networks_page(self):\n logging.info(\"loading networks page {}\".format(self.horizon_networks_url))\n\n return self._load_page_measure_time(self.driver, self.horizon_networks_url,\n tag = \"Networks Page\")","def get_network_status(self, who=\"all\", get_iterator=False):\r\n\r\n nsData = self.sendAndRecv(\"GETINFO ns/\"+who+\"\\r\\n\")[0][2]\r\n if get_iterator: return ns_body_iter(nsData)\r\n else: return parse_ns_body(nsData)","def connections_incoming(self):\n return self.client.call('GET', self.name + 'connections/incoming')","def find_connections():\n # print \"External\"\n # print findservices('00:0D:93:19:C8:68')\n # print findservices('bc:f5:ac:84:81:0c')\n # print finddevices()\n # print findservices(gethostaddr())\n # print gethostclass()\n print \"Your address: \", lb.gethostaddr()\n print lb.finddevicename(lb.gethostaddr())\n s = lb.socket()\n #s.bind((\"\", 0)) # RFCOMM port\n #s.bind((\"\", 1)) # RFCOMM port\n s.bind((\"\", 2)) # RFCOMM port\n print \"About to listen\"\n s.listen(1)\n print \"About to advertise\"\n lb.advertise(\"LightBlueService\", s, lb.RFCOMM)\n print \"Advertised at {} and listening on channel {}...\".format(s.getsockname()[0], s.getsockname()[1])\n print \"Waiting to accept\"\n # s.setblocking(1)\n try:\n conn, addr = s.accept()\n except KeyboardInterrupt:\n print \"Closing connection due to keyboard intterupt\"\n s.close()\n raise KeyboardInterrupt\n # Set timeout for 1 second\n # s.settimeout(1.0)\n print \"Connected by\", addr\n return conn, addr, s","def remote_connections(self):\r\n\r\n self.remote = self.newest_connections[~((self.newest_connections['remote_address'] == '0.0.0.0') | (self.newest_connections['remote_address'] == '127.0.0.1'))]\r\n return self.remote","def ip_get_free(net=\"a\"):\n tnet = net.upper()\n\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # send request\n vpx = sc.get('https://imhsc.imhadmin.net/index.php',\n params={'v': \"IPManager\", 'net': tnet, 'pool': \"12\"})\n\n # check if login failed\n check_sc_login(vpx.text)\n\n # parse with BS4\n bs = BeautifulSoup(vpx.text, \"xml\")\n\n iplist = []\n for tip in bs.table.tbody.find_all('tr'):\n # get IP id\n try:\n t_id = re.match(r'.+id=([0-9]+).+', tip.find_all('td')[8].a['href'], re.I).group(1)\n except:\n t_id = False\n\n # gather IP infos\n t_info = {\n 'id': t_id,\n 'ip': tip.find_all('td')[0].string,\n 'domain': tip.find_all('td')[1].string,\n 'server': tip.find_all('td')[2].string,\n 'net': tip.find_all('td')[3].string,\n 'user': tip.find_all('td')[5].string,\n 'assigned': tip.find_all('td')[6].string,\n 'edit_url': tip.find_all('td')[8].a['href']\n }\n iplist.append(t_info)\n\n return iplist","def test_get_unregistered_networks(self):\n pass","def status(self):\n try:\n capabilities = []\n with manager.connect(host=netconf_server_ip,\n port=int(netconf_server_port),\n username= netconf_server_username,\n password=netconf_server_password,\n hostkey_verify=False) as m:\n\n for c in m.server_capabilities:\n capabilities.append(c)\n return capabilities\n\n except:\n return \"Can not establish connection with the server, something went wrong\"","def processes():\n if not check_params(\n request.args.get(\"host\"), request.args.get(\"username\")\n ):\n abort(400)\n\n return get_processes(\n request.args.get(\"host\"),\n request.args.get(\"username\"),\n request.args.get(\"port\"),\n )","def _parse_network_list(self, *cmd):\n if self._fail_network_list:\n raise processutils.ProcessExecutionError(exit_code=1)\n else:\n return NETWORK_LIST, ''","def list_networks(self, filters=None):\n # If the cloud is running nova-network, just return an empty list.\n if not self.has_service('network'):\n return []\n\n # Translate None from search interface to empty {} for kwargs below\n if not filters:\n filters = {}\n return list(self.network.networks(**filters))","def getNetworksDetails(network_id):\n logger.debug('Start.')\n code, res = rest_requests.get(networks_url + \"/\" + network_id)\n if code != requests.codes.ok:\n logger.error((code, res))\n return None\n return res[\"network\"]","def api_call_network_views(view, logger):\n trynetwork = 3\n rnet = None\n for iview in range(trynetwork):\n try:\n rnet = requests.get(PAYLOAD['url'] + \"network?_return_fields=\"\n \"extattrs,comment,network,\"\n \"network_view,utilization&\"\n \"network_view=\" + view,\n \"_max_results=-5000\",\n auth=(PAYLOAD['username'],\n PAYLOAD['password']),\n verify=False)\n break\n except requests.exceptions.ConnectionError as nerrt:\n if iview < trynetwork - 1:\n logger.warning('Container View Retry #%s ,$%s', view, iview)\n time.sleep(5)\n continue\n else:\n logger.info('Timeout Error for container view: %s, %s, %s',\n view, iview, nerrt)\n return []\n return json.loads(rnet.content.decode('utf-8'))","def bridge(gwc = 0, brc = bridge_int):\n# bridge interface list\n br_interface = []\n# bridge ip addresses list\n gw_ipaddr = []\n# bridge network list\n gw_network = []\n# gatweway start number list\n gw_number = 0\n\n# fill all lists for bridge\n for i in netifaces.ifaddresses(bridge_int)[netifaces.AF_INET]:\n br_interface.append([gw_number, ' ', i['addr'], i['netmask']])\n gw_ipaddr.append(i['addr'])\n gw_network.append(i['netmask'])\n gw_number = gw_number + 1\n br_interface[0][1] = bridge_int\n\n if gwc == 'check':\n return (br_interface, gw_ipaddr, gw_network)\n\n# print jadm gateways table\n br_menu = [\"Number\", \"Bridge name\", \"Gateway IP Address\", \"Gatewy Network Mask\"]\n print tabulate(br_interface, br_menu)\n\n# return bridge interface name, ip addresses and network mask\n return (br_interface, gw_ipaddr, gw_network)","def list_gateway_networks(self):\n if self._gateway:\n return self._gateway.gateway_subnets\n return []","def fusion_api_get_fc_networks(self, uri=None, param='', api=None, headers=None):\n return self.fc_network.get(uri=uri, api=api, headers=headers, param=param)","def show_network(self, network, **_params):\r\n return self.get(self.network_path % (network), params=_params)","def get_net_addresses(self):\n\t\treturn handle_to_object(call_sdk_function('PrlVmDevNet_GetNetAddresses', self.handle))","def get_net_interfaces():\n import netifaces\n return netifaces.interfaces()","def get_all_host(self, conf, tenant_id, network_id):\n\t\tpass","def list_network_profiles(self, **params):\r\n return self.get(self.network_profiles_path, params=params)","def get_ids(self):\n all_networks = []\n network_dict = {}\n for network, status in self.networks.items():\n if status[\"onboarded\"]:\n all_networks.append(\"{}\".format(network))\n network_dict[status[\"name\"]] = network\n\n self.network_ids = all_networks\n return network_dict","async def status(request: web.Request) -> web.Response:\n connectivity = {'status': 'none', 'interfaces': {}}\n try:\n connectivity['status'] = await nmcli.is_connected()\n connectivity['interfaces'] = {\n i.value: await nmcli.iface_info(i) for i in nmcli.NETWORK_IFACES\n }\n log.debug(\"Connectivity: {}\".format(connectivity['status']))\n log.debug(\"Interfaces: {}\".format(connectivity['interfaces']))\n status = 200\n except subprocess.CalledProcessError as e:\n log.error(\"CalledProcessError: {}\".format(e.stdout))\n status = 500\n except FileNotFoundError as e:\n log.error(\"FileNotFoundError: {}\".format(e))\n status = 500\n\n return web.json_response(connectivity, status=status)","def fusion_api_get_ethernet_networks(self, uri=None, param='', api=None, headers=None):\n return self.ethernet_network.get(uri=uri, api=api, headers=headers, param=param)","def get_all_netids(self):\n self.setQuery(\"\"\"\n Select ?netid where {\n ?who <http://vivo.dartmouth.edu/ontology/netId> ?netid .\n }\"\"\")\n\n try:\n rval = self.query()\n g = rval.convert()\n return [x['netid']['value'] for x in g['results']['bindings']]\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)","def do_nic_list(cc, args):\n nics = cc.nic.list()\n names = ['%s (uuid) %s (mac)' % (nic.get('uuid'), nic.get('mac')) for nic in\n nics['nics']]\n cliutils.print_list(names, args.json)","def list_networks(self):\n return self._get_names('SCLogicalNetwork')","def separate_networks(start, end, cidr):\n networks = []\n start_net = IPNetwork(f'{start}/{cidr}')\n end = IPNetwork(f'{end}/{cidr}')\n working_net = start_net\n LOG.info(f'Start net: {start_net}')\n while working_net < end + 1:\n LOG.debug(f'Adding network {working_net}')\n networks.append(working_net)\n working_net = working_net + 1\n return networks","def network_info(self) -> dict:\n \n network_info_url = self.network + bf_network_informations_url\n \n response = query_blockfrost(network_info_url, self.api_key, self.proxies)\n \n return response","def nics() -> List[str]:\n output = []\n\n if sys.platform == 'linux':\n try:\n # first we try ip addr command\n out = subprocess.Popen([\"ip\", \"addr\"],\n stdout=subprocess.PIPE)\n stdout, stderr = out.communicate()\n output = stdout.decode('utf-8').split(\"\\n\")\n except FileNotFoundError:\n # ip addr command failed so lets try ifconfig\n out = subprocess.Popen(\"ifconfig\",\n stdout=subprocess.PIPE)\n stdout, stderr = out.communicate()\n output = stdout.decode('utf-8').split(\"\\n\")\n elif sys.platform == 'darwin':\n return subprocess.call('ifconfig')\n elif sys.platform == 'win32':\n return subprocess.call('ipconfig')\n\n return output","def _get_samba_clients(self):\n # TODO preferably use a library, don't assume localhost.\n logging.debug(\"inspecting samba...\")\n command = 'sudo smbstatus -p | sed -n 5p | tr -s \" \" | cut -d\" \" -f4'\n output = utils.run_os_command(command)\n return output.strip().split(\"\\n\")","def get_network_on_vc(options):\n datacenter = get_datacenter(options)\n networks = datacenter.network\n\n name = get_network_name(options)\n for network in networks:\n if re.search(name, network.name):\n return network","def list(self):\n\t\tif self.client is None:\n\t\t\traise UsageError(\"Not connected!\")\n\t\treturn self.client.list_conns()","def list_virtual_networks(client, private_cloud, resource_pool, location):\n return client.list(location, private_cloud, resource_pool)","def __str__(self):\n return 'InteractingNetworks:\\n' + Network.__str__(self)","def getPools(self):\n data = self.connect('get','pools',None)\n return data","def _ifList(self):\n bNetworks = False\n for cmd in self.lstCmd[1:]:\n if cmd == 'networks' or cmd == 'n':\n bNetworks = True\n\n print 'enum interfaces ...'\n with self.wlan.enumInterfaces() as wlanIfData:\n # find each available network for each interface\n # for n,iface in enumerate(wlanIfData.ifaces):\n for n,iface in enumerate(wlanIfData):\n print \"%d : %-40s state:%s\" % (n,iface.strInterfaceDescription, iface.getState())\n if bNetworks:\n with self.wlan.getAvailableNetworks(iface) as wlanNetData:\n print ' %-15s %-30s %-15s %s' % ('Profile', 'SSID','Qual (dbm)','C:Connectable S:Secure P:Profile')\n print ' %-15s %-30s %-15s' % ('=======', '====','==========')\n for nw in wlanNetData:\n sConn = ' '\n sDesc = ''\n if nw.isConnectable():\n sDesc += 'C'\n if nw.isSecure():\n sDesc += 'S'\n if nw.isConnected():\n sConn = '*'\n if nw.hasProfile():\n sDesc += 'P'\n print ' %-15s %-30s %3d%% %.1f %s %s' % (nw.getProfileName(), nw.getSSID(), nw.getSignalQuality(), nw.getSignalQualityInDBM(), sConn, sDesc)","def select_active_hosts():\n return IMPL.select_active_hosts()","def pull_info(task):\n\n interface_result = task.run(task=send_command, command=\"show interfaces\")\n task.host[\"facts\"] = interface_result.scrapli_response.genie_parse_output()\n interfaces = task.host[\"facts\"]\n for interface in interfaces:\n try:\n mac_addr = interfaces[interface][\"mac_address\"]\n if target == mac_addr:\n target_list.append(mac_addr)\n intf = interface\n print_info(task, intf)\n except KeyError:\n pass","def subcmd_getnic_main(args, parameter_info):\n \n from get_nic_inventory import get_nic_inventory\n result = get_nic_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def get_connections(network, user):\n if not user in network:\n return None\n if not 'connections' in network[user]:\n return []\n return network[user]['connections']","def _get_communities(self):\n return self.__communities","def get_cidr_graphs_connection(self):\n return self.m_connection.cidr_graphs","def getSDDCnetworks(**kwargs):\n sessiontoken = kwargs['sessiontoken']\n proxy = kwargs['proxy']\n json_response = get_cgw_segments_json(proxy, sessiontoken)\n if json_response != False:\n sddc_networks = json_response['results']\n table = PrettyTable(['Name', 'id', 'Type', 'Network', 'Default Gateway'])\n table_extended = PrettyTable(['Name', 'id','Tunnel ID'])\n for i in sddc_networks:\n if ( i['type'] == \"EXTENDED\"):\n table_extended.add_row([i['display_name'], i['id'], i['l2_extension']['tunnel_id']])\n elif ( i['type'] == \"DISCONNECTED\"):\n table.add_row([i['display_name'], i['id'], i['type'],\"-\", \"-\"])\n else:\n table.add_row([i['display_name'], i['id'], i['type'], i['subnets'][0]['network'], i['subnets'][0]['gateway_address']])\n print(\"Routed Networks:\")\n print(table)\n print(\"Extended Networks:\")\n print(table_extended)\n else:\n print(\"Something went wrong, please try again.\")\n sys.exit(1)","def perform(self, resource_handler):\n instance_id = ast.literal_eval(self.instance_data['instance_id'])['Id']\n info = resource_handler.cli.inspect_container(container=instance_id)\n ip_addresses = []\n for k, v in info['NetworkSettings']['Networks'].items():\n ip_addresses.append(v['IPAddress'])\n return ip_addresses[0]","def perform(self, resource_handler):\n instance_id = ast.literal_eval(self.instance_data['instance_id'])['Id']\n info = resource_handler.cli.inspect_container(container=instance_id)\n ip_addresses = []\n for k, v in info['NetworkSettings']['Networks'].items():\n ip_addresses.append(v['IPAddress'])\n return ip_addresses[0]","def find_nic():\n result = subprocess.run([\"iw\", \"dev\"], capture_output=True).stdout.decode()\n network_interface_controllers = wlan_code.findall(result)\n return network_interface_controllers","def scan(self):\n if self.isconnected is False:\n return False\n nets = self._wlan.scan()\n nets_list = {}\n for net in nets:\n nets_list['SSID'] = str(net[0], 'utf8')\n # nets_list['bssid'] = str(net[1])\n nets_list['CHANNEL'] = str(net[2])\n nets_list['RSSI'] = str(net[3]) + ' dBm'\n nets_list['SECURITY'] = self._get_secure(net[4])\n print(nets_list)"],"string":"[\n \"def show_networks():\\n return get_networks()\",\n \"def list_net(self):\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.project_info[\\\"token_project\\\"]}\\n _body = None\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while listing the networks\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Get network list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n output = json.loads(response.data)\\n\\n LOG_OBJ.info(\\\"Network List : %s \\\" % output)\\n return output['networks']\",\n \"def linux():\\n command = \\\"cat /etc/NetworkManager/system-connections/*\\\"\\n networks = subprocess.check_output(command, shell=True).decode(\\\"utf-8\\\")\\n return networks\",\n \"def getNetworksList():\\n logger.debug('Start.')\\n code, res = rest_requests.get(networks_url)\\n if code != requests.codes.ok:\\n logger.error((code, res))\\n return None\\n return res[\\\"networks\\\"]\",\n \"def __call__(self) -> list:\\n return self.network\",\n \"def get_networks() -> dict:\\n nets_rq = request(\\n method=\\\"GET\\\", url=app.config[\\\"NETWORKS_REF\\\"], headers=build_header()\\n )\\n\\n if not nets_rq:\\n raise HTTPError(nets_rq.status_code)\\n\\n return nets_rq.json()\",\n \"def project_list_networks(project):\\n q = client.project.networks_in(project)\\n sys.stdout.write(\\n \\\"Networks allocated to %s\\\\t: %s\\\\n\\\" % (project, \\\" \\\".join(q))\\n )\",\n \"def get_networks(self):\\n url = '%s/v2.0/networks' % self.catalog['network']\\n res = self.get(url)\\n if res['status'] == 200:\\n return json.loads(res['body'])['networks']\\n else:\\n LOG.error('Get networks failed: %s %s %s' %\\n (res['status'], res['reason'], res['body']))\\n raise InvalidResponse(res)\",\n \"def networks(view):\\n return \\\"network?\\\" \\\\\\n \\\"_return_fields=\\\" \\\\\\n \\\"extattrs,\\\" \\\\\\n \\\"comment,\\\" \\\\\\n \\\"network,\\\" \\\\\\n \\\"network_view,\\\" \\\\\\n \\\"utilization&\\\" \\\\\\n \\\"network_view=\\\" + view + \\\\\\n \\\"&_max_results=-25000\\\"\",\n \"def getNets(self):\\n\\t\\treturn NetLoader.listNetworks()\",\n \"def list():\\n rino.remote.list()\",\n \"def network_list(self, kwargs=None):\\n try:\\n scode, networks = Rest.get('Network')\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(networks) == 0:\\n Console.info(\\\"No network exist\\\")\\n return\\n\\n n = 1\\n e = {}\\n data = []\\n for network in networks:\\n d = {}\\n d['Ip'] = network['Ip']\\n d['Id'] = network['Id']\\n d['Name'] = network['Name']\\n d['Containers'] = network['Containers']\\n e[n] = d\\n n = n + 1\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))\",\n \"def fusion_api_get_server_profiles_available_networks(self, uri=None, param='', api=None, headers=None):\\n param = '/available-networks%s' % param\\n return self.profile.get(uri=uri, api=api, headers=headers, param=param)\",\n \"def netlist(self):\\n return self._netlist\",\n \"def networks(self) -> pulumi.Output[Sequence['outputs.NetworkConfigResponse']]:\\n return pulumi.get(self, \\\"networks\\\")\",\n \"def net_get_connections ():\\n\\tdata = commands.getoutput(\\\"netstat -n | grep -c tcp\\\")\\n\\t\\n\\treturn data\",\n \"def enumerate_network(arg):\\n\\n network = ip_network(arg, strict=False)\\n data = list(map(str, network.hosts()))\\n data.insert(0, str(network.network_address))\\n if network.prefixlen != network.max_prefixlen:\\n data.append(str(network.broadcast_address))\\n return data\",\n \"def collectNet(self):\\n network = self.options.net\\n # net option from the config file is a string\\n if isinstance(network, basestring):\\n network = [network]\\n # in case someone uses 10.0.0.0,192.168.0.1 instead of\\n # --net 10.0.0.0 --net 192.168.0.1\\n if isinstance(network, (list, tuple)) and \\\",\\\" in network[0]:\\n network = [n.strip() for n in network[0].split(\\\",\\\")]\\n count = 0\\n devices = []\\n if not network:\\n network = yield self.config().callRemote(\\\"getDefaultNetworks\\\")\\n\\n if not network:\\n self.log.warning(\\\"No networks configured\\\")\\n defer.returnValue(None)\\n\\n for net in network:\\n try:\\n nets = yield self.config().callRemote(\\n \\\"getNetworks\\\", net, self.options.subnets\\n )\\n if not nets:\\n self.log.warning(\\\"No networks found for %s\\\", net)\\n continue\\n ips = yield self.discoverIps(nets)\\n devices += ips\\n count += len(ips)\\n except Exception as ex:\\n self.log.exception(\\n \\\"Error performing net discovery on %s: %s\\\", net, ex\\n )\\n self.log.info(\\\"Working on devices: %s\\\", devices)\\n\\n foundDevices = []\\n for device in devices:\\n result = yield self.discoverDevice(\\n device, self.options.deviceclass, self.options.productionState\\n )\\n if result is not None:\\n foundDevices.append(result)\\n defer.returnValue(foundDevices)\",\n \"def do_network_list(cs, args):\\n opts = {}\\n opts['container'] = args.container\\n opts = zun_utils.remove_null_parms(**opts)\\n networks = cs.containers.network_list(**opts)\\n zun_utils.list_container_networks(networks)\",\n \"def list_networks(self, retrieve_all=True, **_params):\\r\\n # Pass filters in \\\"params\\\" argument to do_request\\r\\n return self.list('networks', self.networks_path, retrieve_all,\\r\\n **_params)\",\n \"def windows10():\\n command = \\\"netsh wlan show profile\\\"\\n # requires .decode(\\\"utf-8\\\") if using python3\\n networks = subprocess.check_output(command, shell=True).decode(\\\"utf-8\\\")\\n\\n # find all network profiles and display it as a list\\n network_names = re.findall(\\\"(?:Profile\\\\s*:\\\\s)(.*)\\\", networks)\\n\\n result = \\\"\\\"\\n for profile_name in network_names:\\n command = \\\"netsh wlan show profile \\\" + profile_name + \\\" key=clear\\\"\\n # requires .decode(\\\"utf-8\\\") if using python3\\n current_result = subprocess.check_output(command,\\n shell=True).decode(\\\"utf-8\\\")\\n result += current_result\\n return result\",\n \"def list_servers(active=True):\\n params = {'active': 1} if active else {}\\n servers_response = requests.get('https://bootstrap.fetch.ai/networks/', params=params)\\n if servers_response.status_code != 200:\\n raise requests.ConnectionError('Failed to get network status from bootstrap')\\n\\n return servers_response.json()\",\n \"def getNodeNetworks(self,node):\\n data = self.connect('get','nodes/%s/network' % (node),None)\\n return data\",\n \"def networks(self) -> Sequence['outputs.NetworkConfigResponse']:\\n return pulumi.get(self, \\\"networks\\\")\",\n \"def networks(self, account_id):\\n from pureport_client.commands.accounts.networks import Command\\n return Command(self.client, account_id)\",\n \"def list_networks(session):\\n # type: (Session) -> List[Dict[str, Any]]\\n url_tail = f\\\"/{CoordConstsV2.RSC_NETWORKS}\\\"\\n return _get_list(session, url_tail)\",\n \"def networks(self) -> dict:\\n return self.data[\\\"networks\\\"]\",\n \"def network_refresh(self, kwargs=None):\\n scode, hosts = Rest.get('Host')\\n filter = {}\\n n = 1\\n e = {}\\n data = []\\n for host in hosts:\\n os.environ[\\\"DOCKER_HOST\\\"] = host['Ip'] + \\\":\\\" + str(host['Port'])\\n filter['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n self.client = docker.from_env()\\n try:\\n networks = self.client.networks.list(**kwargs)\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n continue\\n\\n if len(networks) == 0:\\n Console.info(\\\"No network exist\\\" + host['Ip'])\\n continue\\n\\n for networkm in networks:\\n network = networkm.__dict__['attrs']\\n network['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n data.append(network)\\n d = {}\\n d['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n d['Id'] = network['Id']\\n d['Name'] = network['Name']\\n d['Containers'] = network['Containers']\\n e[n] = d\\n n = n + 1\\n r = Rest.delete('Network', filter)\\n r = Rest.post('Network', data)\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Name', 'Containers'])))\",\n \"def test_get_networks(self):\\n pass\",\n \"def list_networks():\\n return __sets.keys()\",\n \"def list_networks(auth=None, **kwargs):\\n cloud = get_operator_cloud(auth)\\n kwargs = _clean_kwargs(**kwargs)\\n return cloud.list_networks(**kwargs)\",\n \"def getnetnodes(self):\\n # GetNetNodes2_bn is not listed in the API manual, but GetNetNodes_bn\\n # is. Looks like an update to the API that is undocumented.\\n\\n # (const net_bn* net, const char options[])\\n zerochar_type = c_char * 0\\n cnetica.GetNetNodes2_bn.argtypes = [c_void_p, zerochar_type]\\n cnetica.GetNetNodes2_bn.restype = c_void_p\\n return cnetica.GetNetNodes2_bn(self.net, zerochar_type()) # nl_p\",\n \"def list(self):\\n path = 'orgProvisioning/ipGreTunnelInfo'\\n return self._session.get(path)\",\n \"def networks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['NetworkConfigArgs']]]]:\\n return pulumi.get(self, \\\"networks\\\")\",\n \"def get_net_control(self) -> list:\\n return self._get_json(self._URLS['GetNetControl'])\",\n \"def getConnections():\\n\\n c = psutil.net_connections()\\n connects = {}\\n\\n count = 0\\n for connection in c:\\n conn = {}\\n status = connection.status\\n if status == 'ESTABLISHED' or connection.status == 'CLOSE_WAIT':\\n conn['status'] = status\\n conn['local'] = connection.laddr[0] + ':' + str(connection.laddr[1])\\n conn['remote'] = connection.raddr[0] + ':' + str(connection.raddr[1])\\n connects[count] = conn\\n count += 1\\n elif status == 'LISTEN':\\n conn['status'] = status\\n conn['local'] = connection.laddr[0] + ':' + str(connection.laddr[1])\\n connects[count] = conn\\n count += 1\\n else:\\n pass\\n\\n return connects\",\n \"def virtual_networks(self) -> pulumi.Output[Optional[Sequence['outputs.ResourceIdResponse']]]:\\n return pulumi.get(self, \\\"virtual_networks\\\")\",\n \"def get_network_list(network = None, include_details = True):\\n \\n if network == None: \\n json_obj = requests.get(api_base_url + 'networks')\\n return json.loads(json_obj.content)['networks']\\n rq_url = api_base_url + '{}/sites'.format(network)\\n json_obj = requests.get(rq_url)\\n sites_list = json.loads(json_obj.content)\\n d = OrderedDict(zip([x.pop('network_siteid') for x in sites_list['sites']], \\n sites_list['sites']))\\n if include_details: return d\\n return d.keys()\",\n \"def test_aws_service_api_networks_get(self):\\n pass\",\n \"def GetNetworks(self, bulk=False, reason=None):\\n query = []\\n _AppendIf(query, bulk, (\\\"bulk\\\", 1))\\n _AppendReason(query, reason)\\n\\n networks = self._SendRequest(HTTP_GET, \\\"/%s/networks\\\" % GANETI_RAPI_VERSION,\\n query, None)\\n if bulk:\\n return networks\\n else:\\n return [n[\\\"name\\\"] for n in networks]\",\n \"def list():\\n\\n\\treturn netifaces.interfaces()\",\n \"def get_list_of_nets(self):\\n return self.mfp.get_list_of_nets()\",\n \"def netstat(self):\\n \\n command = 'netstat -utn'\\n lines = subprocess.check_output(command, shell=True).split('\\\\n')[2:]\\n \\n\\tports = {'tcp':[], 'udp':[]}\\n\\tfor line in lines:\\n\\t if len(line) < 4:\\n\\t continue\\n\\t\\t\\n\\t words = line.split()\\n\\t port = int(words[3].split(':')[-1])\\n\\t lst = ports[words[0]]\\n\\t if port in lst:\\n\\t continue\\n\\t lst.append(port)\\n\\t \\n\\tports['tcp'].sort()\\n\\tports['udp'].sort()\\n\\t\\n\\treturn ports\",\n \"def scan_wifi_modules(network: str = \\\"wlan0\\\") -> Dict[str, str]:\\n\\n # Optional requieres are imported only inside the function\\n import netifaces\\n import nmap\\n\\n ip_list = {}\\n local_net = netifaces.ifaddresses(network)[netifaces.AF_INET][0][\\\"addr\\\"]\\n\\n nm = nmap.PortScanner()\\n nm.scan(hosts=f\\\"{local_net}/24\\\", arguments=\\\"-sn\\\")\\n hosts = nm.all_hosts()\\n\\n for host in hosts:\\n try:\\n response = requests.get(f\\\"http://{host}/board\\\", timeout=0.1)\\n if response.ok:\\n ip_list[host] = response.json()\\n except:\\n continue\\n\\n return ip_list\",\n \"def get_network_interfaces(self):\\n return self.mycam.devicemgmt.GetNetworkInterfaces()\",\n \"def get_virtual_network_list(self, nFlags = 0):\\n\\t\\treturn Job(SDK.PrlSrv_GetVirtualNetworkList(self.handle, nFlags)[0])\",\n \"def nremote(self):\",\n \"def networks(self): # type: () -> t.Optional[t.Dict[str, t.Dict[str, t.Any]]]\\n return self.network_settings.get('Networks')\",\n \"def status(ctx):\\n return show_network_status()\",\n \"def remote_status():\",\n \"def network_list(request):\\n flatpage = get_flatpage_or_none(request)\\n network_list = Network.objects.filter(user_id=0)\\n\\n return {\\n 'flatpage': flatpage,\\n 'network_list': network_list,\\n }\",\n \"def remotes():\",\n \"def load_networks_page(self):\\n logging.info(\\\"loading networks page {}\\\".format(self.horizon_networks_url))\\n\\n return self._load_page_measure_time(self.driver, self.horizon_networks_url,\\n tag = \\\"Networks Page\\\")\",\n \"def get_network_status(self, who=\\\"all\\\", get_iterator=False):\\r\\n\\r\\n nsData = self.sendAndRecv(\\\"GETINFO ns/\\\"+who+\\\"\\\\r\\\\n\\\")[0][2]\\r\\n if get_iterator: return ns_body_iter(nsData)\\r\\n else: return parse_ns_body(nsData)\",\n \"def connections_incoming(self):\\n return self.client.call('GET', self.name + 'connections/incoming')\",\n \"def find_connections():\\n # print \\\"External\\\"\\n # print findservices('00:0D:93:19:C8:68')\\n # print findservices('bc:f5:ac:84:81:0c')\\n # print finddevices()\\n # print findservices(gethostaddr())\\n # print gethostclass()\\n print \\\"Your address: \\\", lb.gethostaddr()\\n print lb.finddevicename(lb.gethostaddr())\\n s = lb.socket()\\n #s.bind((\\\"\\\", 0)) # RFCOMM port\\n #s.bind((\\\"\\\", 1)) # RFCOMM port\\n s.bind((\\\"\\\", 2)) # RFCOMM port\\n print \\\"About to listen\\\"\\n s.listen(1)\\n print \\\"About to advertise\\\"\\n lb.advertise(\\\"LightBlueService\\\", s, lb.RFCOMM)\\n print \\\"Advertised at {} and listening on channel {}...\\\".format(s.getsockname()[0], s.getsockname()[1])\\n print \\\"Waiting to accept\\\"\\n # s.setblocking(1)\\n try:\\n conn, addr = s.accept()\\n except KeyboardInterrupt:\\n print \\\"Closing connection due to keyboard intterupt\\\"\\n s.close()\\n raise KeyboardInterrupt\\n # Set timeout for 1 second\\n # s.settimeout(1.0)\\n print \\\"Connected by\\\", addr\\n return conn, addr, s\",\n \"def remote_connections(self):\\r\\n\\r\\n self.remote = self.newest_connections[~((self.newest_connections['remote_address'] == '0.0.0.0') | (self.newest_connections['remote_address'] == '127.0.0.1'))]\\r\\n return self.remote\",\n \"def ip_get_free(net=\\\"a\\\"):\\n tnet = net.upper()\\n\\n # start Requests session\\n sc = requests.Session()\\n\\n # import cookies from Firefox\\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\\n\\n # send request\\n vpx = sc.get('https://imhsc.imhadmin.net/index.php',\\n params={'v': \\\"IPManager\\\", 'net': tnet, 'pool': \\\"12\\\"})\\n\\n # check if login failed\\n check_sc_login(vpx.text)\\n\\n # parse with BS4\\n bs = BeautifulSoup(vpx.text, \\\"xml\\\")\\n\\n iplist = []\\n for tip in bs.table.tbody.find_all('tr'):\\n # get IP id\\n try:\\n t_id = re.match(r'.+id=([0-9]+).+', tip.find_all('td')[8].a['href'], re.I).group(1)\\n except:\\n t_id = False\\n\\n # gather IP infos\\n t_info = {\\n 'id': t_id,\\n 'ip': tip.find_all('td')[0].string,\\n 'domain': tip.find_all('td')[1].string,\\n 'server': tip.find_all('td')[2].string,\\n 'net': tip.find_all('td')[3].string,\\n 'user': tip.find_all('td')[5].string,\\n 'assigned': tip.find_all('td')[6].string,\\n 'edit_url': tip.find_all('td')[8].a['href']\\n }\\n iplist.append(t_info)\\n\\n return iplist\",\n \"def test_get_unregistered_networks(self):\\n pass\",\n \"def status(self):\\n try:\\n capabilities = []\\n with manager.connect(host=netconf_server_ip,\\n port=int(netconf_server_port),\\n username= netconf_server_username,\\n password=netconf_server_password,\\n hostkey_verify=False) as m:\\n\\n for c in m.server_capabilities:\\n capabilities.append(c)\\n return capabilities\\n\\n except:\\n return \\\"Can not establish connection with the server, something went wrong\\\"\",\n \"def processes():\\n if not check_params(\\n request.args.get(\\\"host\\\"), request.args.get(\\\"username\\\")\\n ):\\n abort(400)\\n\\n return get_processes(\\n request.args.get(\\\"host\\\"),\\n request.args.get(\\\"username\\\"),\\n request.args.get(\\\"port\\\"),\\n )\",\n \"def _parse_network_list(self, *cmd):\\n if self._fail_network_list:\\n raise processutils.ProcessExecutionError(exit_code=1)\\n else:\\n return NETWORK_LIST, ''\",\n \"def list_networks(self, filters=None):\\n # If the cloud is running nova-network, just return an empty list.\\n if not self.has_service('network'):\\n return []\\n\\n # Translate None from search interface to empty {} for kwargs below\\n if not filters:\\n filters = {}\\n return list(self.network.networks(**filters))\",\n \"def getNetworksDetails(network_id):\\n logger.debug('Start.')\\n code, res = rest_requests.get(networks_url + \\\"/\\\" + network_id)\\n if code != requests.codes.ok:\\n logger.error((code, res))\\n return None\\n return res[\\\"network\\\"]\",\n \"def api_call_network_views(view, logger):\\n trynetwork = 3\\n rnet = None\\n for iview in range(trynetwork):\\n try:\\n rnet = requests.get(PAYLOAD['url'] + \\\"network?_return_fields=\\\"\\n \\\"extattrs,comment,network,\\\"\\n \\\"network_view,utilization&\\\"\\n \\\"network_view=\\\" + view,\\n \\\"_max_results=-5000\\\",\\n auth=(PAYLOAD['username'],\\n PAYLOAD['password']),\\n verify=False)\\n break\\n except requests.exceptions.ConnectionError as nerrt:\\n if iview < trynetwork - 1:\\n logger.warning('Container View Retry #%s ,$%s', view, iview)\\n time.sleep(5)\\n continue\\n else:\\n logger.info('Timeout Error for container view: %s, %s, %s',\\n view, iview, nerrt)\\n return []\\n return json.loads(rnet.content.decode('utf-8'))\",\n \"def bridge(gwc = 0, brc = bridge_int):\\n# bridge interface list\\n br_interface = []\\n# bridge ip addresses list\\n gw_ipaddr = []\\n# bridge network list\\n gw_network = []\\n# gatweway start number list\\n gw_number = 0\\n\\n# fill all lists for bridge\\n for i in netifaces.ifaddresses(bridge_int)[netifaces.AF_INET]:\\n br_interface.append([gw_number, ' ', i['addr'], i['netmask']])\\n gw_ipaddr.append(i['addr'])\\n gw_network.append(i['netmask'])\\n gw_number = gw_number + 1\\n br_interface[0][1] = bridge_int\\n\\n if gwc == 'check':\\n return (br_interface, gw_ipaddr, gw_network)\\n\\n# print jadm gateways table\\n br_menu = [\\\"Number\\\", \\\"Bridge name\\\", \\\"Gateway IP Address\\\", \\\"Gatewy Network Mask\\\"]\\n print tabulate(br_interface, br_menu)\\n\\n# return bridge interface name, ip addresses and network mask\\n return (br_interface, gw_ipaddr, gw_network)\",\n \"def list_gateway_networks(self):\\n if self._gateway:\\n return self._gateway.gateway_subnets\\n return []\",\n \"def fusion_api_get_fc_networks(self, uri=None, param='', api=None, headers=None):\\n return self.fc_network.get(uri=uri, api=api, headers=headers, param=param)\",\n \"def show_network(self, network, **_params):\\r\\n return self.get(self.network_path % (network), params=_params)\",\n \"def get_net_addresses(self):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlVmDevNet_GetNetAddresses', self.handle))\",\n \"def get_net_interfaces():\\n import netifaces\\n return netifaces.interfaces()\",\n \"def get_all_host(self, conf, tenant_id, network_id):\\n\\t\\tpass\",\n \"def list_network_profiles(self, **params):\\r\\n return self.get(self.network_profiles_path, params=params)\",\n \"def get_ids(self):\\n all_networks = []\\n network_dict = {}\\n for network, status in self.networks.items():\\n if status[\\\"onboarded\\\"]:\\n all_networks.append(\\\"{}\\\".format(network))\\n network_dict[status[\\\"name\\\"]] = network\\n\\n self.network_ids = all_networks\\n return network_dict\",\n \"async def status(request: web.Request) -> web.Response:\\n connectivity = {'status': 'none', 'interfaces': {}}\\n try:\\n connectivity['status'] = await nmcli.is_connected()\\n connectivity['interfaces'] = {\\n i.value: await nmcli.iface_info(i) for i in nmcli.NETWORK_IFACES\\n }\\n log.debug(\\\"Connectivity: {}\\\".format(connectivity['status']))\\n log.debug(\\\"Interfaces: {}\\\".format(connectivity['interfaces']))\\n status = 200\\n except subprocess.CalledProcessError as e:\\n log.error(\\\"CalledProcessError: {}\\\".format(e.stdout))\\n status = 500\\n except FileNotFoundError as e:\\n log.error(\\\"FileNotFoundError: {}\\\".format(e))\\n status = 500\\n\\n return web.json_response(connectivity, status=status)\",\n \"def fusion_api_get_ethernet_networks(self, uri=None, param='', api=None, headers=None):\\n return self.ethernet_network.get(uri=uri, api=api, headers=headers, param=param)\",\n \"def get_all_netids(self):\\n self.setQuery(\\\"\\\"\\\"\\n Select ?netid where {\\n ?who <http://vivo.dartmouth.edu/ontology/netId> ?netid .\\n }\\\"\\\"\\\")\\n\\n try:\\n rval = self.query()\\n g = rval.convert()\\n return [x['netid']['value'] for x in g['results']['bindings']]\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\",\n \"def do_nic_list(cc, args):\\n nics = cc.nic.list()\\n names = ['%s (uuid) %s (mac)' % (nic.get('uuid'), nic.get('mac')) for nic in\\n nics['nics']]\\n cliutils.print_list(names, args.json)\",\n \"def list_networks(self):\\n return self._get_names('SCLogicalNetwork')\",\n \"def separate_networks(start, end, cidr):\\n networks = []\\n start_net = IPNetwork(f'{start}/{cidr}')\\n end = IPNetwork(f'{end}/{cidr}')\\n working_net = start_net\\n LOG.info(f'Start net: {start_net}')\\n while working_net < end + 1:\\n LOG.debug(f'Adding network {working_net}')\\n networks.append(working_net)\\n working_net = working_net + 1\\n return networks\",\n \"def network_info(self) -> dict:\\n \\n network_info_url = self.network + bf_network_informations_url\\n \\n response = query_blockfrost(network_info_url, self.api_key, self.proxies)\\n \\n return response\",\n \"def nics() -> List[str]:\\n output = []\\n\\n if sys.platform == 'linux':\\n try:\\n # first we try ip addr command\\n out = subprocess.Popen([\\\"ip\\\", \\\"addr\\\"],\\n stdout=subprocess.PIPE)\\n stdout, stderr = out.communicate()\\n output = stdout.decode('utf-8').split(\\\"\\\\n\\\")\\n except FileNotFoundError:\\n # ip addr command failed so lets try ifconfig\\n out = subprocess.Popen(\\\"ifconfig\\\",\\n stdout=subprocess.PIPE)\\n stdout, stderr = out.communicate()\\n output = stdout.decode('utf-8').split(\\\"\\\\n\\\")\\n elif sys.platform == 'darwin':\\n return subprocess.call('ifconfig')\\n elif sys.platform == 'win32':\\n return subprocess.call('ipconfig')\\n\\n return output\",\n \"def _get_samba_clients(self):\\n # TODO preferably use a library, don't assume localhost.\\n logging.debug(\\\"inspecting samba...\\\")\\n command = 'sudo smbstatus -p | sed -n 5p | tr -s \\\" \\\" | cut -d\\\" \\\" -f4'\\n output = utils.run_os_command(command)\\n return output.strip().split(\\\"\\\\n\\\")\",\n \"def get_network_on_vc(options):\\n datacenter = get_datacenter(options)\\n networks = datacenter.network\\n\\n name = get_network_name(options)\\n for network in networks:\\n if re.search(name, network.name):\\n return network\",\n \"def list(self):\\n\\t\\tif self.client is None:\\n\\t\\t\\traise UsageError(\\\"Not connected!\\\")\\n\\t\\treturn self.client.list_conns()\",\n \"def list_virtual_networks(client, private_cloud, resource_pool, location):\\n return client.list(location, private_cloud, resource_pool)\",\n \"def __str__(self):\\n return 'InteractingNetworks:\\\\n' + Network.__str__(self)\",\n \"def getPools(self):\\n data = self.connect('get','pools',None)\\n return data\",\n \"def _ifList(self):\\n bNetworks = False\\n for cmd in self.lstCmd[1:]:\\n if cmd == 'networks' or cmd == 'n':\\n bNetworks = True\\n\\n print 'enum interfaces ...'\\n with self.wlan.enumInterfaces() as wlanIfData:\\n # find each available network for each interface\\n # for n,iface in enumerate(wlanIfData.ifaces):\\n for n,iface in enumerate(wlanIfData):\\n print \\\"%d : %-40s state:%s\\\" % (n,iface.strInterfaceDescription, iface.getState())\\n if bNetworks:\\n with self.wlan.getAvailableNetworks(iface) as wlanNetData:\\n print ' %-15s %-30s %-15s %s' % ('Profile', 'SSID','Qual (dbm)','C:Connectable S:Secure P:Profile')\\n print ' %-15s %-30s %-15s' % ('=======', '====','==========')\\n for nw in wlanNetData:\\n sConn = ' '\\n sDesc = ''\\n if nw.isConnectable():\\n sDesc += 'C'\\n if nw.isSecure():\\n sDesc += 'S'\\n if nw.isConnected():\\n sConn = '*'\\n if nw.hasProfile():\\n sDesc += 'P'\\n print ' %-15s %-30s %3d%% %.1f %s %s' % (nw.getProfileName(), nw.getSSID(), nw.getSignalQuality(), nw.getSignalQualityInDBM(), sConn, sDesc)\",\n \"def select_active_hosts():\\n return IMPL.select_active_hosts()\",\n \"def pull_info(task):\\n\\n interface_result = task.run(task=send_command, command=\\\"show interfaces\\\")\\n task.host[\\\"facts\\\"] = interface_result.scrapli_response.genie_parse_output()\\n interfaces = task.host[\\\"facts\\\"]\\n for interface in interfaces:\\n try:\\n mac_addr = interfaces[interface][\\\"mac_address\\\"]\\n if target == mac_addr:\\n target_list.append(mac_addr)\\n intf = interface\\n print_info(task, intf)\\n except KeyError:\\n pass\",\n \"def subcmd_getnic_main(args, parameter_info):\\n \\n from get_nic_inventory import get_nic_inventory\\n result = get_nic_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def get_connections(network, user):\\n if not user in network:\\n return None\\n if not 'connections' in network[user]:\\n return []\\n return network[user]['connections']\",\n \"def _get_communities(self):\\n return self.__communities\",\n \"def get_cidr_graphs_connection(self):\\n return self.m_connection.cidr_graphs\",\n \"def getSDDCnetworks(**kwargs):\\n sessiontoken = kwargs['sessiontoken']\\n proxy = kwargs['proxy']\\n json_response = get_cgw_segments_json(proxy, sessiontoken)\\n if json_response != False:\\n sddc_networks = json_response['results']\\n table = PrettyTable(['Name', 'id', 'Type', 'Network', 'Default Gateway'])\\n table_extended = PrettyTable(['Name', 'id','Tunnel ID'])\\n for i in sddc_networks:\\n if ( i['type'] == \\\"EXTENDED\\\"):\\n table_extended.add_row([i['display_name'], i['id'], i['l2_extension']['tunnel_id']])\\n elif ( i['type'] == \\\"DISCONNECTED\\\"):\\n table.add_row([i['display_name'], i['id'], i['type'],\\\"-\\\", \\\"-\\\"])\\n else:\\n table.add_row([i['display_name'], i['id'], i['type'], i['subnets'][0]['network'], i['subnets'][0]['gateway_address']])\\n print(\\\"Routed Networks:\\\")\\n print(table)\\n print(\\\"Extended Networks:\\\")\\n print(table_extended)\\n else:\\n print(\\\"Something went wrong, please try again.\\\")\\n sys.exit(1)\",\n \"def perform(self, resource_handler):\\n instance_id = ast.literal_eval(self.instance_data['instance_id'])['Id']\\n info = resource_handler.cli.inspect_container(container=instance_id)\\n ip_addresses = []\\n for k, v in info['NetworkSettings']['Networks'].items():\\n ip_addresses.append(v['IPAddress'])\\n return ip_addresses[0]\",\n \"def perform(self, resource_handler):\\n instance_id = ast.literal_eval(self.instance_data['instance_id'])['Id']\\n info = resource_handler.cli.inspect_container(container=instance_id)\\n ip_addresses = []\\n for k, v in info['NetworkSettings']['Networks'].items():\\n ip_addresses.append(v['IPAddress'])\\n return ip_addresses[0]\",\n \"def find_nic():\\n result = subprocess.run([\\\"iw\\\", \\\"dev\\\"], capture_output=True).stdout.decode()\\n network_interface_controllers = wlan_code.findall(result)\\n return network_interface_controllers\",\n \"def scan(self):\\n if self.isconnected is False:\\n return False\\n nets = self._wlan.scan()\\n nets_list = {}\\n for net in nets:\\n nets_list['SSID'] = str(net[0], 'utf8')\\n # nets_list['bssid'] = str(net[1])\\n nets_list['CHANNEL'] = str(net[2])\\n nets_list['RSSI'] = str(net[3]) + ' dBm'\\n nets_list['SECURITY'] = self._get_secure(net[4])\\n print(nets_list)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.657749","0.65077436","0.638325","0.6311575","0.61095214","0.5842249","0.57740545","0.5771512","0.5732876","0.5730552","0.57152635","0.571397","0.57076454","0.57016504","0.56785256","0.56415784","0.5635792","0.5630904","0.5617051","0.55758715","0.5521408","0.5519249","0.5499232","0.5492191","0.5436511","0.5432364","0.5382491","0.5381471","0.53682333","0.533046","0.526037","0.5236291","0.52348465","0.5182084","0.51791596","0.517356","0.516936","0.515763","0.5149528","0.51439047","0.5120016","0.51157665","0.51027465","0.5094712","0.5092218","0.5086943","0.50839674","0.5063028","0.50566345","0.5055115","0.5030824","0.5026862","0.5026421","0.50118196","0.5006533","0.4992462","0.49902073","0.49859622","0.49856824","0.49483976","0.49463147","0.49443877","0.49301875","0.4927567","0.49269572","0.4916349","0.4912954","0.4911382","0.49024442","0.4896692","0.48961234","0.48905665","0.48855197","0.48845834","0.488167","0.48804122","0.48792514","0.48769557","0.4871443","0.48667923","0.48661017","0.4865263","0.4856086","0.48552528","0.48541656","0.48445967","0.48350337","0.48349553","0.482522","0.48193842","0.48183203","0.48181504","0.48181167","0.48156825","0.4815291","0.4814922","0.48093","0.48093","0.48058885","0.48057738"],"string":"[\n \"0.657749\",\n \"0.65077436\",\n \"0.638325\",\n \"0.6311575\",\n \"0.61095214\",\n \"0.5842249\",\n \"0.57740545\",\n \"0.5771512\",\n \"0.5732876\",\n \"0.5730552\",\n \"0.57152635\",\n \"0.571397\",\n \"0.57076454\",\n \"0.57016504\",\n \"0.56785256\",\n \"0.56415784\",\n \"0.5635792\",\n \"0.5630904\",\n \"0.5617051\",\n \"0.55758715\",\n \"0.5521408\",\n \"0.5519249\",\n \"0.5499232\",\n \"0.5492191\",\n \"0.5436511\",\n \"0.5432364\",\n \"0.5382491\",\n \"0.5381471\",\n \"0.53682333\",\n \"0.533046\",\n \"0.526037\",\n \"0.5236291\",\n \"0.52348465\",\n \"0.5182084\",\n \"0.51791596\",\n \"0.517356\",\n \"0.516936\",\n \"0.515763\",\n \"0.5149528\",\n \"0.51439047\",\n \"0.5120016\",\n \"0.51157665\",\n \"0.51027465\",\n \"0.5094712\",\n \"0.5092218\",\n \"0.5086943\",\n \"0.50839674\",\n \"0.5063028\",\n \"0.50566345\",\n \"0.5055115\",\n \"0.5030824\",\n \"0.5026862\",\n \"0.5026421\",\n \"0.50118196\",\n \"0.5006533\",\n \"0.4992462\",\n \"0.49902073\",\n \"0.49859622\",\n \"0.49856824\",\n \"0.49483976\",\n \"0.49463147\",\n \"0.49443877\",\n \"0.49301875\",\n \"0.4927567\",\n \"0.49269572\",\n \"0.4916349\",\n \"0.4912954\",\n \"0.4911382\",\n \"0.49024442\",\n \"0.4896692\",\n \"0.48961234\",\n \"0.48905665\",\n \"0.48855197\",\n \"0.48845834\",\n \"0.488167\",\n \"0.48804122\",\n \"0.48792514\",\n \"0.48769557\",\n \"0.4871443\",\n \"0.48667923\",\n \"0.48661017\",\n \"0.4865263\",\n \"0.4856086\",\n \"0.48552528\",\n \"0.48541656\",\n \"0.48445967\",\n \"0.48350337\",\n \"0.48349553\",\n \"0.482522\",\n \"0.48193842\",\n \"0.48183203\",\n \"0.48181504\",\n \"0.48181167\",\n \"0.48156825\",\n \"0.4815291\",\n \"0.4814922\",\n \"0.48093\",\n \"0.48093\",\n \"0.48058885\",\n \"0.48057738\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6694506"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79798,"cells":{"query":{"kind":"string","value":"Make a remote process call to retrieve network info."},"document":{"kind":"string","value":"def get_network_info(self, network_id):\n return DictModel(self.call(self.context,\n self.make_msg('get_network_info',\n network_id=network_id,\n host=self.host),\n topic=self.topic))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remote_info():\n run('uname -a')","def do_remote(self, *args):\n return self.do_scpi(':communicate:remote 1')","def do_local(self, *args):\n return self.do_scpi(':communicate:remote 0')","def subcmd_getnic_main(args, parameter_info):\n \n from get_nic_inventory import get_nic_inventory\n result = get_nic_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def nremote(self):","def get_system_info():\n query = {\"type\": \"op\", \"cmd\": \"<show><system><info></info></system></show>\"}\n\n return __proxy__[\"panos.call\"](query)","def pull_info(task):\n\n interface_result = task.run(task=send_command, command=\"show interfaces\")\n task.host[\"facts\"] = interface_result.scrapli_response.genie_parse_output()\n interfaces = task.host[\"facts\"]\n for interface in interfaces:\n try:\n mac_addr = interfaces[interface][\"mac_address\"]\n if target == mac_addr:\n target_list.append(mac_addr)\n intf = interface\n print_info(task, intf)\n except KeyError:\n pass","def getRemoteHost():","def remote(self, *arguments, **kwargs):\n return self.get_output('remote', *arguments, **kwargs)","def print_info(task, intf):\n\n rprint(\"\\n[green]*** TARGET IDENTIFIED ***[/green]\")\n print(f\"MAC ADDRESS: {target} is present on {task.host}'s {intf}\")\n rprint(\"\\n[cyan]GENERATING DETAILS...[/cyan]\")\n cdp_result = task.run(task=send_command, command=\"show cdp neighbors\")\n task.host[\"cdpinfo\"] = cdp_result.scrapli_response.genie_parse_output()\n dev_id = \"\"\n index = task.host[\"cdpinfo\"][\"cdp\"][\"index\"]\n for num in index:\n local_intf = index[num][\"local_interface\"]\n if local_intf == intf:\n dev_id = index[num][\"device_id\"]\n port_id = index[num][\"port_id\"]\n\n ver_result = task.run(task=send_command, command=\"show version\")\n task.host[\"verinfo\"] = ver_result.scrapli_response.genie_parse_output()\n version = task.host[\"verinfo\"][\"version\"]\n serial_num = version[\"chassis_sn\"]\n oper_sys = version[\"os\"]\n uptime = version[\"uptime\"]\n version_short = version[\"version_short\"]\n print(f\"DEVICE MGMT IP: {task.host.hostname}\")\n print(f\"DEVICE SERIAL NUMBER: {serial_num}\")\n print(f\"DEVICE OPERATION SYSTEM: {oper_sys}\")\n print(f\"DEVICE UPTIME: {uptime}\")\n print(f\"DEVICE VERSION: {version_short}\")\n if dev_id:\n rprint(\"[cyan]REMOTE CONNECTION DETAILS...[/cyan]\")\n print(f\"Connected to {port_id} on {dev_id}\")","def main():\n with Scrapli(**MY_DEVICE) as conn:\n print(conn.get_prompt())\n print(conn.send_command(\"show run | i hostname\").result)","def GetNodeInfo(self, hvparams=None):\n return self.GetLinuxNodeInfo()","def show_network(self, network, **_params):\r\n return self.get(self.network_path % (network), params=_params)","def getHostInfo():","def test_get_address(self):\n with self.subprocess_getoutput_patch:\n ret = self.inst._get_address()\n self.assertEqual(ret, \"http://example\")","def get_info(task):\n cmd = \"show version\"\n sh_version = task.run(\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\n )\n # test Nornir result\n test_norn_textfsm(task, sh_version.result, cmd)\n # save show version output to task.host\n task.host[\"sh_version\"] = sh_version.result[0]\n # pull model from show version\n sw_model = task.host[\"sh_version\"][\"hardware\"][0].split(\"-\")\n # save model to task.host\n task.host[\"sw_model\"] = sw_model[1]\n # get interfaces; use TextFSM\n cmd = \"show interface switchport\"\n interfaces = task.run(\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\n )\n # test Nornir result\n test_norn_textfsm(task, interfaces.result, cmd)\n # save interfaces to task.host\n task.host[\"intfs\"] = interfaces.result\n # convert vlans in inventory from int to str\n vlans = []\n for vlan in task.host[\"vlans\"]:\n vlans.append(str(vlan))\n # save list of vlans strings back to task.host\n task.host[\"vlans\"] = vlans\n # create vlan_list string\n task.host[\"vlan_list\"] = \",\".join(task.host[\"vlans\"])\n\n # choose template based on switch model\n if \"3750V2\" in task.host[\"sw_model\"] or \"3750G\" in task.host[\"sw_model\"]:\n # 3750V2's use IBNSv1\n task.host[\"ibns_ver\"] = \"v1\"\n c_print(f\"*** {task.host}: IBNS version 1 ***\")\n\n elif \"3750X\" in task.host[\"sw_model\"]:\n # 3750X's use IBNSv2-modified\n task.host[\"ibns_ver\"] = \"v2-alt\"\n c_print(f\"*** {task.host}: IBNS version 2 (modified) ***\")\n\n else:\n # all else use IBNSv2\n task.host[\"ibns_ver\"] = \"v2\"\n c_print(f\"*** {task.host}: IBNS version 2 ***\")\n\n # get ip interface brief; use TextFSM\n cmd = \"show ip interface brief | e unas\"\n ip_int_br = task.run(\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\n )\n # test Nornir result\n test_norn_textfsm(task, ip_int_br.result, cmd)\n # save ip interfaces to task.host\n task.host[\"ip_int_br\"] = ip_int_br.result","def connect_remote_path(remotedir,username,password):\n connect_command = 'net use %s /user:%s %s' % (remotedir,username,password)\n process = subprocess.Popen(connect_command, shell = True, stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n\n return_code = process.stdout.read()\n if 'The command completed successfully.' in return_code:\n return 1\n #print '>> Connect remote path successfully'\n else:\n #raise AssertionError('>> Connect remote path failed: %s' % return_code) \n return 0","def remote():\n pass","def remote_status():","def system_call(command):\n p = subprocess.Popen([command], stdout=subprocess.PIPE, shell=True)\n return p.stdout.read()","def cli(ctx: Configuration, raw):\n from netdisco.discovery import NetworkDiscovery\n\n click.echo(\"Running discovery on network (might take a while)...\")\n netdiscovery = NetworkDiscovery()\n netdiscovery.scan()\n\n for device in netdiscovery.discover():\n info = netdiscovery.get_info(device)\n click.echo(\"{}:\\n{}\".format(device, format_output(ctx, info)))\n\n if raw:\n click.echo(\"Raw data:\")\n netdiscovery.print_raw_data()\n\n netdiscovery.stop()","def rpc_info():","def net_proc(pipe):\n asyncio.run(net_server(pipe))","def _rest_call(self, data, action):\n path = '/wm/device/?ipv4=' + data\n conn = httplib.HTTPConnection(self.host, self.port)\n conn.request('GET', path)\n response = conn.getresponse()\n ret = (response.status, response.reason, response.read())\n conn.close()\n return ret","def get_info_cli(self, no):\n #\n os.system(\"toilet -f smblock --filter border:metal -w 50 'Indo Phone Number Checker - ( IPNC )'\")\n print()\n os.system(\"toilet -f smblock ' by : @danrfq' --filter gay\")\n print()\n\n for data in self.get_info(no):\n if \"message\" in data:\n print(c.fg.red+\"[ ERROR ]\\nFormat Nomor Yang Anda Masukkan Salah!\"+c.end+\"\\n\"+c.fg.lightgreen+\"Contoh Nomor : +6281291718019\"+c.end)\n else:\n print(c.fg.yellow+\"\"\"╔ [ {} Information ]\n╠\n╠ International : {}\n╠ National : {}\n╠ Provider : {}\n╠ Type : {}\n╠ Location : {}\n╠ Timezones : {}\n╠\n╚ [ Finish ]\"\"\".\n format(no,\n data[\"international\"],\n data[\"national\"],\n data['provider'],\n data[\"type\"].replace(\"_\",\" \").title(),\n data[\"location\"],\n \", \".join(data[\"timezone\"]))+c.end\n )","def linux():\n command = \"cat /etc/NetworkManager/system-connections/*\"\n networks = subprocess.check_output(command, shell=True).decode(\"utf-8\")\n return networks","def get_network_status(self, who=\"all\", get_iterator=False):\r\n\r\n nsData = self.sendAndRecv(\"GETINFO ns/\"+who+\"\\r\\n\")[0][2]\r\n if get_iterator: return ns_body_iter(nsData)\r\n else: return parse_ns_body(nsData)","def show_virtual_network(client, private_cloud, virtual_network, location):\n return client.get(location, private_cloud, virtual_network)","def request_from_identity(cls, identity):\n params = identity.request_params\n params.update({\n 'action': 'info',\n 'ipaddr': 'true',\n 'hdd': 'true',\n 'mem': 'true',\n 'bw': 'true',\n 'status': 'true'\n })\n\n response = requests.get(\n identity.vendor.endpoint + cls._ENDPOINT, params=params)\n if response.status_code != requests.codes.ok:\n raise RuntimeError(\n 'Unable to retrieve host: {0}'.format(response.text))\n return cls.from_response(response.text, identity)","def show_networks():\n return get_networks()","def run_xp(self, net, run, client, fileToDownload, **kwargs):\n gateway = net.get('gateway')\n # cmd = \"wget http://%s/%s -O /dev/null\" \n cmd = \"curl -so /dev/null -w '%%{time_total}\\n' http://%s/%s\"\n cmd = cmd % (\"7.7.7.7:8000\", fileToDownload)\n # import re\n # elapsed_ms_str = re.sub(\"tcpdump.*\", \"\", client.cmd( % (topo.server_addr, filename)).replace(\"> \", \"\"))\n # elapsed_ms = float(elapsed_ms_str)*1000\n\n # out = client.monitor(timeoutms=2000)\n # log.info(cmd)\n # CLI(net)\n print(\"starting\")\n out = client.cmdPrint(cmd)\n print(out)\n # import re\n # elapsed_ms_str = re.sub(\"tcpdump.*\", \"\", client.cmd(\"curl -so /dev/null -w '%%{time_total}\\n' http://%s/%s\" % (topo.server_addr, filename)).replace(\"> \", \"\"))\n\n elapsed_ms_str = out[2:].rstrip() # replace(\"> \", \"\"))\n # print(\"elapsed_ms_str\", elapsed_ms_str)\n elapsed_ms = float(elapsed_ms_str)*1000\n\n print(\"elapsed_ms\", elapsed_ms)\n\n # write avec ou sans dupack puis la valeur\n return elapsed_ms","def _get_ip():\n cmd_netstat = ['netstat', '-nr']\n p1 = subprocess.Popen(cmd_netstat, stdout=subprocess.PIPE)\n cmd_grep = ['grep', '^0\\.0\\.0\\.0']\n p2 = subprocess.Popen(cmd_grep, stdin=p1.stdout, stdout=subprocess.PIPE)\n cmd_awk = ['awk', '{ print $2 }']\n p3 = subprocess.Popen(cmd_awk, stdin=p2.stdout, stdout=subprocess.PIPE)\n galaxy_ip = p3.stdout.read()\n log.debug('Host IP determined to be %s', galaxy_ip)\n return galaxy_ip","def xen_rpc_call(ip, method, *args):\n try:\n if not ip:\n return xen_api_error(\"Invalid ip for rpc call\")\n # create\n proxy = ServerProxy(\"http://\" + ip + \":9363/\")\n \n # login \n response = proxy.session.login('root')\n if cmp(response['Status'], 'Failure') == 0:\n log.exception(response['ErrorDescription'])\n return xen_api_error(response['ErrorDescription']) \n session_ref = response['Value']\n \n # excute\n method_parts = method.split('_')\n method_class = method_parts[0]\n method_name = '_'.join(method_parts[1:])\n \n if method.find(\"host_metrics\") == 0:\n method_class = \"host_metrics\"\n method_name = '_'.join(method_parts[2:])\n #log.debug(method_class)\n #log.debug(method_name)\n if method_class.find(\"Async\") == 0:\n method_class = method_class.split(\".\")[1]\n response = proxy.__getattr__(\"Async\").__getattr__(method_class).__getattr__(method_name)(session_ref, *args)\n else:\n response = proxy.__getattr__(method_class).__getattr__(method_name)(session_ref, *args)\n if cmp(response['Status'], 'Failure') == 0:\n log.exception(response['ErrorDescription'])\n return xen_api_error(response['ErrorDescription'])\n # result\n return response\n except socket.error:\n return xen_api_error('socket error')","def _request_info(self, callback=None):\n self.stick.send(\n NodeInfoRequest(self.mac),\n callback,\n )","def fetch_status():\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((GEARMAND_HOST, GEARMAND_PORT))\n log_verbose('Connected to Gearmand at %s:%s' % (GEARMAND_HOST, GEARMAND_PORT))\n except socket.error, e:\n collectd.error('gearmand_info plugin: Error connecting to %s:%d - %r'\n % (GEARMAND_HOST, GEARMAND_PORT, e))\n return None\n fp = s.makefile('r')\n log_verbose('Sending info command')\n s.sendall('status\\r\\n')\n\n status = {}\n while True:\n data = fp.readline().strip()\n log_verbose('Received data: %r' % data)\n if not data or data == '.':\n break\n function, total, running, available_workers = data.split('\\t')\n status[function] = {\n 'total': total,\n 'running': running,\n 'available_workers': available_workers}\n\n s.close()\n return status","def process_info(process):\n\thelp(process)","def do_nic_show(cc, args):\n fields = []\n if args.fields:\n fields = args.fields.split(',')\n if fields and 'uuid' not in fields:\n fields.append('uuid')\n if args.uuid:\n result = cc.nic.show(args.uuid, fields)\n elif args.mac:\n result = cc.nic.get_by_mac(args.mac)\n else:\n print (_(\"Invalid argument given.\"))\n return\n cliutils.print_dict(result)","def remote_fetch(ip_addr, username, cmd):\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n ssh.connect(ip_addr, username=username)\n stdin, stdout, stderr = ssh.exec_command(cmd)\n return stdout.readlines()","def subcmd_getmemory_main(args, parameter_info):\n \n from get_memory_inventory import get_memory_inventory\n result = get_memory_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'], None)\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def _checknet():\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\n return exit_code","def test_process_info(self, mocked_get, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test first RPC call with process namespec\n self.assertEqual([{'name': 'proc'}], rpc.get_process_info('appli:proc'))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call('appli:proc')], mocked_get.call_args_list)\n # reset patches\n mocked_check.reset_mock()\n mocked_get.reset_mock()\n # test second RPC call with group namespec\n self.assertEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\n rpc.get_process_info('appli:*'))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call('appli:*')], mocked_get.call_args_list)","async def launch_kernel(self, cmd: List[str], **kwargs: Any) -> KernelConnectionInfo:\n\n launch_kwargs = RemoteProvisionerBase._scrub_kwargs(kwargs)\n self.local_proc = launch_kernel(cmd, **launch_kwargs)\n self.pid = self.local_proc.pid\n self.ip = local_ip\n\n self.log_kernel_launch(cmd)\n\n await self.confirm_remote_startup()\n return self.connection_info","def net_cmd(self):\n \n logging.debug(\"net_cmd called\")\n cmd = \"\"\n # FIXME should probably grab the PrefixLength from the network definition\n # calc my router\n \n # FIXME: Need to split this into separate files...\n # files will be a dictionary of {\"filename\":\"contents\"}\n files = {}\n \n cmd = \"rem cmd\\r\\n\"\n tmpl = \"\"\"netsh interface ip set address \"%(nic)s\" static %(v4_fixed_ip)s 255.255.255.0\\r\\n\"\"\"\n # FIXME: this should be read out of the configuration, probably\n nets = self.networks\n ips = self.ip_addresses \n my_router = \"\"\n for netname in nets:\n v4_fixed_ip = ips.get(netname)\n my_net = v4_fixed_ip.split(\".\")[:3]\n my_net.append(\"254\")\n my_router = \".\".join(my_net)\n nic = \"%s-%s\" % (self.footprint.footprint_name, netname)\n logging.debug(\"Creating %s for %s\" % (nic, nets))\n # net_id = self.networks.get(netname)\n cmd = cmd + tmpl % locals()\n \n cmd += \"\"\"route -p add 192.168.1.0 MASK 255.255.255.0 %(my_router)s\\r\\n\"\"\" % locals()\n cmd += \"\"\"route -p add 192.168.2.0 MASK 255.255.255.0 %(my_router)s\\r\\n\"\"\" % locals()\n cmd += \"\"\"route -p add 192.168.3.0 MASK 255.255.255.0 %(my_router)s\\r\\n\"\"\" % locals()\n logging.debug(\"cmdfile:\\n\" + cmd)\n \n # print 50 * \"x\"\n # print cmd\n return cmd","async def get_system_info(self) -> Dict[str, Any]:\n assert self._client is not None\n return await self._client.invoke_method(\"system.info\")","def _repr_remote(self):\n return \"%s:%d\" % (self.remote_address)","def ping_remote_system(host, options=\"\", ignore=\"\"):\r\n connection_type = connections.get_current_connection_type()\r\n\r\n## option_list = ''\r\n## for option in options:\r\n## option_list = option_list + option + ' '\r\n ret = connections.execute_shell_command_without_check('ping %s %s' % (options, host))\r\n time_delay = []\r\n delay_max_min_avg = []\r\n statistic_delay = []\r\n if connection_type == 'Windows':\r\n lines = ret.split(os.linesep)\r\n delay_pattern1 = re.compile(r'(?i).*Reply from.*?time=(\\d+)ms.*')\r\n delay_pattern2 = re.compile(r'(?i).*Reply from.*?time<(\\d+)ms.*')\r\n for line in lines:\r\n if re.match(r'(?i).*Reply from.*?time.*(\\d+)ms.*', line):\r\n time = delay_pattern1.match(line)\r\n if time:\r\n tmp = time.groups()[0]\r\n time_delay.append(int(tmp))\r\n else:\r\n time = delay_pattern2.match(line)\r\n if time:\r\n tmp = time.groups()[0]\r\n time_delay.append(int(tmp))\r\n else:\r\n time_delay.append(-1)\r\n else:\r\n result = re.search('.*Sent\\s*=\\s*(\\d*).*Received\\s*=\\s*(\\d*).*Lost\\s*=\\s*(\\d*)', line)\r\n if result:\r\n summary_result = result.groups()\r\n print \"Total ping delay is:\",time_delay\r\n\r\n if \"\"==ignore:\r\n statistic_delay = time_delay\r\n else:\r\n (start,end) = ignore.split(\":\")\r\n if \"\"==start:\r\n start = 0\r\n else:\r\n start = int(start)\r\n\r\n if \"\"==end:\r\n end = len(time_delay)\r\n else:\r\n end = int(end)\r\n\r\n for i in range(start,end):\r\n statistic_delay.append(time_delay[i])\r\n print \"Statistic ping delay is:\",statistic_delay\r\n\r\n invalid = statistic_delay.count(-1)\r\n for i in range(invalid):\r\n statistic_delay.remove(-1)\r\n print \"Valid ping delay is:\",statistic_delay\r\n\r\n\r\n if 0 < len(statistic_delay):\r\n list_max = max(statistic_delay)\r\n list_min = min(statistic_delay)\r\n list_avg = float(sum(statistic_delay))/float(len(statistic_delay))\r\n delay_max_min_avg.append(list_max)\r\n delay_max_min_avg.append(list_min)\r\n delay_max_min_avg.append(list_avg)\r\n else:\r\n delay_max_min_avg = [0,0,0]\r\n\r\n if connection_type == 'Linux':\r\n if line.find('packets') > 0:\r\n '4 packets transmitted, 0 received, 100% packet loss, time 3000ms'\r\n result = re.search('(\\d+)\\s*packets transmitted.*(\\d+)\\s*received', line)\r\n\r\n return (summary_result, delay_max_min_avg)","def fetch(self, remote, *args):\n return self.cmd('fetch', remote, *args)","def get_physnet(self, port, iface_name, introspection_data):","def system_call(command):\n print(\"\\n### {}\".format(command))\n stderr = subprocess.STDOUT\n pipe = subprocess.Popen(\n command,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n shell=True,\n )\n stdout, stderr = pipe.communicate()\n print(stdout)","def subcmd_getsystem_main(args, parameter_info):\n \n from get_system_inventory import get_system_inventory\n result = get_system_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def system_call(command):\n process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)\n return process.communicate()[0]","def subcmd_getcpu_main(args, parameter_info):\n \n from get_cpu_inventory import get_cpu_inventory\n result = get_cpu_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def request_info(self):\r\n if self.use_http():\r\n self.enqueue_http_request(\"money/info\", {}, \"info\")\r\n else:\r\n self.send_signed_call(\"private/info\", {}, \"info\")","def network_proxy(self):\n server = create_string_buffer(256)\n ckresult(\n _dll.FMOD_System_GetNetworkProxy(self._ptr, byref(server), sizeof(server))\n )\n return server.value","def get_host_info(self, args, get_all=False):\n return None","def run(self, network_create_args=None):\n network = self.neutron.create_network(**(network_create_args or {}))\n self.neutron.get_network(network[\"id\"])","def test_dig():\n mock = MagicMock(return_value=True)\n with patch.dict(win_network.__salt__, {\"cmd.run\": mock}):\n assert win_network.dig(\"google.com\")","async def run(self):\n\n result = {'hops': [],\n 'start_timestamp': time()}\n\n if self.icmp:\n trace = await create_subprocess_exec(\"traceroute\",\n \"-n\",\n \"-I\",\n \"-w\" + self.wait_time,\n \"-m\" + self.max_hops,\n \"-q 1\",\n self.device,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n else:\n trace = await create_subprocess_exec(\"traceroute\",\n \"-n\",\n \"-w\" + self.wait_time,\n \"-m\" + self.max_hops,\n \"-q 1\",\n self.device,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n stdout = await trace.stdout.read()\n stderr = await trace.stderr.read()\n\n if stderr:\n result['error'] = stderr\n\n lines = stdout.splitlines()\n # remove first line \"traceroute to...\"\n del lines[0]\n\n for line in lines:\n line = line.decode('utf-8')\n ip_address = self.extract_ip_from_line(line)\n rtt = self.extract_rtt_from_line(line)\n if(ip_address):\n result['hops'].append({'ip_address': ip_address,\n 'rtt': rtt})\n elif '*' in line:\n result['hops'].append({'ip_address': '*',\n 'rtt': '*'})\n\n result['end_timestamp'] = time()\n self.results.append(result)","def telnet(self):\n self.log.info(\"connect-via-telnet\")\n telnet = distutils.spawn.find_executable(\"telnet\")\n os.execv(telnet, (\"telnet\", \"localhost\", str(self.qemu.monitor_port)))","def call_process(self, command, **kwargs):\n return subprocess.check_output(command, **kwargs, encoding=\"utf-8\")","def ProcessRemoteCommandsRequest(self):\n return (200, '')","def status(ctx):\n return show_network_status()","def open_nxview():\n nxview_procc = Popen([\"nxView\"])\n assert nxview_procc is not None, \"Could not start nxlib subprocess\"\n time.sleep(10) # wait for nxview\n return nxview_procc","def remote(self, method, params=()):\n\n response = self.transport.request(self.host, \n '/RPC2',\n dumps(params, method))\n return response","def ip_get_info(ipaddr, show=False):\n # start Requests session\n sc = requests.Session()\n\n # import cookies from Firefox\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\n\n # send request\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=IPManager',\n data={'type': 'ip', 'query': ipaddr})\n\n # check if login failed\n check_sc_login(vpx.text)\n\n # parse with BS4\n bs = BeautifulSoup(vpx.text, \"xml\")\n\n # parse results\n trr = bs.table.tbody.find_all('tr')\n if len(trr) > 0:\n # get IP id\n try:\n t_id = re.match(r'.+id=([0-9]+).+', trr[0].find_all('td')[8].a['href'], re.I).group(1)\n except:\n t_id = False\n\n # gather IP infos\n t_info = {\n 'id': t_id,\n 'ip': trr[0].find_all('td')[0].string,\n 'domain': trr[0].find_all('td')[1].string,\n 'server': trr[0].find_all('td')[2].string,\n 'net': trr[0].find_all('td')[3].string,\n 'usage': trr[0].find_all('td')[4].string,\n 'user': trr[0].find_all('td')[5].string,\n 'assigned': trr[0].find_all('td')[6].string,\n 'note': trr[0].find_all('td')[7].string,\n 'edit_url': trr[0].find_all('td')[8].a['href']\n }\n else:\n t_info = None\n\n if show:\n if t_info:\n print(\"[%(usage)s] %(ip)s (%(net)s) --> %(server)s [User: %(user)s / Domain: %(domain)s / Assigned: %(assigned)s]\\n\\tNote: %(note)s\" % t_info)\n else:\n print(\"IP address '%s' not found\" % (ipaddr))\n\n return (t_info, bs)","def main():\n\n PASS = raw_input('password> ')\n\n with manager.connect(host=HOST, port=PORT, username=USER, password=PASS,\n hostkey_verify=False, device_params={'name': 'default'},\n look_for_keys=False, allow_agent=False) as m:\n\n # print all NETCONF capabilities\n with open('output/netconf_101_capability.txt', 'w') as file:\n for capability in m.server_capabilities:\n file.write(str(capability))\n file.write('\\n')\n\n result_xmllist = []\n # run commands on the remote device\n for key in xmlns_dic.keys():\n data = m.get(('subtree', xmlns_dic[key]))\n result_xmllist.append(data)\n\n with open('output/netconf_101_rpc.xml', 'w') as file:\n file.write(str(result_xmllist))\n\n result_jsonlist = []\n for data in result_xmllist:\n # print all in xml\n print(data)\n\n # print all in json\n result_xml_str = repr(data)\n result_json_parsed_str = json.dumps(xmltodict.parse(result_xml_str))\n result_json_parsed_dict = json.loads(result_json_parsed_str)\n\n print(json.dumps(result_json_parsed_dict, indent=4, sort_keys=True))\n result_jsonlist.append(result_json_parsed_dict)\n\n with open('output/netconf_101_rpc.json', 'w') as file:\n json.dump(result_jsonlist, file, indent=4, sort_keys=True)\n\n\n # xml_doc = xml.dom.minidom.parseString(result.xml)\n # mac_address = xml_doc.getElementsByTagName(\"mod:mac_address\")\n # print(mac_address)","def find_net_info(sfe):\n print(\"-\" * 20 + \" find_net_info started\")\n virt_net = sfe.list_virtual_networks()\n json_virt_net = virt_net.to_json()\n #pprint(json_virt_net)\n virt_mask = json_virt_net['virtualNetworks'][0]['netmask']\n svip = json_virt_net['virtualNetworks'][0]['svip']\n\n # Break the netmask into constituent octets to get the one that determines the host network\n mask_oct1 = int(virt_mask.split(\".\")[0])\n mask_oct2 = int(virt_mask.split(\".\")[1])\n mask_oct3 = int(virt_mask.split(\".\")[2])\n mask_oct4 = int(virt_mask.split(\".\")[3])\n\n # Return the octet that has the determining bits\n if mask_oct1 != 255:\n oct_pos = 0\n comp_oct = mask_oct1\n elif mask_oct2 != 255:\n oct_pos = 1\n comp_oct = mask_oct2\n elif mask_oct3 != 255:\n oct_pos = 2\n comp_oct = mask_oct3\n else:\n oct_pos = 3\n comp_oct = mask_oct4\n\n # Find the network block size\n comp_block = 256 - comp_oct \n\n # Find the SVIP host bits\n comp_svip = int(svip.split(\".\")[oct_pos])\n int_svip = int(comp_svip)\n return int_svip, comp_block, oct_pos","def do_command(command):\n send_command(command)\n # time.sleep(0.1) # may be required on slow machines\n response = get_response()\n print(\"Rcvd: <<< \" + response)\n return response","def execute(connection_info: NodeConnectionInfo) -> dict:\n response = rpc_client.request(\n connection_info.address_rpc,\n constants.RPC_INFO_GET_PEERS\n )\n\n return response.data.result[\"peers\"]","def execute(host, username, command, port=22):\n log = logging.getLogger('device.remotecall')\n ret = {}\n \"\"\"Executar comando com a opcao de echo $? no final para\n descobrir o valor do retorno da execucao do comando\"\"\"\n cmd = 'ssh -p %s %s@%s' % (port, username, host)\n cmd = shlex.split(cmd)\n command = command + ' ; echo $?'\n cmd.append(command)\n log.info('COMMAND=%s', cmd)\n try:\n out = subprocess.check_output(cmd)\n \"\"\"Necessario separar as linhas em lista, por causa\n da compatilidade com a implementacao ssh anterior\"\"\"\n out = out.splitlines(True)\n \"\"\"Descartar ultima linha por causa da quebra de linha\n do valor de retorno\"\"\"\n ret['output'] = out[:-1]\n ret['exit_code'] = int(out[-1])\n except subprocess.CalledProcessError as e:\n ret['output'] = e.output\n ret['exit_code'] = int(e.returncode)\n \"\"\"Forcar codificacao da string\"\"\"\n ret['output'] = map(lambda x: unicode(x, 'utf-8'), ret['output'])\n log.info('Return status [%s] command:%s', ret['exit_code'], command)\n return ret","def ip_command():\n # 1. Get input host from Demisto\n ip = demisto.args().get('ip')\n if not is_ip_valid(ip):\n return_error('Invalid IP address, Please retry with a valid IP address')\n # 2. Get the host reputation from SlashNext API\n response = ip_lookup(ip=ip)\n if response.get('errorNo') != 0:\n return\n # 3. Parse and format the response\n dbot_score_cont, ip_cont = get_dbot_std_context(\n ip, 'IP', response.get('threatData').get('verdict'), response.get('threatData').get('threatType'))\n\n snx_ioc_cont = get_snx_host_ioc_context(ip, 'IP', response.get('threatData'))\n\n ec = {\n 'SlashNext.IP(val.Value === obj.Value)': snx_ioc_cont,\n 'DBotScore': dbot_score_cont,\n 'IP': ip_cont\n }\n\n title = 'SlashNext Phishing Incident Response - IP Lookup\\n' \\\n '##### ip = {}'.format(ip)\n\n md = tableToMarkdown(\n title,\n snx_ioc_cont,\n ['Value',\n 'Type',\n 'Verdict',\n 'ThreatStatus',\n 'ThreatName',\n 'ThreatType',\n 'FirstSeen',\n 'LastSeen']\n )\n\n return_outputs(md, ec, snx_ioc_cont)","def network_info(self) -> dict:\n \n network_info_url = self.network + bf_network_informations_url\n \n response = query_blockfrost(network_info_url, self.api_key, self.proxies)\n \n return response","def api_myip():\n return request.remote_addr, 200, {'Content-Type': 'text/plain'}","def remote_getSysinfo(self, request):\r\n # TODO : replace these calls with call to rce.util.sysinfo\r\n response_table = {\r\n 'size':self._size,\r\n 'cpu':self._cpu,\r\n 'memory': self._memeory,\r\n 'bandwidth': self._bandwidth,\r\n # 'keyword': some value or function to provide the data\r\n }\r\n\r\n return response_table[request]","def local_info():\n local('uname -a')","def infocalypse_info(ui_, repo, **opts):\n # FCP not required. Hmmm... Hack\n opts['fcphost'] = ''\n opts['fcpport'] = 0\n params, stored_cfg = get_config_info(ui_, opts)\n request_uri = opts['uri']\n if request_uri == '':\n request_uri = stored_cfg.get_request_uri(repo.root)\n if not request_uri:\n ui_.warn(\"There is no stored request URI for this repo.\\n\"\n \"Please set one with the --uri option.\\n\")\n return\n\n params['REQUEST_URI'] = request_uri\n execute_info(ui_, repo, params, stored_cfg)","def do_GET(self):\n server_ip = Setup.parse_options()['ip_address']\n uri = \"http://\" + server_ip + self.path\n response = urllib.urlopen(uri)\n self.copyfile(response, self.wfile)\n headers = self.generate_header_dic(self.headers.headers)\n ip_address = self.client_address[0] # get client iP address\n if Setup.system_status != 'green':\n self.process_request(ip_address, headers, self.path)\n self.process_response(ip_address, response.headers)","def test_get_network(self):\n pass","def execute_remote(ipaddr, command):\n\n\treturn execute(['ssh', '-f', '-t', '-oPasswordAuthentication=no',\n\t\t'-l', 'alt', ipaddr, command])","def run(host, tasks, procsPerTask, memFracPerTask, outputLoc, primaryIP, allocation,\n instance, localIP):\n argStr = ' '.join(sys.argv[1:])\n #argStr = ' '.join([str(i) for i in locals().values()]) # do not move this line.\n #TODO: Determine logDir value.\n logDir = \"\"\n\n #os.system(\"sleep 15\")\n os.system(\"python {0} {1} &\".format(join(dirname(__file__), \"networking.py\"), argStr))\n os.system(\"sleep 20\")\n port = 13001\n buf = 1024\n tcpsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n m_addr = (primaryIP, port)\n tcpsocket.connect(m_addr)\n register_command = \"register\\t{0}\\t{1}\".format(host, tasks)\n os.system(\"echo {0} >> {1}\".format(register_command, join(logDir, \"sent.log\")))\n tcpsocket.send(register_command)\n tcpsocket.close()","def ping(host,hidden=True):\r\n\r\n # Ping command count option as function of OS\r\n param = '-n' if platform.system()=='Windows' else '-c'\r\n\r\n # Building the command. Ex: \"ping -c 1 google.com\"\r\n command = ['ping', param, '1', host]\r\n \r\n startupinfo = None\r\n if hidden:\r\n if os.name == 'nt':\r\n startupinfo = subprocess.STARTUPINFO()\r\n startupinfo.dwFlags |= subprocess._subprocess.STARTF_USESHOWWINDOW#subprocess.STARTF_USESHOWWINDOW\r\n #proc = subprocess.Popen(command, startupinfo=startupinfo) \r\n return subprocess.call(command, startupinfo=startupinfo) == 0","def _request(self, method, api_url, **kwargs):\n if not self.connected:\n raise Exception(\"'{d}' is not connected for alias '{a}'\"\n .format(d=self.device.name,\n a=self.alias))\n\n # Deal with the dn\n full_url = '{f}{api_url}'.format(f=self.url, api_url=api_url)\n\n if 'data' in kwargs:\n payload = kwargs['data']\n elif 'json' in kwargs:\n payload = kwargs['json']\n else:\n payload = ''\n\n log.info(\"Sending {method} command to '{d}':\"\n \"\\napi_url: {furl}\\nPayload:{payload}\"\n .format(method=method,\n d=self.device.name,\n furl=full_url,\n payload=payload))\n\n headers = {\n 'Dcnm-Token': self.token,\n 'Content-type': 'application/json'\n }\n\n # Send to the device\n response = self.session.request(method=method, url=full_url, headers=headers, **kwargs)\n\n\n # Make sure it was successful\n try:\n response.raise_for_status()\n except Exception:\n raise RequestException(\n \"'{c}' result code has been returned \"\n \"for '{d}'.\\nResponse from server: \"\n \"{r}\".format(d=self.device.name,\n c=response.status_code,\n r=response.text))\n\n # In case the response cannot be decoded into json\n # warn and return the raw text\n\n try:\n output = response.json()\n if isinstance(output, list):\n output = {\"temproot\": output}\n except Exception:\n log.warning('Could not decode json. Returning text!')\n output = response.text\n\n return output","def identify_num_simulate():\r\n # http / https ip:port\r\n proxy = {'http': 'http://112.85.165.113:9999'}\r\n response = requests.get(base_url + '/get', proxies=proxy)\r\n print(response.status_code)\r\n print(response.text)","def get_session_info():\n query = {\"type\": \"op\", \"cmd\": \"<show><session><info></info></session></show>\"}\n\n return __proxy__[\"panos.call\"](query)","def get_net_details(self, net_name=\"dummy_net\", net_id=None):\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks\"\n _headers = {'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n result = self.request(\"GET\", _url, _headers, _body)\n if result is None:\n LOG_OBJ.error(\"No response from Server while listing the nets\")\n return result.status\n if result.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Get network Failed with status %s \" % result.status)\n return result.status\n output = json.loads(result.data)\n\n for nets in output['networks']:\n if (net_id is not None and (nets['id'] == net_id)) or \\\n nets['name'].lower() == net_name.lower():\n LOG_OBJ.debug(\"Net details : %s \" % nets)\n return nets\n\n LOG_OBJ.debug(\"Network with name:%s or with ID:%s is Not Found\" %\n (net_name, net_id))","def get_system_info(self):\r\n method = self.public_endpoints['system_info']['method']\r\n url = self.base_url + self.public_endpoints['system_info']['url']\r\n req = requests.request(method, url)\r\n res = req.json()\r\n\r\n if res['success'] == True:\r\n return res[\"result\"]\r\n else:\r\n return res","async def run(self):\n\n result = {'start_timestamp': time()}\n\n ping = await create_subprocess_exec(\"/bin/ping\",\n self.device,\n \"-c \" + self.count,\n \"-l \" + self.preload,\n \"-W \" + self.timeout,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout = await ping.stdout.read()\n stderr = await ping.stderr.read()\n\n if stderr:\n result['error'] = stderr.decode('utf-8').strip()\n else:\n lines = stdout.splitlines()\n second_last_line = lines[len(lines)-2].decode('utf-8').split()\n last_line = lines[len(lines)-1].decode('utf-8')\n if not last_line:\n # if the last line is empty\n # none of the packets arrived\n result['error'] = 'Host unreachable'\n result['packets_sent'] = second_last_line[0]\n result['packets_recv'] = second_last_line[3]\n else:\n last_line = last_line.split()[3].split('/')\n result['min'] = last_line[0]\n result['avg'] = last_line[1]\n result['max'] = last_line[2]\n result['mdev'] = last_line[3]\n result['packets_sent'] = second_last_line[0]\n result['packets_recv'] = second_last_line[3]\n\n result['end_timestamp'] = time()\n self.results.append(result)\n return result","def RemoteCall(self, address, argDict):\r\n print(f\"Making RPC for {argDict[RPC_ARG_REQUEST_TYPE]} to {address}\")\r\n requestSocket = self.CreateAClientSocket(address)\r\n requestByteArray = pickle.dumps(argDict)\r\n requestSocket.sendall(requestByteArray)\r\n requestSocket.shutdown(socket.SHUT_WR)\r\n value = pickle.loads(requestSocket.recv(BUFFER_SIZE))\r\n requestSocket.shutdown(socket.SHUT_RD)\r\n requestSocket.close()\r\n return value","def __call__(self) -> list:\n return self.network","def _get_ilo_access(remote_console):\n url = remote_console.get('remoteConsoleUrl')\n url_parse = parse.urlparse(url)\n host_ip = parse.parse_qs(url_parse.netloc).get('addr')[0]\n token = parse.parse_qs(url_parse.netloc).get('sessionkey')[0]\n return host_ip, token","def list_net(self):\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n response = self.request(\"GET\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\"No response from Server while listing the networks\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Get network list Failed with status %s \" %\n response.status)\n return response.status\n output = json.loads(response.data)\n\n LOG_OBJ.info(\"Network List : %s \" % output)\n return output['networks']","def executeRemoteCommand(self, data):\n return self.session.request('diag/command/', 'POST',\n self.getXML(data, 'remoteCommand'))","def getNetworksDetails(network_id):\n logger.debug('Start.')\n code, res = rest_requests.get(networks_url + \"/\" + network_id)\n if code != requests.codes.ok:\n logger.error((code, res))\n return None\n return res[\"network\"]","def info(self):\n return requests.get(self.info_url + self.pid).json()","def getProcessInfo(self, name):\r\n self._update('getProcessInfo')\r\n\r\n group, process = self._getGroupAndProcess(name)\r\n\r\n if process is None:\r\n raise RPCError(Faults.BAD_NAME, name)\r\n\r\n start = int(process.laststart)\r\n stop = int(process.laststop)\r\n now = int(time.time())\r\n\r\n state = process.get_state()\r\n spawnerr = process.spawnerr or ''\r\n exitstatus = process.exitstatus or 0\r\n stdout_logfile = process.config.stdout_logfile or ''\r\n stderr_logfile = process.config.stderr_logfile or ''\r\n\r\n info = {\r\n 'name':process.config.name,\r\n 'group':group.config.name,\r\n 'start':start,\r\n 'stop':stop,\r\n 'now':now,\r\n 'state':state,\r\n 'statename':getProcessStateDescription(state),\r\n 'spawnerr':spawnerr,\r\n 'exitstatus':exitstatus,\r\n 'logfile':stdout_logfile, # b/c alias\r\n 'stdout_logfile':stdout_logfile,\r\n 'stderr_logfile':stderr_logfile,\r\n 'pid':process.pid,\r\n }\r\n\r\n description = self._interpretProcessInfo(info)\r\n info['description'] = description\r\n return info","def getIpTcp(self):\n\t\tquery = ''\n\t\tconn = self.get_connection()\n\t\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\n\t\tconn.request('GET', self.get_path() + '/' + query, headers=headers)\n\t\tresponse = conn.getresponse()\n\t\texpected_status = 200\n\t\terrors = {500: 'An unexpected runtime exception', 404: 'Specified tcp does not exist'}\n\t\tpayload = self.get_output(response, expected_status, errors)\n\t\tconn.close()\n\t\tif self.debug:\n\t\t\tprint 'payload:', payload\n\t\tif payload == '':\n\t\t\tpayload = None\n\t\tif payload is not None:\n\t\t\tdata = json.loads(payload)\n\t\t\tpayload= data.get('tcp')\n\t\treturn deserialize_Tcp_json(payload)","def _getvpn(node, cliargs=CliArg()):\n header = 'http://www.vpngate.net'\n site = 'http://'+node._vpn\n try:\n response = urllib2.urlopen(site)\n data = response.read()\n except:\n print(\" unable to establish network connection.\\\n \\n .....\\\n \\n check network connectivity and site status.\")\n sys.exit(1)\n vpn = None\n vpn_ = []\n \"find desired protocol\"\n if cliargs._proto in 'udp':\n vpns_ = re.findall(r'(/common/openvpn[^\\'\" >]+)', data)\n for x in range(len(vpns_)):\n if str(vpns_[x]).find('udp') >= 0:\n vpn_.append(vpns_[x])\n else:\n vpns_ = re.findall(r'(/common/openvpn[^\\'\" >]+)', data)\n for x in range(len(vpns_)):\n if str(vpns_[x]).find('tcp') >= 0:\n vpn_.append(vpns_[x])\n if vpn_:\n \"remove garbage\"\n vpn = vpn_[0]\n pos = vpn.find('amp;')\n while pos is not -1:\n vpn = vpn[:pos]+vpn[pos+4:]\n pos = vpn.find('amp;')\n if vpn:\n return header + vpn\n return None","def network(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"network\")","def get_net_info(net_env):\n\n # check the shared dir first. it could force us into local mode\n shared_dir = os.getenv(SHARED_DIR_ENV)\n if shared_dir is None:\n # if no shared directory is set, we MUST operate in local mode\n print \"No shared directory set. Defaulting to local mode\"\n os.environ[SHARED_DIR_ENV] = SHARED_DIR_DEFAULT\n elif shared_dir == SHARED_DIR_DEFAULT\n pass\n else:\n net_string = os.environ.get(net_env)\n\n if net_string:\n host, port = validate(net_string)\n return host, port\n\n # finally default to local mode\n net_string = DEFAULT_NET[net_env]\n\n if net_string:\n return validate(net_string)","def run_info ( run_num ) : \n global _rinfos_\n rinfo = _rinfos_.get ( run_num , None )\n if rinfo : return rinfo \n \n try :\n \n #\n url = run_url.format ( run_num )\n _obj = urllib.urlopen ( url )\n rinfo = json.load ( _obj )\n\n rinfo = rinfo if rinfo else None\n _rinfos_ [ run_num ] = rinfo \n return rinfo\n \n except:\n return None \n\n return None","def ShowTaskIPC(cmd_args=None):\n if not cmd_args:\n print \"No arguments passed\"\n print ShowTaskIPC.__doc__\n return False\n tval = kern.GetValueFromAddress(cmd_args[0], 'task *')\n if not tval:\n print \"unknown arguments:\", str(cmd_args)\n return False\n print GetTaskSummary.header + \" \" + GetProcSummary.header\n pval = Cast(tval.bsd_info, 'proc *')\n print GetTaskSummary(tval) + \" \" + GetProcSummary(pval)\n print GetTaskBusyIPCSummary.header\n (summary, table_size, nbusy, nmsgs) = GetTaskBusyIPCSummary(tval)\n print summary"],"string":"[\n \"def remote_info():\\n run('uname -a')\",\n \"def do_remote(self, *args):\\n return self.do_scpi(':communicate:remote 1')\",\n \"def do_local(self, *args):\\n return self.do_scpi(':communicate:remote 0')\",\n \"def subcmd_getnic_main(args, parameter_info):\\n \\n from get_nic_inventory import get_nic_inventory\\n result = get_nic_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def nremote(self):\",\n \"def get_system_info():\\n query = {\\\"type\\\": \\\"op\\\", \\\"cmd\\\": \\\"<show><system><info></info></system></show>\\\"}\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def pull_info(task):\\n\\n interface_result = task.run(task=send_command, command=\\\"show interfaces\\\")\\n task.host[\\\"facts\\\"] = interface_result.scrapli_response.genie_parse_output()\\n interfaces = task.host[\\\"facts\\\"]\\n for interface in interfaces:\\n try:\\n mac_addr = interfaces[interface][\\\"mac_address\\\"]\\n if target == mac_addr:\\n target_list.append(mac_addr)\\n intf = interface\\n print_info(task, intf)\\n except KeyError:\\n pass\",\n \"def getRemoteHost():\",\n \"def remote(self, *arguments, **kwargs):\\n return self.get_output('remote', *arguments, **kwargs)\",\n \"def print_info(task, intf):\\n\\n rprint(\\\"\\\\n[green]*** TARGET IDENTIFIED ***[/green]\\\")\\n print(f\\\"MAC ADDRESS: {target} is present on {task.host}'s {intf}\\\")\\n rprint(\\\"\\\\n[cyan]GENERATING DETAILS...[/cyan]\\\")\\n cdp_result = task.run(task=send_command, command=\\\"show cdp neighbors\\\")\\n task.host[\\\"cdpinfo\\\"] = cdp_result.scrapli_response.genie_parse_output()\\n dev_id = \\\"\\\"\\n index = task.host[\\\"cdpinfo\\\"][\\\"cdp\\\"][\\\"index\\\"]\\n for num in index:\\n local_intf = index[num][\\\"local_interface\\\"]\\n if local_intf == intf:\\n dev_id = index[num][\\\"device_id\\\"]\\n port_id = index[num][\\\"port_id\\\"]\\n\\n ver_result = task.run(task=send_command, command=\\\"show version\\\")\\n task.host[\\\"verinfo\\\"] = ver_result.scrapli_response.genie_parse_output()\\n version = task.host[\\\"verinfo\\\"][\\\"version\\\"]\\n serial_num = version[\\\"chassis_sn\\\"]\\n oper_sys = version[\\\"os\\\"]\\n uptime = version[\\\"uptime\\\"]\\n version_short = version[\\\"version_short\\\"]\\n print(f\\\"DEVICE MGMT IP: {task.host.hostname}\\\")\\n print(f\\\"DEVICE SERIAL NUMBER: {serial_num}\\\")\\n print(f\\\"DEVICE OPERATION SYSTEM: {oper_sys}\\\")\\n print(f\\\"DEVICE UPTIME: {uptime}\\\")\\n print(f\\\"DEVICE VERSION: {version_short}\\\")\\n if dev_id:\\n rprint(\\\"[cyan]REMOTE CONNECTION DETAILS...[/cyan]\\\")\\n print(f\\\"Connected to {port_id} on {dev_id}\\\")\",\n \"def main():\\n with Scrapli(**MY_DEVICE) as conn:\\n print(conn.get_prompt())\\n print(conn.send_command(\\\"show run | i hostname\\\").result)\",\n \"def GetNodeInfo(self, hvparams=None):\\n return self.GetLinuxNodeInfo()\",\n \"def show_network(self, network, **_params):\\r\\n return self.get(self.network_path % (network), params=_params)\",\n \"def getHostInfo():\",\n \"def test_get_address(self):\\n with self.subprocess_getoutput_patch:\\n ret = self.inst._get_address()\\n self.assertEqual(ret, \\\"http://example\\\")\",\n \"def get_info(task):\\n cmd = \\\"show version\\\"\\n sh_version = task.run(\\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\\n )\\n # test Nornir result\\n test_norn_textfsm(task, sh_version.result, cmd)\\n # save show version output to task.host\\n task.host[\\\"sh_version\\\"] = sh_version.result[0]\\n # pull model from show version\\n sw_model = task.host[\\\"sh_version\\\"][\\\"hardware\\\"][0].split(\\\"-\\\")\\n # save model to task.host\\n task.host[\\\"sw_model\\\"] = sw_model[1]\\n # get interfaces; use TextFSM\\n cmd = \\\"show interface switchport\\\"\\n interfaces = task.run(\\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\\n )\\n # test Nornir result\\n test_norn_textfsm(task, interfaces.result, cmd)\\n # save interfaces to task.host\\n task.host[\\\"intfs\\\"] = interfaces.result\\n # convert vlans in inventory from int to str\\n vlans = []\\n for vlan in task.host[\\\"vlans\\\"]:\\n vlans.append(str(vlan))\\n # save list of vlans strings back to task.host\\n task.host[\\\"vlans\\\"] = vlans\\n # create vlan_list string\\n task.host[\\\"vlan_list\\\"] = \\\",\\\".join(task.host[\\\"vlans\\\"])\\n\\n # choose template based on switch model\\n if \\\"3750V2\\\" in task.host[\\\"sw_model\\\"] or \\\"3750G\\\" in task.host[\\\"sw_model\\\"]:\\n # 3750V2's use IBNSv1\\n task.host[\\\"ibns_ver\\\"] = \\\"v1\\\"\\n c_print(f\\\"*** {task.host}: IBNS version 1 ***\\\")\\n\\n elif \\\"3750X\\\" in task.host[\\\"sw_model\\\"]:\\n # 3750X's use IBNSv2-modified\\n task.host[\\\"ibns_ver\\\"] = \\\"v2-alt\\\"\\n c_print(f\\\"*** {task.host}: IBNS version 2 (modified) ***\\\")\\n\\n else:\\n # all else use IBNSv2\\n task.host[\\\"ibns_ver\\\"] = \\\"v2\\\"\\n c_print(f\\\"*** {task.host}: IBNS version 2 ***\\\")\\n\\n # get ip interface brief; use TextFSM\\n cmd = \\\"show ip interface brief | e unas\\\"\\n ip_int_br = task.run(\\n task=netmiko_send_command, command_string=cmd, use_textfsm=True\\n )\\n # test Nornir result\\n test_norn_textfsm(task, ip_int_br.result, cmd)\\n # save ip interfaces to task.host\\n task.host[\\\"ip_int_br\\\"] = ip_int_br.result\",\n \"def connect_remote_path(remotedir,username,password):\\n connect_command = 'net use %s /user:%s %s' % (remotedir,username,password)\\n process = subprocess.Popen(connect_command, shell = True, stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n\\n return_code = process.stdout.read()\\n if 'The command completed successfully.' in return_code:\\n return 1\\n #print '>> Connect remote path successfully'\\n else:\\n #raise AssertionError('>> Connect remote path failed: %s' % return_code) \\n return 0\",\n \"def remote():\\n pass\",\n \"def remote_status():\",\n \"def system_call(command):\\n p = subprocess.Popen([command], stdout=subprocess.PIPE, shell=True)\\n return p.stdout.read()\",\n \"def cli(ctx: Configuration, raw):\\n from netdisco.discovery import NetworkDiscovery\\n\\n click.echo(\\\"Running discovery on network (might take a while)...\\\")\\n netdiscovery = NetworkDiscovery()\\n netdiscovery.scan()\\n\\n for device in netdiscovery.discover():\\n info = netdiscovery.get_info(device)\\n click.echo(\\\"{}:\\\\n{}\\\".format(device, format_output(ctx, info)))\\n\\n if raw:\\n click.echo(\\\"Raw data:\\\")\\n netdiscovery.print_raw_data()\\n\\n netdiscovery.stop()\",\n \"def rpc_info():\",\n \"def net_proc(pipe):\\n asyncio.run(net_server(pipe))\",\n \"def _rest_call(self, data, action):\\n path = '/wm/device/?ipv4=' + data\\n conn = httplib.HTTPConnection(self.host, self.port)\\n conn.request('GET', path)\\n response = conn.getresponse()\\n ret = (response.status, response.reason, response.read())\\n conn.close()\\n return ret\",\n \"def get_info_cli(self, no):\\n #\\n os.system(\\\"toilet -f smblock --filter border:metal -w 50 'Indo Phone Number Checker - ( IPNC )'\\\")\\n print()\\n os.system(\\\"toilet -f smblock ' by : @danrfq' --filter gay\\\")\\n print()\\n\\n for data in self.get_info(no):\\n if \\\"message\\\" in data:\\n print(c.fg.red+\\\"[ ERROR ]\\\\nFormat Nomor Yang Anda Masukkan Salah!\\\"+c.end+\\\"\\\\n\\\"+c.fg.lightgreen+\\\"Contoh Nomor : +6281291718019\\\"+c.end)\\n else:\\n print(c.fg.yellow+\\\"\\\"\\\"╔ [ {} Information ]\\n╠\\n╠ International : {}\\n╠ National : {}\\n╠ Provider : {}\\n╠ Type : {}\\n╠ Location : {}\\n╠ Timezones : {}\\n╠\\n╚ [ Finish ]\\\"\\\"\\\".\\n format(no,\\n data[\\\"international\\\"],\\n data[\\\"national\\\"],\\n data['provider'],\\n data[\\\"type\\\"].replace(\\\"_\\\",\\\" \\\").title(),\\n data[\\\"location\\\"],\\n \\\", \\\".join(data[\\\"timezone\\\"]))+c.end\\n )\",\n \"def linux():\\n command = \\\"cat /etc/NetworkManager/system-connections/*\\\"\\n networks = subprocess.check_output(command, shell=True).decode(\\\"utf-8\\\")\\n return networks\",\n \"def get_network_status(self, who=\\\"all\\\", get_iterator=False):\\r\\n\\r\\n nsData = self.sendAndRecv(\\\"GETINFO ns/\\\"+who+\\\"\\\\r\\\\n\\\")[0][2]\\r\\n if get_iterator: return ns_body_iter(nsData)\\r\\n else: return parse_ns_body(nsData)\",\n \"def show_virtual_network(client, private_cloud, virtual_network, location):\\n return client.get(location, private_cloud, virtual_network)\",\n \"def request_from_identity(cls, identity):\\n params = identity.request_params\\n params.update({\\n 'action': 'info',\\n 'ipaddr': 'true',\\n 'hdd': 'true',\\n 'mem': 'true',\\n 'bw': 'true',\\n 'status': 'true'\\n })\\n\\n response = requests.get(\\n identity.vendor.endpoint + cls._ENDPOINT, params=params)\\n if response.status_code != requests.codes.ok:\\n raise RuntimeError(\\n 'Unable to retrieve host: {0}'.format(response.text))\\n return cls.from_response(response.text, identity)\",\n \"def show_networks():\\n return get_networks()\",\n \"def run_xp(self, net, run, client, fileToDownload, **kwargs):\\n gateway = net.get('gateway')\\n # cmd = \\\"wget http://%s/%s -O /dev/null\\\" \\n cmd = \\\"curl -so /dev/null -w '%%{time_total}\\\\n' http://%s/%s\\\"\\n cmd = cmd % (\\\"7.7.7.7:8000\\\", fileToDownload)\\n # import re\\n # elapsed_ms_str = re.sub(\\\"tcpdump.*\\\", \\\"\\\", client.cmd( % (topo.server_addr, filename)).replace(\\\"> \\\", \\\"\\\"))\\n # elapsed_ms = float(elapsed_ms_str)*1000\\n\\n # out = client.monitor(timeoutms=2000)\\n # log.info(cmd)\\n # CLI(net)\\n print(\\\"starting\\\")\\n out = client.cmdPrint(cmd)\\n print(out)\\n # import re\\n # elapsed_ms_str = re.sub(\\\"tcpdump.*\\\", \\\"\\\", client.cmd(\\\"curl -so /dev/null -w '%%{time_total}\\\\n' http://%s/%s\\\" % (topo.server_addr, filename)).replace(\\\"> \\\", \\\"\\\"))\\n\\n elapsed_ms_str = out[2:].rstrip() # replace(\\\"> \\\", \\\"\\\"))\\n # print(\\\"elapsed_ms_str\\\", elapsed_ms_str)\\n elapsed_ms = float(elapsed_ms_str)*1000\\n\\n print(\\\"elapsed_ms\\\", elapsed_ms)\\n\\n # write avec ou sans dupack puis la valeur\\n return elapsed_ms\",\n \"def _get_ip():\\n cmd_netstat = ['netstat', '-nr']\\n p1 = subprocess.Popen(cmd_netstat, stdout=subprocess.PIPE)\\n cmd_grep = ['grep', '^0\\\\.0\\\\.0\\\\.0']\\n p2 = subprocess.Popen(cmd_grep, stdin=p1.stdout, stdout=subprocess.PIPE)\\n cmd_awk = ['awk', '{ print $2 }']\\n p3 = subprocess.Popen(cmd_awk, stdin=p2.stdout, stdout=subprocess.PIPE)\\n galaxy_ip = p3.stdout.read()\\n log.debug('Host IP determined to be %s', galaxy_ip)\\n return galaxy_ip\",\n \"def xen_rpc_call(ip, method, *args):\\n try:\\n if not ip:\\n return xen_api_error(\\\"Invalid ip for rpc call\\\")\\n # create\\n proxy = ServerProxy(\\\"http://\\\" + ip + \\\":9363/\\\")\\n \\n # login \\n response = proxy.session.login('root')\\n if cmp(response['Status'], 'Failure') == 0:\\n log.exception(response['ErrorDescription'])\\n return xen_api_error(response['ErrorDescription']) \\n session_ref = response['Value']\\n \\n # excute\\n method_parts = method.split('_')\\n method_class = method_parts[0]\\n method_name = '_'.join(method_parts[1:])\\n \\n if method.find(\\\"host_metrics\\\") == 0:\\n method_class = \\\"host_metrics\\\"\\n method_name = '_'.join(method_parts[2:])\\n #log.debug(method_class)\\n #log.debug(method_name)\\n if method_class.find(\\\"Async\\\") == 0:\\n method_class = method_class.split(\\\".\\\")[1]\\n response = proxy.__getattr__(\\\"Async\\\").__getattr__(method_class).__getattr__(method_name)(session_ref, *args)\\n else:\\n response = proxy.__getattr__(method_class).__getattr__(method_name)(session_ref, *args)\\n if cmp(response['Status'], 'Failure') == 0:\\n log.exception(response['ErrorDescription'])\\n return xen_api_error(response['ErrorDescription'])\\n # result\\n return response\\n except socket.error:\\n return xen_api_error('socket error')\",\n \"def _request_info(self, callback=None):\\n self.stick.send(\\n NodeInfoRequest(self.mac),\\n callback,\\n )\",\n \"def fetch_status():\\n try:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.connect((GEARMAND_HOST, GEARMAND_PORT))\\n log_verbose('Connected to Gearmand at %s:%s' % (GEARMAND_HOST, GEARMAND_PORT))\\n except socket.error, e:\\n collectd.error('gearmand_info plugin: Error connecting to %s:%d - %r'\\n % (GEARMAND_HOST, GEARMAND_PORT, e))\\n return None\\n fp = s.makefile('r')\\n log_verbose('Sending info command')\\n s.sendall('status\\\\r\\\\n')\\n\\n status = {}\\n while True:\\n data = fp.readline().strip()\\n log_verbose('Received data: %r' % data)\\n if not data or data == '.':\\n break\\n function, total, running, available_workers = data.split('\\\\t')\\n status[function] = {\\n 'total': total,\\n 'running': running,\\n 'available_workers': available_workers}\\n\\n s.close()\\n return status\",\n \"def process_info(process):\\n\\thelp(process)\",\n \"def do_nic_show(cc, args):\\n fields = []\\n if args.fields:\\n fields = args.fields.split(',')\\n if fields and 'uuid' not in fields:\\n fields.append('uuid')\\n if args.uuid:\\n result = cc.nic.show(args.uuid, fields)\\n elif args.mac:\\n result = cc.nic.get_by_mac(args.mac)\\n else:\\n print (_(\\\"Invalid argument given.\\\"))\\n return\\n cliutils.print_dict(result)\",\n \"def remote_fetch(ip_addr, username, cmd):\\n ssh = paramiko.SSHClient()\\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n\\n ssh.connect(ip_addr, username=username)\\n stdin, stdout, stderr = ssh.exec_command(cmd)\\n return stdout.readlines()\",\n \"def subcmd_getmemory_main(args, parameter_info):\\n \\n from get_memory_inventory import get_memory_inventory\\n result = get_memory_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'], None)\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def _checknet():\\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\\n return exit_code\",\n \"def test_process_info(self, mocked_get, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test first RPC call with process namespec\\n self.assertEqual([{'name': 'proc'}], rpc.get_process_info('appli:proc'))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call('appli:proc')], mocked_get.call_args_list)\\n # reset patches\\n mocked_check.reset_mock()\\n mocked_get.reset_mock()\\n # test second RPC call with group namespec\\n self.assertEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\\n rpc.get_process_info('appli:*'))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call('appli:*')], mocked_get.call_args_list)\",\n \"async def launch_kernel(self, cmd: List[str], **kwargs: Any) -> KernelConnectionInfo:\\n\\n launch_kwargs = RemoteProvisionerBase._scrub_kwargs(kwargs)\\n self.local_proc = launch_kernel(cmd, **launch_kwargs)\\n self.pid = self.local_proc.pid\\n self.ip = local_ip\\n\\n self.log_kernel_launch(cmd)\\n\\n await self.confirm_remote_startup()\\n return self.connection_info\",\n \"def net_cmd(self):\\n \\n logging.debug(\\\"net_cmd called\\\")\\n cmd = \\\"\\\"\\n # FIXME should probably grab the PrefixLength from the network definition\\n # calc my router\\n \\n # FIXME: Need to split this into separate files...\\n # files will be a dictionary of {\\\"filename\\\":\\\"contents\\\"}\\n files = {}\\n \\n cmd = \\\"rem cmd\\\\r\\\\n\\\"\\n tmpl = \\\"\\\"\\\"netsh interface ip set address \\\"%(nic)s\\\" static %(v4_fixed_ip)s 255.255.255.0\\\\r\\\\n\\\"\\\"\\\"\\n # FIXME: this should be read out of the configuration, probably\\n nets = self.networks\\n ips = self.ip_addresses \\n my_router = \\\"\\\"\\n for netname in nets:\\n v4_fixed_ip = ips.get(netname)\\n my_net = v4_fixed_ip.split(\\\".\\\")[:3]\\n my_net.append(\\\"254\\\")\\n my_router = \\\".\\\".join(my_net)\\n nic = \\\"%s-%s\\\" % (self.footprint.footprint_name, netname)\\n logging.debug(\\\"Creating %s for %s\\\" % (nic, nets))\\n # net_id = self.networks.get(netname)\\n cmd = cmd + tmpl % locals()\\n \\n cmd += \\\"\\\"\\\"route -p add 192.168.1.0 MASK 255.255.255.0 %(my_router)s\\\\r\\\\n\\\"\\\"\\\" % locals()\\n cmd += \\\"\\\"\\\"route -p add 192.168.2.0 MASK 255.255.255.0 %(my_router)s\\\\r\\\\n\\\"\\\"\\\" % locals()\\n cmd += \\\"\\\"\\\"route -p add 192.168.3.0 MASK 255.255.255.0 %(my_router)s\\\\r\\\\n\\\"\\\"\\\" % locals()\\n logging.debug(\\\"cmdfile:\\\\n\\\" + cmd)\\n \\n # print 50 * \\\"x\\\"\\n # print cmd\\n return cmd\",\n \"async def get_system_info(self) -> Dict[str, Any]:\\n assert self._client is not None\\n return await self._client.invoke_method(\\\"system.info\\\")\",\n \"def _repr_remote(self):\\n return \\\"%s:%d\\\" % (self.remote_address)\",\n \"def ping_remote_system(host, options=\\\"\\\", ignore=\\\"\\\"):\\r\\n connection_type = connections.get_current_connection_type()\\r\\n\\r\\n## option_list = ''\\r\\n## for option in options:\\r\\n## option_list = option_list + option + ' '\\r\\n ret = connections.execute_shell_command_without_check('ping %s %s' % (options, host))\\r\\n time_delay = []\\r\\n delay_max_min_avg = []\\r\\n statistic_delay = []\\r\\n if connection_type == 'Windows':\\r\\n lines = ret.split(os.linesep)\\r\\n delay_pattern1 = re.compile(r'(?i).*Reply from.*?time=(\\\\d+)ms.*')\\r\\n delay_pattern2 = re.compile(r'(?i).*Reply from.*?time<(\\\\d+)ms.*')\\r\\n for line in lines:\\r\\n if re.match(r'(?i).*Reply from.*?time.*(\\\\d+)ms.*', line):\\r\\n time = delay_pattern1.match(line)\\r\\n if time:\\r\\n tmp = time.groups()[0]\\r\\n time_delay.append(int(tmp))\\r\\n else:\\r\\n time = delay_pattern2.match(line)\\r\\n if time:\\r\\n tmp = time.groups()[0]\\r\\n time_delay.append(int(tmp))\\r\\n else:\\r\\n time_delay.append(-1)\\r\\n else:\\r\\n result = re.search('.*Sent\\\\s*=\\\\s*(\\\\d*).*Received\\\\s*=\\\\s*(\\\\d*).*Lost\\\\s*=\\\\s*(\\\\d*)', line)\\r\\n if result:\\r\\n summary_result = result.groups()\\r\\n print \\\"Total ping delay is:\\\",time_delay\\r\\n\\r\\n if \\\"\\\"==ignore:\\r\\n statistic_delay = time_delay\\r\\n else:\\r\\n (start,end) = ignore.split(\\\":\\\")\\r\\n if \\\"\\\"==start:\\r\\n start = 0\\r\\n else:\\r\\n start = int(start)\\r\\n\\r\\n if \\\"\\\"==end:\\r\\n end = len(time_delay)\\r\\n else:\\r\\n end = int(end)\\r\\n\\r\\n for i in range(start,end):\\r\\n statistic_delay.append(time_delay[i])\\r\\n print \\\"Statistic ping delay is:\\\",statistic_delay\\r\\n\\r\\n invalid = statistic_delay.count(-1)\\r\\n for i in range(invalid):\\r\\n statistic_delay.remove(-1)\\r\\n print \\\"Valid ping delay is:\\\",statistic_delay\\r\\n\\r\\n\\r\\n if 0 < len(statistic_delay):\\r\\n list_max = max(statistic_delay)\\r\\n list_min = min(statistic_delay)\\r\\n list_avg = float(sum(statistic_delay))/float(len(statistic_delay))\\r\\n delay_max_min_avg.append(list_max)\\r\\n delay_max_min_avg.append(list_min)\\r\\n delay_max_min_avg.append(list_avg)\\r\\n else:\\r\\n delay_max_min_avg = [0,0,0]\\r\\n\\r\\n if connection_type == 'Linux':\\r\\n if line.find('packets') > 0:\\r\\n '4 packets transmitted, 0 received, 100% packet loss, time 3000ms'\\r\\n result = re.search('(\\\\d+)\\\\s*packets transmitted.*(\\\\d+)\\\\s*received', line)\\r\\n\\r\\n return (summary_result, delay_max_min_avg)\",\n \"def fetch(self, remote, *args):\\n return self.cmd('fetch', remote, *args)\",\n \"def get_physnet(self, port, iface_name, introspection_data):\",\n \"def system_call(command):\\n print(\\\"\\\\n### {}\\\".format(command))\\n stderr = subprocess.STDOUT\\n pipe = subprocess.Popen(\\n command,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT,\\n shell=True,\\n )\\n stdout, stderr = pipe.communicate()\\n print(stdout)\",\n \"def subcmd_getsystem_main(args, parameter_info):\\n \\n from get_system_inventory import get_system_inventory\\n result = get_system_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def system_call(command):\\n process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)\\n return process.communicate()[0]\",\n \"def subcmd_getcpu_main(args, parameter_info):\\n \\n from get_cpu_inventory import get_cpu_inventory\\n result = get_cpu_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def request_info(self):\\r\\n if self.use_http():\\r\\n self.enqueue_http_request(\\\"money/info\\\", {}, \\\"info\\\")\\r\\n else:\\r\\n self.send_signed_call(\\\"private/info\\\", {}, \\\"info\\\")\",\n \"def network_proxy(self):\\n server = create_string_buffer(256)\\n ckresult(\\n _dll.FMOD_System_GetNetworkProxy(self._ptr, byref(server), sizeof(server))\\n )\\n return server.value\",\n \"def get_host_info(self, args, get_all=False):\\n return None\",\n \"def run(self, network_create_args=None):\\n network = self.neutron.create_network(**(network_create_args or {}))\\n self.neutron.get_network(network[\\\"id\\\"])\",\n \"def test_dig():\\n mock = MagicMock(return_value=True)\\n with patch.dict(win_network.__salt__, {\\\"cmd.run\\\": mock}):\\n assert win_network.dig(\\\"google.com\\\")\",\n \"async def run(self):\\n\\n result = {'hops': [],\\n 'start_timestamp': time()}\\n\\n if self.icmp:\\n trace = await create_subprocess_exec(\\\"traceroute\\\",\\n \\\"-n\\\",\\n \\\"-I\\\",\\n \\\"-w\\\" + self.wait_time,\\n \\\"-m\\\" + self.max_hops,\\n \\\"-q 1\\\",\\n self.device,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n else:\\n trace = await create_subprocess_exec(\\\"traceroute\\\",\\n \\\"-n\\\",\\n \\\"-w\\\" + self.wait_time,\\n \\\"-m\\\" + self.max_hops,\\n \\\"-q 1\\\",\\n self.device,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n\\n stdout = await trace.stdout.read()\\n stderr = await trace.stderr.read()\\n\\n if stderr:\\n result['error'] = stderr\\n\\n lines = stdout.splitlines()\\n # remove first line \\\"traceroute to...\\\"\\n del lines[0]\\n\\n for line in lines:\\n line = line.decode('utf-8')\\n ip_address = self.extract_ip_from_line(line)\\n rtt = self.extract_rtt_from_line(line)\\n if(ip_address):\\n result['hops'].append({'ip_address': ip_address,\\n 'rtt': rtt})\\n elif '*' in line:\\n result['hops'].append({'ip_address': '*',\\n 'rtt': '*'})\\n\\n result['end_timestamp'] = time()\\n self.results.append(result)\",\n \"def telnet(self):\\n self.log.info(\\\"connect-via-telnet\\\")\\n telnet = distutils.spawn.find_executable(\\\"telnet\\\")\\n os.execv(telnet, (\\\"telnet\\\", \\\"localhost\\\", str(self.qemu.monitor_port)))\",\n \"def call_process(self, command, **kwargs):\\n return subprocess.check_output(command, **kwargs, encoding=\\\"utf-8\\\")\",\n \"def ProcessRemoteCommandsRequest(self):\\n return (200, '')\",\n \"def status(ctx):\\n return show_network_status()\",\n \"def open_nxview():\\n nxview_procc = Popen([\\\"nxView\\\"])\\n assert nxview_procc is not None, \\\"Could not start nxlib subprocess\\\"\\n time.sleep(10) # wait for nxview\\n return nxview_procc\",\n \"def remote(self, method, params=()):\\n\\n response = self.transport.request(self.host, \\n '/RPC2',\\n dumps(params, method))\\n return response\",\n \"def ip_get_info(ipaddr, show=False):\\n # start Requests session\\n sc = requests.Session()\\n\\n # import cookies from Firefox\\n sc.cookies.update(get_cookies('imhsc.imhadmin.net'))\\n\\n # send request\\n vpx = sc.post('https://imhsc.imhadmin.net/index.php?v=IPManager',\\n data={'type': 'ip', 'query': ipaddr})\\n\\n # check if login failed\\n check_sc_login(vpx.text)\\n\\n # parse with BS4\\n bs = BeautifulSoup(vpx.text, \\\"xml\\\")\\n\\n # parse results\\n trr = bs.table.tbody.find_all('tr')\\n if len(trr) > 0:\\n # get IP id\\n try:\\n t_id = re.match(r'.+id=([0-9]+).+', trr[0].find_all('td')[8].a['href'], re.I).group(1)\\n except:\\n t_id = False\\n\\n # gather IP infos\\n t_info = {\\n 'id': t_id,\\n 'ip': trr[0].find_all('td')[0].string,\\n 'domain': trr[0].find_all('td')[1].string,\\n 'server': trr[0].find_all('td')[2].string,\\n 'net': trr[0].find_all('td')[3].string,\\n 'usage': trr[0].find_all('td')[4].string,\\n 'user': trr[0].find_all('td')[5].string,\\n 'assigned': trr[0].find_all('td')[6].string,\\n 'note': trr[0].find_all('td')[7].string,\\n 'edit_url': trr[0].find_all('td')[8].a['href']\\n }\\n else:\\n t_info = None\\n\\n if show:\\n if t_info:\\n print(\\\"[%(usage)s] %(ip)s (%(net)s) --> %(server)s [User: %(user)s / Domain: %(domain)s / Assigned: %(assigned)s]\\\\n\\\\tNote: %(note)s\\\" % t_info)\\n else:\\n print(\\\"IP address '%s' not found\\\" % (ipaddr))\\n\\n return (t_info, bs)\",\n \"def main():\\n\\n PASS = raw_input('password> ')\\n\\n with manager.connect(host=HOST, port=PORT, username=USER, password=PASS,\\n hostkey_verify=False, device_params={'name': 'default'},\\n look_for_keys=False, allow_agent=False) as m:\\n\\n # print all NETCONF capabilities\\n with open('output/netconf_101_capability.txt', 'w') as file:\\n for capability in m.server_capabilities:\\n file.write(str(capability))\\n file.write('\\\\n')\\n\\n result_xmllist = []\\n # run commands on the remote device\\n for key in xmlns_dic.keys():\\n data = m.get(('subtree', xmlns_dic[key]))\\n result_xmllist.append(data)\\n\\n with open('output/netconf_101_rpc.xml', 'w') as file:\\n file.write(str(result_xmllist))\\n\\n result_jsonlist = []\\n for data in result_xmllist:\\n # print all in xml\\n print(data)\\n\\n # print all in json\\n result_xml_str = repr(data)\\n result_json_parsed_str = json.dumps(xmltodict.parse(result_xml_str))\\n result_json_parsed_dict = json.loads(result_json_parsed_str)\\n\\n print(json.dumps(result_json_parsed_dict, indent=4, sort_keys=True))\\n result_jsonlist.append(result_json_parsed_dict)\\n\\n with open('output/netconf_101_rpc.json', 'w') as file:\\n json.dump(result_jsonlist, file, indent=4, sort_keys=True)\\n\\n\\n # xml_doc = xml.dom.minidom.parseString(result.xml)\\n # mac_address = xml_doc.getElementsByTagName(\\\"mod:mac_address\\\")\\n # print(mac_address)\",\n \"def find_net_info(sfe):\\n print(\\\"-\\\" * 20 + \\\" find_net_info started\\\")\\n virt_net = sfe.list_virtual_networks()\\n json_virt_net = virt_net.to_json()\\n #pprint(json_virt_net)\\n virt_mask = json_virt_net['virtualNetworks'][0]['netmask']\\n svip = json_virt_net['virtualNetworks'][0]['svip']\\n\\n # Break the netmask into constituent octets to get the one that determines the host network\\n mask_oct1 = int(virt_mask.split(\\\".\\\")[0])\\n mask_oct2 = int(virt_mask.split(\\\".\\\")[1])\\n mask_oct3 = int(virt_mask.split(\\\".\\\")[2])\\n mask_oct4 = int(virt_mask.split(\\\".\\\")[3])\\n\\n # Return the octet that has the determining bits\\n if mask_oct1 != 255:\\n oct_pos = 0\\n comp_oct = mask_oct1\\n elif mask_oct2 != 255:\\n oct_pos = 1\\n comp_oct = mask_oct2\\n elif mask_oct3 != 255:\\n oct_pos = 2\\n comp_oct = mask_oct3\\n else:\\n oct_pos = 3\\n comp_oct = mask_oct4\\n\\n # Find the network block size\\n comp_block = 256 - comp_oct \\n\\n # Find the SVIP host bits\\n comp_svip = int(svip.split(\\\".\\\")[oct_pos])\\n int_svip = int(comp_svip)\\n return int_svip, comp_block, oct_pos\",\n \"def do_command(command):\\n send_command(command)\\n # time.sleep(0.1) # may be required on slow machines\\n response = get_response()\\n print(\\\"Rcvd: <<< \\\" + response)\\n return response\",\n \"def execute(connection_info: NodeConnectionInfo) -> dict:\\n response = rpc_client.request(\\n connection_info.address_rpc,\\n constants.RPC_INFO_GET_PEERS\\n )\\n\\n return response.data.result[\\\"peers\\\"]\",\n \"def execute(host, username, command, port=22):\\n log = logging.getLogger('device.remotecall')\\n ret = {}\\n \\\"\\\"\\\"Executar comando com a opcao de echo $? no final para\\n descobrir o valor do retorno da execucao do comando\\\"\\\"\\\"\\n cmd = 'ssh -p %s %s@%s' % (port, username, host)\\n cmd = shlex.split(cmd)\\n command = command + ' ; echo $?'\\n cmd.append(command)\\n log.info('COMMAND=%s', cmd)\\n try:\\n out = subprocess.check_output(cmd)\\n \\\"\\\"\\\"Necessario separar as linhas em lista, por causa\\n da compatilidade com a implementacao ssh anterior\\\"\\\"\\\"\\n out = out.splitlines(True)\\n \\\"\\\"\\\"Descartar ultima linha por causa da quebra de linha\\n do valor de retorno\\\"\\\"\\\"\\n ret['output'] = out[:-1]\\n ret['exit_code'] = int(out[-1])\\n except subprocess.CalledProcessError as e:\\n ret['output'] = e.output\\n ret['exit_code'] = int(e.returncode)\\n \\\"\\\"\\\"Forcar codificacao da string\\\"\\\"\\\"\\n ret['output'] = map(lambda x: unicode(x, 'utf-8'), ret['output'])\\n log.info('Return status [%s] command:%s', ret['exit_code'], command)\\n return ret\",\n \"def ip_command():\\n # 1. Get input host from Demisto\\n ip = demisto.args().get('ip')\\n if not is_ip_valid(ip):\\n return_error('Invalid IP address, Please retry with a valid IP address')\\n # 2. Get the host reputation from SlashNext API\\n response = ip_lookup(ip=ip)\\n if response.get('errorNo') != 0:\\n return\\n # 3. Parse and format the response\\n dbot_score_cont, ip_cont = get_dbot_std_context(\\n ip, 'IP', response.get('threatData').get('verdict'), response.get('threatData').get('threatType'))\\n\\n snx_ioc_cont = get_snx_host_ioc_context(ip, 'IP', response.get('threatData'))\\n\\n ec = {\\n 'SlashNext.IP(val.Value === obj.Value)': snx_ioc_cont,\\n 'DBotScore': dbot_score_cont,\\n 'IP': ip_cont\\n }\\n\\n title = 'SlashNext Phishing Incident Response - IP Lookup\\\\n' \\\\\\n '##### ip = {}'.format(ip)\\n\\n md = tableToMarkdown(\\n title,\\n snx_ioc_cont,\\n ['Value',\\n 'Type',\\n 'Verdict',\\n 'ThreatStatus',\\n 'ThreatName',\\n 'ThreatType',\\n 'FirstSeen',\\n 'LastSeen']\\n )\\n\\n return_outputs(md, ec, snx_ioc_cont)\",\n \"def network_info(self) -> dict:\\n \\n network_info_url = self.network + bf_network_informations_url\\n \\n response = query_blockfrost(network_info_url, self.api_key, self.proxies)\\n \\n return response\",\n \"def api_myip():\\n return request.remote_addr, 200, {'Content-Type': 'text/plain'}\",\n \"def remote_getSysinfo(self, request):\\r\\n # TODO : replace these calls with call to rce.util.sysinfo\\r\\n response_table = {\\r\\n 'size':self._size,\\r\\n 'cpu':self._cpu,\\r\\n 'memory': self._memeory,\\r\\n 'bandwidth': self._bandwidth,\\r\\n # 'keyword': some value or function to provide the data\\r\\n }\\r\\n\\r\\n return response_table[request]\",\n \"def local_info():\\n local('uname -a')\",\n \"def infocalypse_info(ui_, repo, **opts):\\n # FCP not required. Hmmm... Hack\\n opts['fcphost'] = ''\\n opts['fcpport'] = 0\\n params, stored_cfg = get_config_info(ui_, opts)\\n request_uri = opts['uri']\\n if request_uri == '':\\n request_uri = stored_cfg.get_request_uri(repo.root)\\n if not request_uri:\\n ui_.warn(\\\"There is no stored request URI for this repo.\\\\n\\\"\\n \\\"Please set one with the --uri option.\\\\n\\\")\\n return\\n\\n params['REQUEST_URI'] = request_uri\\n execute_info(ui_, repo, params, stored_cfg)\",\n \"def do_GET(self):\\n server_ip = Setup.parse_options()['ip_address']\\n uri = \\\"http://\\\" + server_ip + self.path\\n response = urllib.urlopen(uri)\\n self.copyfile(response, self.wfile)\\n headers = self.generate_header_dic(self.headers.headers)\\n ip_address = self.client_address[0] # get client iP address\\n if Setup.system_status != 'green':\\n self.process_request(ip_address, headers, self.path)\\n self.process_response(ip_address, response.headers)\",\n \"def test_get_network(self):\\n pass\",\n \"def execute_remote(ipaddr, command):\\n\\n\\treturn execute(['ssh', '-f', '-t', '-oPasswordAuthentication=no',\\n\\t\\t'-l', 'alt', ipaddr, command])\",\n \"def run(host, tasks, procsPerTask, memFracPerTask, outputLoc, primaryIP, allocation,\\n instance, localIP):\\n argStr = ' '.join(sys.argv[1:])\\n #argStr = ' '.join([str(i) for i in locals().values()]) # do not move this line.\\n #TODO: Determine logDir value.\\n logDir = \\\"\\\"\\n\\n #os.system(\\\"sleep 15\\\")\\n os.system(\\\"python {0} {1} &\\\".format(join(dirname(__file__), \\\"networking.py\\\"), argStr))\\n os.system(\\\"sleep 20\\\")\\n port = 13001\\n buf = 1024\\n tcpsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n m_addr = (primaryIP, port)\\n tcpsocket.connect(m_addr)\\n register_command = \\\"register\\\\t{0}\\\\t{1}\\\".format(host, tasks)\\n os.system(\\\"echo {0} >> {1}\\\".format(register_command, join(logDir, \\\"sent.log\\\")))\\n tcpsocket.send(register_command)\\n tcpsocket.close()\",\n \"def ping(host,hidden=True):\\r\\n\\r\\n # Ping command count option as function of OS\\r\\n param = '-n' if platform.system()=='Windows' else '-c'\\r\\n\\r\\n # Building the command. Ex: \\\"ping -c 1 google.com\\\"\\r\\n command = ['ping', param, '1', host]\\r\\n \\r\\n startupinfo = None\\r\\n if hidden:\\r\\n if os.name == 'nt':\\r\\n startupinfo = subprocess.STARTUPINFO()\\r\\n startupinfo.dwFlags |= subprocess._subprocess.STARTF_USESHOWWINDOW#subprocess.STARTF_USESHOWWINDOW\\r\\n #proc = subprocess.Popen(command, startupinfo=startupinfo) \\r\\n return subprocess.call(command, startupinfo=startupinfo) == 0\",\n \"def _request(self, method, api_url, **kwargs):\\n if not self.connected:\\n raise Exception(\\\"'{d}' is not connected for alias '{a}'\\\"\\n .format(d=self.device.name,\\n a=self.alias))\\n\\n # Deal with the dn\\n full_url = '{f}{api_url}'.format(f=self.url, api_url=api_url)\\n\\n if 'data' in kwargs:\\n payload = kwargs['data']\\n elif 'json' in kwargs:\\n payload = kwargs['json']\\n else:\\n payload = ''\\n\\n log.info(\\\"Sending {method} command to '{d}':\\\"\\n \\\"\\\\napi_url: {furl}\\\\nPayload:{payload}\\\"\\n .format(method=method,\\n d=self.device.name,\\n furl=full_url,\\n payload=payload))\\n\\n headers = {\\n 'Dcnm-Token': self.token,\\n 'Content-type': 'application/json'\\n }\\n\\n # Send to the device\\n response = self.session.request(method=method, url=full_url, headers=headers, **kwargs)\\n\\n\\n # Make sure it was successful\\n try:\\n response.raise_for_status()\\n except Exception:\\n raise RequestException(\\n \\\"'{c}' result code has been returned \\\"\\n \\\"for '{d}'.\\\\nResponse from server: \\\"\\n \\\"{r}\\\".format(d=self.device.name,\\n c=response.status_code,\\n r=response.text))\\n\\n # In case the response cannot be decoded into json\\n # warn and return the raw text\\n\\n try:\\n output = response.json()\\n if isinstance(output, list):\\n output = {\\\"temproot\\\": output}\\n except Exception:\\n log.warning('Could not decode json. Returning text!')\\n output = response.text\\n\\n return output\",\n \"def identify_num_simulate():\\r\\n # http / https ip:port\\r\\n proxy = {'http': 'http://112.85.165.113:9999'}\\r\\n response = requests.get(base_url + '/get', proxies=proxy)\\r\\n print(response.status_code)\\r\\n print(response.text)\",\n \"def get_session_info():\\n query = {\\\"type\\\": \\\"op\\\", \\\"cmd\\\": \\\"<show><session><info></info></session></show>\\\"}\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def get_net_details(self, net_name=\\\"dummy_net\\\", net_id=None):\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks\\\"\\n _headers = {'x-auth-token': self.project_info[\\\"token_project\\\"]}\\n _body = None\\n\\n result = self.request(\\\"GET\\\", _url, _headers, _body)\\n if result is None:\\n LOG_OBJ.error(\\\"No response from Server while listing the nets\\\")\\n return result.status\\n if result.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Get network Failed with status %s \\\" % result.status)\\n return result.status\\n output = json.loads(result.data)\\n\\n for nets in output['networks']:\\n if (net_id is not None and (nets['id'] == net_id)) or \\\\\\n nets['name'].lower() == net_name.lower():\\n LOG_OBJ.debug(\\\"Net details : %s \\\" % nets)\\n return nets\\n\\n LOG_OBJ.debug(\\\"Network with name:%s or with ID:%s is Not Found\\\" %\\n (net_name, net_id))\",\n \"def get_system_info(self):\\r\\n method = self.public_endpoints['system_info']['method']\\r\\n url = self.base_url + self.public_endpoints['system_info']['url']\\r\\n req = requests.request(method, url)\\r\\n res = req.json()\\r\\n\\r\\n if res['success'] == True:\\r\\n return res[\\\"result\\\"]\\r\\n else:\\r\\n return res\",\n \"async def run(self):\\n\\n result = {'start_timestamp': time()}\\n\\n ping = await create_subprocess_exec(\\\"/bin/ping\\\",\\n self.device,\\n \\\"-c \\\" + self.count,\\n \\\"-l \\\" + self.preload,\\n \\\"-W \\\" + self.timeout,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n stdout = await ping.stdout.read()\\n stderr = await ping.stderr.read()\\n\\n if stderr:\\n result['error'] = stderr.decode('utf-8').strip()\\n else:\\n lines = stdout.splitlines()\\n second_last_line = lines[len(lines)-2].decode('utf-8').split()\\n last_line = lines[len(lines)-1].decode('utf-8')\\n if not last_line:\\n # if the last line is empty\\n # none of the packets arrived\\n result['error'] = 'Host unreachable'\\n result['packets_sent'] = second_last_line[0]\\n result['packets_recv'] = second_last_line[3]\\n else:\\n last_line = last_line.split()[3].split('/')\\n result['min'] = last_line[0]\\n result['avg'] = last_line[1]\\n result['max'] = last_line[2]\\n result['mdev'] = last_line[3]\\n result['packets_sent'] = second_last_line[0]\\n result['packets_recv'] = second_last_line[3]\\n\\n result['end_timestamp'] = time()\\n self.results.append(result)\\n return result\",\n \"def RemoteCall(self, address, argDict):\\r\\n print(f\\\"Making RPC for {argDict[RPC_ARG_REQUEST_TYPE]} to {address}\\\")\\r\\n requestSocket = self.CreateAClientSocket(address)\\r\\n requestByteArray = pickle.dumps(argDict)\\r\\n requestSocket.sendall(requestByteArray)\\r\\n requestSocket.shutdown(socket.SHUT_WR)\\r\\n value = pickle.loads(requestSocket.recv(BUFFER_SIZE))\\r\\n requestSocket.shutdown(socket.SHUT_RD)\\r\\n requestSocket.close()\\r\\n return value\",\n \"def __call__(self) -> list:\\n return self.network\",\n \"def _get_ilo_access(remote_console):\\n url = remote_console.get('remoteConsoleUrl')\\n url_parse = parse.urlparse(url)\\n host_ip = parse.parse_qs(url_parse.netloc).get('addr')[0]\\n token = parse.parse_qs(url_parse.netloc).get('sessionkey')[0]\\n return host_ip, token\",\n \"def list_net(self):\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/networks\\\"\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.project_info[\\\"token_project\\\"]}\\n _body = None\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while listing the networks\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Get network list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n output = json.loads(response.data)\\n\\n LOG_OBJ.info(\\\"Network List : %s \\\" % output)\\n return output['networks']\",\n \"def executeRemoteCommand(self, data):\\n return self.session.request('diag/command/', 'POST',\\n self.getXML(data, 'remoteCommand'))\",\n \"def getNetworksDetails(network_id):\\n logger.debug('Start.')\\n code, res = rest_requests.get(networks_url + \\\"/\\\" + network_id)\\n if code != requests.codes.ok:\\n logger.error((code, res))\\n return None\\n return res[\\\"network\\\"]\",\n \"def info(self):\\n return requests.get(self.info_url + self.pid).json()\",\n \"def getProcessInfo(self, name):\\r\\n self._update('getProcessInfo')\\r\\n\\r\\n group, process = self._getGroupAndProcess(name)\\r\\n\\r\\n if process is None:\\r\\n raise RPCError(Faults.BAD_NAME, name)\\r\\n\\r\\n start = int(process.laststart)\\r\\n stop = int(process.laststop)\\r\\n now = int(time.time())\\r\\n\\r\\n state = process.get_state()\\r\\n spawnerr = process.spawnerr or ''\\r\\n exitstatus = process.exitstatus or 0\\r\\n stdout_logfile = process.config.stdout_logfile or ''\\r\\n stderr_logfile = process.config.stderr_logfile or ''\\r\\n\\r\\n info = {\\r\\n 'name':process.config.name,\\r\\n 'group':group.config.name,\\r\\n 'start':start,\\r\\n 'stop':stop,\\r\\n 'now':now,\\r\\n 'state':state,\\r\\n 'statename':getProcessStateDescription(state),\\r\\n 'spawnerr':spawnerr,\\r\\n 'exitstatus':exitstatus,\\r\\n 'logfile':stdout_logfile, # b/c alias\\r\\n 'stdout_logfile':stdout_logfile,\\r\\n 'stderr_logfile':stderr_logfile,\\r\\n 'pid':process.pid,\\r\\n }\\r\\n\\r\\n description = self._interpretProcessInfo(info)\\r\\n info['description'] = description\\r\\n return info\",\n \"def getIpTcp(self):\\n\\t\\tquery = ''\\n\\t\\tconn = self.get_connection()\\n\\t\\theaders = { 'Content-type' : 'application/json', 'Authorization' : 'A10 %s' %self.sessionid}\\n\\t\\tconn.request('GET', self.get_path() + '/' + query, headers=headers)\\n\\t\\tresponse = conn.getresponse()\\n\\t\\texpected_status = 200\\n\\t\\terrors = {500: 'An unexpected runtime exception', 404: 'Specified tcp does not exist'}\\n\\t\\tpayload = self.get_output(response, expected_status, errors)\\n\\t\\tconn.close()\\n\\t\\tif self.debug:\\n\\t\\t\\tprint 'payload:', payload\\n\\t\\tif payload == '':\\n\\t\\t\\tpayload = None\\n\\t\\tif payload is not None:\\n\\t\\t\\tdata = json.loads(payload)\\n\\t\\t\\tpayload= data.get('tcp')\\n\\t\\treturn deserialize_Tcp_json(payload)\",\n \"def _getvpn(node, cliargs=CliArg()):\\n header = 'http://www.vpngate.net'\\n site = 'http://'+node._vpn\\n try:\\n response = urllib2.urlopen(site)\\n data = response.read()\\n except:\\n print(\\\" unable to establish network connection.\\\\\\n \\\\n .....\\\\\\n \\\\n check network connectivity and site status.\\\")\\n sys.exit(1)\\n vpn = None\\n vpn_ = []\\n \\\"find desired protocol\\\"\\n if cliargs._proto in 'udp':\\n vpns_ = re.findall(r'(/common/openvpn[^\\\\'\\\" >]+)', data)\\n for x in range(len(vpns_)):\\n if str(vpns_[x]).find('udp') >= 0:\\n vpn_.append(vpns_[x])\\n else:\\n vpns_ = re.findall(r'(/common/openvpn[^\\\\'\\\" >]+)', data)\\n for x in range(len(vpns_)):\\n if str(vpns_[x]).find('tcp') >= 0:\\n vpn_.append(vpns_[x])\\n if vpn_:\\n \\\"remove garbage\\\"\\n vpn = vpn_[0]\\n pos = vpn.find('amp;')\\n while pos is not -1:\\n vpn = vpn[:pos]+vpn[pos+4:]\\n pos = vpn.find('amp;')\\n if vpn:\\n return header + vpn\\n return None\",\n \"def network(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"network\\\")\",\n \"def get_net_info(net_env):\\n\\n # check the shared dir first. it could force us into local mode\\n shared_dir = os.getenv(SHARED_DIR_ENV)\\n if shared_dir is None:\\n # if no shared directory is set, we MUST operate in local mode\\n print \\\"No shared directory set. Defaulting to local mode\\\"\\n os.environ[SHARED_DIR_ENV] = SHARED_DIR_DEFAULT\\n elif shared_dir == SHARED_DIR_DEFAULT\\n pass\\n else:\\n net_string = os.environ.get(net_env)\\n\\n if net_string:\\n host, port = validate(net_string)\\n return host, port\\n\\n # finally default to local mode\\n net_string = DEFAULT_NET[net_env]\\n\\n if net_string:\\n return validate(net_string)\",\n \"def run_info ( run_num ) : \\n global _rinfos_\\n rinfo = _rinfos_.get ( run_num , None )\\n if rinfo : return rinfo \\n \\n try :\\n \\n #\\n url = run_url.format ( run_num )\\n _obj = urllib.urlopen ( url )\\n rinfo = json.load ( _obj )\\n\\n rinfo = rinfo if rinfo else None\\n _rinfos_ [ run_num ] = rinfo \\n return rinfo\\n \\n except:\\n return None \\n\\n return None\",\n \"def ShowTaskIPC(cmd_args=None):\\n if not cmd_args:\\n print \\\"No arguments passed\\\"\\n print ShowTaskIPC.__doc__\\n return False\\n tval = kern.GetValueFromAddress(cmd_args[0], 'task *')\\n if not tval:\\n print \\\"unknown arguments:\\\", str(cmd_args)\\n return False\\n print GetTaskSummary.header + \\\" \\\" + GetProcSummary.header\\n pval = Cast(tval.bsd_info, 'proc *')\\n print GetTaskSummary(tval) + \\\" \\\" + GetProcSummary(pval)\\n print GetTaskBusyIPCSummary.header\\n (summary, table_size, nbusy, nmsgs) = GetTaskBusyIPCSummary(tval)\\n print summary\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6053576","0.60060537","0.5726204","0.56940055","0.5621628","0.5613466","0.54797566","0.544631","0.5397861","0.5345061","0.5319306","0.5302995","0.5250394","0.5229157","0.52221423","0.51966923","0.51758206","0.513977","0.5130418","0.51230186","0.51097083","0.5099621","0.5071112","0.5063016","0.50587386","0.5038513","0.5028574","0.5021097","0.5003183","0.50023365","0.50003403","0.49820563","0.49726373","0.49659047","0.49627742","0.4952671","0.4948951","0.49360332","0.49359617","0.49336106","0.4931653","0.49292397","0.4925899","0.49222335","0.49147162","0.49053156","0.48816648","0.48654896","0.48653114","0.486295","0.4861304","0.48595127","0.4858134","0.48581278","0.4857656","0.48509413","0.48386958","0.4830149","0.48272398","0.48268622","0.48174986","0.48159692","0.48085773","0.4807326","0.47983342","0.47981918","0.47917637","0.47908017","0.4788619","0.47799718","0.47724983","0.4771225","0.47669986","0.4764989","0.4764493","0.47644314","0.47613797","0.47610456","0.4759858","0.47557336","0.47532883","0.4752956","0.47476155","0.47465786","0.47454143","0.47382447","0.47361323","0.47348607","0.47325426","0.47282416","0.47233608","0.4708478","0.47049028","0.4703503","0.4702762","0.46986303","0.46890062","0.4686163","0.46843198","0.46821007","0.46817687"],"string":"[\n \"0.6053576\",\n \"0.60060537\",\n \"0.5726204\",\n \"0.56940055\",\n \"0.5621628\",\n \"0.5613466\",\n \"0.54797566\",\n \"0.544631\",\n \"0.5397861\",\n \"0.5345061\",\n \"0.5319306\",\n \"0.5302995\",\n \"0.5250394\",\n \"0.5229157\",\n \"0.52221423\",\n \"0.51966923\",\n \"0.51758206\",\n \"0.513977\",\n \"0.5130418\",\n \"0.51230186\",\n \"0.51097083\",\n \"0.5099621\",\n \"0.5071112\",\n \"0.5063016\",\n \"0.50587386\",\n \"0.5038513\",\n \"0.5028574\",\n \"0.5021097\",\n \"0.5003183\",\n \"0.50023365\",\n \"0.50003403\",\n \"0.49820563\",\n \"0.49726373\",\n \"0.49659047\",\n \"0.49627742\",\n \"0.4952671\",\n \"0.4948951\",\n \"0.49360332\",\n \"0.49359617\",\n \"0.49336106\",\n \"0.4931653\",\n \"0.49292397\",\n \"0.4925899\",\n \"0.49222335\",\n \"0.49147162\",\n \"0.49053156\",\n \"0.48816648\",\n \"0.48654896\",\n \"0.48653114\",\n \"0.486295\",\n \"0.4861304\",\n \"0.48595127\",\n \"0.4858134\",\n \"0.48581278\",\n \"0.4857656\",\n \"0.48509413\",\n \"0.48386958\",\n \"0.4830149\",\n \"0.48272398\",\n \"0.48268622\",\n \"0.48174986\",\n \"0.48159692\",\n \"0.48085773\",\n \"0.4807326\",\n \"0.47983342\",\n \"0.47981918\",\n \"0.47917637\",\n \"0.47908017\",\n \"0.4788619\",\n \"0.47799718\",\n \"0.47724983\",\n \"0.4771225\",\n \"0.47669986\",\n \"0.4764989\",\n \"0.4764493\",\n \"0.47644314\",\n \"0.47613797\",\n \"0.47610456\",\n \"0.4759858\",\n \"0.47557336\",\n \"0.47532883\",\n \"0.4752956\",\n \"0.47476155\",\n \"0.47465786\",\n \"0.47454143\",\n \"0.47382447\",\n \"0.47361323\",\n \"0.47348607\",\n \"0.47325426\",\n \"0.47282416\",\n \"0.47233608\",\n \"0.4708478\",\n \"0.47049028\",\n \"0.4703503\",\n \"0.4702762\",\n \"0.46986303\",\n \"0.46890062\",\n \"0.4686163\",\n \"0.46843198\",\n \"0.46821007\",\n \"0.46817687\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79799,"cells":{"query":{"kind":"string","value":"Make a remote process call to create the dhcp port."},"document":{"kind":"string","value":"def get_dhcp_port(self, network_id, device_id):\n return DictModel(self.call(self.context,\n self.make_msg('get_dhcp_port',\n network_id=network_id,\n device_id=device_id,\n host=self.host),\n topic=self.topic))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_dhcp_port(self, port):\n LOG.debug(\"create_dhcp_port: %s\", port)\n port['port']['id'] = port['port']['network_id']\n\n # The following MAC address will be assigned to the Linux dummy\n # interface that\n # networking_calico.agent.linux.interface.RoutedInterfaceDriver\n # creates. Therefore it will never actually be used or involved in the\n # sending or receiving of any real data. Hence it should not matter\n # that we use a hardcoded value here, and the same value on every\n # networking-calico compute host. The '2' bit of the first byte means\n # 'locally administered', which makes sense for a hardcoded value like\n # this and distinguishes it from the space of managed MAC addresses.\n port['port']['mac_address'] = '02:00:00:00:00:00'\n port['port']['device_owner'] = constants.DEVICE_OWNER_DHCP\n return dhcp.DictModel(port['port'])","async def create(context, name_or_id,\n neutronclient,\n network_id,\n subnet_id=None,\n ip_addresses=None,\n admin_state_up=True,\n security_groups=None,\n use_existing=False):\n\n if not use_existing:\n port_dict = {\n 'port': {\n 'admin_state_up': admin_state_up,\n 'name': name_or_id,\n 'network_id': network_id,\n }\n }\n fixed_ips = []\n if subnet_id:\n subnet = {'subnet_id': subnet_id}\n if ip_addresses:\n subnet.update({'ip_address': ip_addresses})\n fixed_ips.append(subnet)\n port_dict['port']['fixed_ips'] = fixed_ips\n if security_groups:\n port_dict['port']['security_groups'] = security_groups\n\n context.logger.info('Creating port with identifiers: {0}'\n .format(str(port_dict)))\n port = neutronclient.create_port(body=port_dict)\n\n else:\n context.logger.info('Using existing port \"{0}\".'\n .format(name_or_id))\n port = neutronclient.show_port(name_or_id)\n\n return port['port']","def launch (no_flow = False,\n network = \"192.168.0.0/24\", # Address range\n first = 1, last = None, count = None, # Address range\n ip = \"192.168.0.254\",\n router = (), # Auto\n dns = (), # Auto\n dpid = None, # All\n ports = None, # All\n __INSTANCE__ = None):\n def fixint (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n return int(i)\n def fix (i):\n i = str(i)\n if i.lower() == \"none\": return None\n if i.lower() == \"true\": return None\n if i == '()': return ()\n return i\n first,last,count = map(fixint,(first,last,count))\n router,dns = map(fix,(router,dns))\n\n if ports is not None:\n ports = ports.split(\",\")\n ports = set(int(p) if p.isdigit() else p for p in ports)\n\n pool = SimpleAddressPool(network = network, first = first, last = last,\n count = count)\n\n inst = DHCPD(install_flow = not no_flow, pool = pool,\n ip_address = ip, router_address = router,\n dns_address = dns, dpid = dpid, ports = ports)\n\n if __INSTANCE__[0] == 0:\n # First or only instance\n core.register(inst)\n\n log.debug(\"DHCP serving a%s\", str(pool)[2:-1])","def create_command(self, on_or_off: bool, port: int):\n return self.power_strip.create_command(port, on_or_off)","def port_create(request, network_id, **kwargs):\n LOG.debug(\"port_create(): netid=%(network_id)s, kwargs=%(kwargs)s\",\n {'network_id': network_id, 'kwargs': kwargs})\n kwargs = unescape_port_kwargs(**kwargs)\n body = {'port': {'network_id': network_id}}\n if 'tenant_id' not in kwargs:\n kwargs['tenant_id'] = request.user.project_id\n body['port'].update(kwargs)\n port = neutronclient(request).create_port(body=body).get('port')\n return Port(port)","def cmd_port(args):","def create(self, name, network_id, fixed_ips, host_id=None, profile=None,\n vnic_type=None, device_owner=None, device_id=None, \n security_groups=None, mac_address=None, tenant_id=None):\n data = {\n \"port\": {\n \"network_id\": network_id,\n \"name\": name,\n \"admin_state_up\": True,\n \"fixed_ips\": fixed_ips,\n }\n }\n if tenant_id is not None:\n data[u'port'][u'tenant_id'] = tenant_id\n if host_id is not None:\n data['port']['binding:host_id'] = host_id\n if profile is not None:\n data['port']['binding:profile'] = profile\n if host_id is not None:\n data['port']['binding:vnic_type'] = vnic_type\n if device_owner is not None:\n data['port']['device_owner'] = device_owner\n if device_id is not None:\n data['port']['device_id'] = device_id\n if security_groups is not None:\n data['port']['security_groups'] = security_groups\n if mac_address is not None:\n data[u'allowed_address_pairs'] = [{u'mac_address':mac_address,\n u'ip_address':fixed_ips[0][u'ip_address']}]\n\n path = '%s/ports' % self.ver\n res = self.client.call(path, 'POST', data=json.dumps(data), \n token=self.manager.identity.token)\n self.logger.debug('Create openstack port: %s' % truncate(res))\n return res[0]['port']","def create_dhcp_relay(self, iface_name='global', server_ip=None, fwd_iface_name=None):\n pass","def create_port(self, context, port):\n LOG.debug(_(\"NeutronRestProxyV2: create_port() called\"))\n\n # Update DB in new session so exceptions rollback changes\n with context.session.begin(subtransactions=True):\n port[\"port\"][\"admin_state_up\"] = False\n dhcp_opts = port['port'].get(edo_ext.EXTRADHCPOPTS, [])\n new_port = super(NeutronRestProxyV2, self).create_port(context,\n port)\n if (portbindings.HOST_ID in port['port']\n and 'id' in new_port):\n host_id = port['port'][portbindings.HOST_ID]\n porttracker_db.put_port_hostid(context, new_port['id'],\n host_id)\n self._process_port_create_extra_dhcp_opts(context, new_port,\n dhcp_opts)\n new_port = self._extend_port_dict_binding(context, new_port)\n net = super(NeutronRestProxyV2,\n self).get_network(context, new_port[\"network_id\"])\n\n if self.add_meta_server_route:\n if new_port['device_owner'] == 'network:dhcp':\n destination = METADATA_SERVER_IP + '/32'\n self._add_host_route(context, destination, new_port)\n\n # create on network ctrl\n mapped_port = self._map_state_and_status(new_port)\n self.servers.rest_create_port(net, mapped_port)\n\n # connect device to network, if present\n device_id = port[\"port\"].get(\"device_id\")\n if device_id:\n try:\n self.servers.rest_plug_interface(net[\"tenant_id\"], net[\"id\"],\n new_port, device_id)\n except RemoteRestError:\n with excutils.save_and_reraise_exception():\n port_update = {\"port\": {\"status\": \"ERROR\"}}\n super(NeutronRestProxyV2, self).update_port(\n context,\n new_port[\"id\"],\n port_update\n )\n # Set port state up and return that port\n port_update = {\"port\": {\"admin_state_up\": True}}\n new_port = super(NeutronRestProxyV2, self).update_port(context,\n new_port[\"id\"],\n port_update)\n return self._extend_port_dict_binding(context, new_port)","def create_port(self, body=None):\r\n return self.post(self.ports_path, body=body)","def release_dhcp_port(self, network_id, device_id):\n LOG.debug(\"release_dhcp_port: %s %s\", network_id, device_id)","def remote_createTunnel(self, name, targetIP):\r\n if name not in self._bridges:\r\n raise InternalError('Bridge does not exist.')\r\n\r\n key = (name, targetIP)\r\n\r\n if key in self._uid:\r\n raise InternalError('Tunnel already exists.')\r\n\r\n while 1:\r\n uid = randomString(self._UID_LEN)\r\n\r\n if uid not in self._uid.itervalues():\r\n break\r\n\r\n self._uid[key] = uid\r\n port = 'gre-{0}'.format(uid)\r\n\r\n return execute(('/usr/bin/ovs-vsctl', 'add-port', 'br-{0}'.format(name),\r\n port, '--', 'set', 'interface', port, 'type=gre',\r\n 'options:remote_ip={0}'.format(targetIP)),\r\n reactor=self._reactor)","def create(self):\n\t\treturn handle_to_object(call_sdk_function('PrlPortFwd_Create'))","def dhcp(self, dhcp):\n\n self._dhcp = dhcp","def create(clients, context, name='test port'):\n network_id = context['network_id']\n logger.info(\"Taking action port.create_for_server{}.\".format(name))\n neutron = clients.get_neutron()\n body = {\n \"port\": {\n \"network_id\": network_id,\n \"name\": name\n }\n }\n resp = neutron.create_port(body=body)\n port = resp['port']\n context['port_id'] = port['id']\n context.setdefault('stack', []).append({'port_id': port['id']})","def configureDHCP():\n dhcpStart = config.get(\"hotspot\", \"dhcpstart\")\n dhcpEnd = config.get(\"hotspot\", \"dhcpend\")\n dnsmasqConfig = f\"\"\"#PI Hotspot config\ndomain-needed\nbogus-priv\ndhcp-option=option:dns-server\ndhcp-authoritative\ndhcp-range={dhcpStart},{dhcpEnd},1h\n\"\"\"\n confFile = open(\"/etc/dnsmasq.conf\", \"w\")\n confFile.write(dnsmasqConfig)\n confFile.close()","def setup_dhcp_env(device):\n raise NotImplementedError","def create_port(self, context, port):\n LOG.debug(_(\"QuantumRestProxyV2: create_port() called\"))\n\n # Update DB\n port[\"port\"][\"admin_state_up\"] = False\n new_port = super(QuantumRestProxyV2, self).create_port(context, port)\n net = super(QuantumRestProxyV2,\n self).get_network(context, new_port[\"network_id\"])\n\n # create on networl ctrl\n try:\n resource = PORT_RESOURCE_PATH % (net[\"tenant_id\"], net[\"id\"])\n data = {\n \"port\": {\n \"id\": new_port[\"id\"],\n \"state\": \"ACTIVE\",\n }\n }\n ret = self.servers.post(resource, data)\n if not self.servers.action_success(ret):\n raise RemoteRestError(ret[2])\n\n # connect device to network, if present\n if port[\"port\"].get(\"device_id\"):\n self._plug_interface(context,\n net[\"tenant_id\"], net[\"id\"],\n new_port[\"id\"], new_port[\"id\"] + \"00\")\n except RemoteRestError as e:\n LOG.error(_(\"QuantumRestProxyV2: Unable to create remote port: \"\n \"%s\"), e.message)\n super(QuantumRestProxyV2, self).delete_port(context,\n new_port[\"id\"])\n raise\n\n # Set port state up and return that port\n port_update = {\"port\": {\"admin_state_up\": True}}\n return super(QuantumRestProxyV2, self).update_port(context,\n new_port[\"id\"],\n port_update)","def create_relay08_cmd(port_id, stime=0.5, comport='/dev/ttyUSB4'):\n with open(\"runner/operation_lib/host_passwd\") as pwdfp:\n host_passwd = pwdfp.read()\n\n cmd = 'echo '+host_passwd+' | sudo -S python runner/operation_lib/base_lib/relay08_ctrl_button.py'\n if port_id in range(1, 9):\n cmd = cmd + ' -p ' + str(port_id)\n else:\n print '******please input correct port_id which in (1~8)********'\n sys.exit()\n\n if not stime == 0.5:\n cmd = cmd + ' -t ' + str(stime)\n\n if not comport == '/dev/ttyUSB4':\n cmd = cmd + ' -c ' + str(comport)\n os.system(cmd)","def release_dhcp_port(self, network_id, device_id):\n return self.call(self.context,\n self.make_msg('release_dhcp_port',\n network_id=network_id,\n device_id=device_id,\n host=self.host),\n topic=self.topic)","def start_srv(start, process):\n if not \"conf_option\" in world.cfg:\n world.cfg[\"conf_option\"] = \"\"\n\n world.cfg['log_file'] = build_log_path()\n fabric_sudo_command('cat /dev/null >' + world.cfg['log_file'])\n world.cfg[\"dhcp_log_file\"] = world.cfg['log_file']\n\n log = \"local7\"\n if world.f_cfg.isc_dhcp_log_facility != \"\":\n log = world.f_cfg.isc_dhcp_log_facility\n\n world.cfg['log_facility'] = '''\\nlog-facility {log};\\n'''.format(**locals())\n\n add_defaults()\n cfg_write()\n log.debug(\"Start ISC-DHCP with generated config:\")\n convert_cfg_file(world.cfg[\"cfg_file\"])\n fabric_send_file(world.cfg[\"cfg_file\"] + '_processed', world.cfg[\"cfg_file\"] + '_processed')\n copy_configuration_file(world.cfg[\"cfg_file\"] + '_processed')\n remove_local_file(world.cfg[\"cfg_file\"])\n #set_ethernet_interface()\n stop_srv()\n\n world.cfg['leases'] = build_leases_path()\n\n #fabric_sudo_command('echo y |rm ' + world.cfg['leases'])\n fabric_sudo_command('touch ' + world.cfg['leases'])\n\n result = fabric_sudo_command('(' + os.path.join(world.f_cfg.software_install_path, 'sbin/dhcpd') + ' -cf server.cfg_processed'\n + ' -lf ' + world.cfg['leases']\n + '&); sleep ' + str(world.f_cfg.sleep_time_1) + ';')\n\n check_process_result(start, result, process)\n\n # clear configs in case we would like make couple configs in one test\n world.cfg[\"conf_time\"] = \"\"\n world.cfg[\"log_facility\"] = \"\"\n world.cfg[\"custom_lines\"] = \"\"\n world.cfg[\"conf_option\"] = \"\"\n world.cfg[\"conf_vendor\"] = \"\"","async def test_dhcp(hass: HomeAssistant) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN,\n data=dhcp.DhcpServiceInfo(\n ip=\"1.2.3.4\", macaddress=\"01:23:45:67:89:ab\", hostname=\"mock_hostname\"\n ),\n context={\"source\": config_entries.SOURCE_DHCP},\n )\n\n assert result.get(\"type\") == FlowResultType.FORM\n assert result.get(\"step_id\") == \"user\"","def dhcp_callback(self, state, target_mac=None, target_ip=None, exception=None):\n self.record_result('dhcp', info=target_mac, ip=target_ip, state=state, exception=exception)\n self.target_mac = target_mac\n self.target_ip = target_ip\n if exception:\n self._state_transition(_STATE.ERROR, _STATE.DHCP)\n self.runner.target_set_error(self.port_set, exception)\n else:\n self._state_transition(_STATE.BASE, _STATE.DHCP)","def allocate_hosting_port(self, context, router_id, port_db, network_type,\n hosting_device_id):\n pass","def create_host(self, conf, tenant_id, network_id, params):\n\t\tpass","def _create_router_port(self, method, api, header, data):\n self._execute_api(method, api, header, data)","def _NewConnection(self, *args, **kwargs):\n return Connection(*args, dhcp_method=Connection.DHCP_DHCPCD, **kwargs)","def _gen_cmd(cmd, address):\n family = {4: 'inet', 6: 'inet6'}[address[0].version]\n args = ['addr', cmd, '%s/%s' % (address[0], address[1])]\n if family == 'inet' and cmd == 'add':\n args += ['brd', '+']\n args += ['dev', real_ifname]\n if family == 'inet6':\n args = ['-6'] + args\n return args","def create(openstack_resource):\n # Update port config before create port\n _update_port_config(openstack_resource.config)\n\n # Create port\n created_resource = openstack_resource.create()\n ipv4_list, ipv6_list = _get_fixed_ips_from_port(created_resource)\n fixed_ips = ipv4_list + ipv6_list\n _export_ips_to_port_instance(ipv4_list, ipv6_list)\n\n # Handle runtime properties\n update_runtime_properties(\n {\n RESOURCE_ID: created_resource.id,\n 'fixed_ips': fixed_ips,\n 'mac_address': created_resource.mac_address,\n 'allowed_address_pairs': created_resource.allowed_address_pairs,\n }\n )","def process_create_port(self, context, port, result):\n\n # Process extension data\n port_ext = self._create_port_ext(result, port, context=context)\n switchports = self._update_switchports(result, port,\n session=context.session)\n self._find_port_dict_extensions(result, None, port_ext=port_ext,\n switchports=switchports,\n session=context.session)\n\n # Validate we can actually configure this port\n if result[\"commit\"]:\n self._validate_port_can_commit(result, None,\n session=context.session)","def _NewConnection(self, *args, **kwargs):\n kwargs.setdefault('dhcp_method', Connection.DHCP_DHCPCD)\n\n return Connection(*args, **kwargs)","def setup_dhcp_config(self, board_config):\n raise NotImplementedError","def create_test_port(**kw):\n port = get_test_port(**kw)\n # Let DB generate ID if it isn't specified explicitly\n if 'id' not in kw:\n del port['id']\n dbapi = db_api.get_instance()\n return dbapi.create_port(port)","def create_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.create_port(port)\n except:\n pass","def create_dhcp_pool(options, vsm_obj, range, default_gateway):\n edge = Edge(vsm_obj, '4.0')\n edge_id = get_edge(vsm_obj)\n edge.id = edge_id\n\n dhcp_py_dict = {\n 'enabled': True,\n 'logging': {'loglevel': 'info', 'enable': False},\n 'ippools': [\n {\n 'autoconfiguredns': True,\n 'defaultGateway': default_gateway,\n 'iprange': range,\n }\n ],\n }\n dhcp_client = DHCP(edge)\n print(\"Creating dhcp ippool with range %s\" % range)\n dhcp_schema_object = dhcp_client.get_schema_object(dhcp_py_dict)\n existing_dhcp_schema = dhcp_client.read()\n if existing_dhcp_schema and existing_dhcp_schema.ipPools:\n print \"append dhcp ippool to existing list\"\n dhcp_schema_object.ipPools = existing_dhcp_schema.ipPools + \\\n dhcp_schema_object.ipPools\n result = dhcp_client.create(dhcp_schema_object)\n\n if (result[0].response.status != 204):\n r_vars = vars(result[0])\n print(\"Create IP Pool error: %s\" % result[0].response.reason)\n print ', '.join(\"%s: %s\" % item for item in r_vars.items())\n return False\n return True","def start_daemon(*,\n port: int=0,\n udp_port: int=0,\n websocket_port: int=0,\n label: str,\n method: str='npx',\n verbose: int=0,\n host: str='',\n public_url: str='',\n noudp: bool=False,\n isbootstrap: bool=False,\n nomulticast: bool=False,\n static_config: str='',\n node_arg: List[str]=[]\n):\n from kachery_p2p import __version__\n\n if _probe_daemon() is not None:\n raise Exception('Cannot start daemon. Already running.')\n\n api_port = _api_port()\n api_host = _api_host()\n config_dir = os.getenv('KACHERY_P2P_CONFIG_DIR', f'{pathlib.Path.home()}/.kachery-p2p')\n\n start_args = []\n if isbootstrap:\n start_args.append(f'--isbootstrap')\n if noudp:\n start_args.append(f'--noudp')\n if nomulticast:\n start_args.append(f'--nomulticast')\n start_args.append(f'--verbose {verbose}')\n if host:\n start_args.append(f'--host {host}')\n if public_url:\n start_args.append(f'--public-url {public_url}')\n if websocket_port > 0:\n start_args.append(f'--websocket-port {websocket_port}')\n if udp_port is not None:\n start_args.append(f'--udp-port {udp_port}')\n if static_config:\n start_args.append(f'--static-config {static_config}')\n start_args.append(f'--label {label}')\n start_args.append(f'--http-port {port}')\n\n assert method in ['npx', 'dev'], f'Invalid method for start_daemon: {method}'\n\n thisdir = os.path.dirname(os.path.realpath(__file__))\n if method == 'npx':\n try:\n subprocess.check_call(['npx', 'check-node-version', '--print', '--node', '>=12'])\n except:\n raise Exception('Please install nodejs version >=12. This is required in order to run kachery-p2p-daemon.')\n \n \n for na in node_arg:\n start_args.append(f'--node-arg={na}')\n\n npm_package = f'{thisdir}/kachery-p2p-daemon-0.7.1.tgz'\n if not os.path.exists(npm_package):\n raise Exception(f'No such file: {npm_package}')\n \n ss = ShellScript(f'''\n #!/bin/bash\n set -ex\n\n export KACHERY_P2P_API_PORT=\"{api_port}\"\n export KACHERY_P2P_API_HOST=\"{api_host}\"\n export KACHERY_P2P_CONFIG_DIR=\"{config_dir}\"\n npm install -g -y {npm_package}\n exec kachery-p2p-daemon start {' '.join(start_args)}\n ''')\n ss.start()\n try:\n retcode = ss.wait()\n finally:\n ss.stop()\n ss.kill()\n elif method == 'dev':\n ss = ShellScript(f'''\n #!/bin/bash\n set -ex\n\n export KACHERY_P2P_API_PORT=\"{api_port}\"\n export KACHERY_P2P_API_HOST=\"{api_host}\"\n export KACHERY_P2P_CONFIG_DIR=\"{config_dir}\"\n cd {thisdir}/../daemon\n # exec node_modules/ts-node/dist/bin.js {' '.join(node_arg)} ./src/cli.ts start {' '.join(start_args)}\n exec node {' '.join(node_arg)} -r ts-node/register ./src/cli.ts start {' '.join(start_args)}\n ''')\n ss.start()\n try:\n ss.wait()\n finally:\n ss.stop()\n ss.kill()\n else:\n raise Exception(f'Invalid method for starting daemon: {method}')","def start(self, ifname = None):\n \n if not self._slave_dhcp_process is None:\n raise Exception('DhcpClientAlreadyStarted')\n \n if not ifname is None:\n self._ifname = ifname\n \n if self._ifname is None:\n raise Exception('NoInterfaceProvided')\n \n self._slave_dhcp_process = SlaveDhcpClientProcess(dhcp_client_daemon_exec_path=self._dhcp_client_daemon_exec_path, ifname=self._ifname, logger=logger)\n self._slave_dhcp_process.start()\n self._new_lease_event.clear()\n self._dhcp_client_ctrl = RemoteDhcpClientControl(ifname=self._ifname) # Create a RemoteDhcpClientControl object that symbolizes the control on the remote process (over D-Bus)\n self._dhcp_client_ctrl.notifyNewLease(self._got_new_lease) # Ask underlying RemoteDhcpClientControl object to call self._new_lease_retrieved() as soon as we get a new lease \n logger.debug('DHCP client started on ' + self._ifname)\n slave_pid = self._dhcp_client_ctrl.getRemotePid()\n if slave_pid is None:\n logger.warn('Could not get remote process PID')\n raise('RemoteCommunicationError')\n else:\n logger.debug('Slave has PID ' + str(slave_pid)) \n self._slave_dhcp_process.addSlavePid(slave_pid)\n\n self._dhcp_client_ctrl.sendDiscover()","def test_port_create_with_binding_and_no_subnets(self):\n with self.network() as network:\n segment = self._test_create_segment(\n network_id=network['network']['id'],\n physical_network='physnet',\n network_type=constants.TYPE_VLAN)\n\n # Map the host to the segment\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n\n # No subnets, so no allocation. But, it shouldn't be an error.\n self.assertEqual(0, len(res['port']['fixed_ips']))","def port_cmd(self):\n print_debug(\"Executing PORT\")\n # PORT creates a new connection from server to client.\n sock = new_socket()\n self.port_connection(sock)\n # Get required parameters for PORT command.\n port_params, host_ip, host_port = self.parse_port_req(sock)\n print_debug(\"PARAMS: \" + port_params)\n command = \"PORT %s\\r\\n\" % port_params\n msg_rec = self.send_and_log(self.s, command)\n print_debug(msg_rec)\n return msg_rec, sock","def _RunDHCPCD(self, **kwargs):\n del kwargs\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\n interface=self.interface)\n # -K: Don't receive link messages for carrier status. You should\n # only have to use this with buggy device drivers or running\n # dhcpcd through a network manager.\n # -c: Location to the hooks file. If the default location happens to be\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\n timeout=self._dhcp_timeout,\n interface=self.interface)\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\n timeout=self._dhcp_timeout,\n cmd=dhcp_command)\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\n\n logging.info('Killing any existing dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n logging.info('Clearing any existing ifconfig networks...')\n self._device.Call(clear_ifconfig_command)\n\n logging.info('Starting dhcpcd...')\n self._device.CheckCall(dhcp_timeout_command)\n\n logging.info('Verifying IP address...')\n ip = self._LeasedIP()\n if not ip:\n self._device.Call(force_kill_command)\n raise WiFiError('DHCP bind failed')\n logging.info('Success: bound to IP %s', ip)\n\n yield ip # We have bound an IP; yield back to the caller.\n\n logging.info('Killing any remaining dhcpcd processes...')\n self._device.Call(force_kill_command)\n\n yield # We have released the IP.","def _send_port_command(self, host: str, port: int):\n hbytes = host.split('.')\n pbytes = [repr(port//256), repr(port%256)]\n cmd = \"PORT \" + \",\".join(hbytes + pbytes) + CRLF\n response = self.send_cmd(cmd.encode())\n return response","def main():\n\n args = TrafficScriptArg(['tx_src_ip', 'tx_dst_ip'])\n\n tx_if = args.get_arg('tx_if')\n rx_if = args.get_arg('rx_if')\n\n rxq = RxQueue(rx_if)\n txq = TxQueue(tx_if)\n\n tx_src_ip = args.get_arg('tx_src_ip')\n tx_dst_ip = args.get_arg('tx_dst_ip')\n\n sent_packets = []\n\n dhcp_discover = Ether(dst=\"ff:ff:ff:ff:ff:ff\") / \\\n IP(src=tx_src_ip, dst=tx_dst_ip) / \\\n UDP(sport=UDP_SERVICES.bootpc, dport=UDP_SERVICES.bootps) / \\\n BOOTP(op=1,) / \\\n DHCP(options=[(\"message-type\", \"discover\"),\n \"end\"])\n\n sent_packets.append(dhcp_discover)\n txq.send(dhcp_discover)\n\n for _ in range(10):\n dhcp_discover = rxq.recv(2)\n if is_discover(dhcp_discover):\n break\n else:\n raise RuntimeError(\"DHCP DISCOVER Rx timeout\")\n\n sys.exit(0)","def create_port_forward_rule(self, ipaddressid, protocol, virtualmachineid,\n privateport, privateendport,\n publicport, publicendport): \n params = {'command':'createPortForwardingRule',\n 'ipaddressid':ipaddressid,\n 'protocol':protocol,\n 'privateport':privateport,\n 'privateendport':privateendport,\n 'publicport':publicport,\n 'publicendport':publicendport,\n 'virtualmachineid':virtualmachineid,\n 'openfirewall':False} \n\n try:\n response = self.send_request(params)\n res = json.loads(response)\n clsk_job_id = res['createportforwardingruleresponse']['jobid']\n self.logger.debug('Start job - createPortForwardingRule: %s' % res)\n return clsk_job_id\n except KeyError as ex:\n raise ClskError('Error parsing json data: %s' % ex)\n except ApiError as ex:\n raise ClskError(ex)","def set_dhcp_conn(nic):\n nic.EnableDHCP()\n # After static DNS servers are specified to start using Dynamic Host\n # Configuration Protocol (DHCP) instead of static DNS servers,\n # you can call the method without supplying \"in\" parameters.\n nic.SetDNSServerSearchOrder()","def test_build_command_port(self):\n actual_result = IperfServerCommandBuilder()\\\n .set_port(IPERF_PORT)\\\n .build_server_command()\n self.assertListEqual(actual_result, ['iperf', '-s', '-p', '22'])","def host_create(self,hostname,hostip,groupid,templateid):\n data = json.dumps(\n {\n \"jsonrpc\": \"2.0\",\n \"method\": \"host.create\",\n \"params\": {\n \"host\": hostname,\n \"interfaces\": [\n {\n \"type\": 1,\n \"main\": 1,\n \"useip\": 1,\n \"ip\": hostip,\n \"dns\": \"\",\n \"port\": \"10050\"\n }\n ],\n \"groups\": groupid, \n \"templates\":templateid,\n\n },\n \"auth\": self.authID,\n \"id\": 1 \n })\n res = self.get_data(data)['result']\n if (res !=0) and (len(res) != 0):\n return res\n else:\n return 0","def create_relay08_cmd_ifwi(port_id, ifwi, stime=0.5, comport='/dev/ttyUSB4', host_passwd='123456'):\n cmd = 'echo '+host_passwd+' | sudo -S python runner/operation_lib/base_lib/relay08_ctrl_button.py'\n if port_id in range(1, 9):\n cmd = cmd + ' -p ' + str(port_id)\n else:\n print '******please input correct port_id which in (1~8)********'\n sys.exit()\n\n if not stime == 0.5:\n print 'stime ==== ', stime\n cmd = cmd + ' -t ' + str(stime)\n if not comport == '/dev/ttyUSB4':\n cmd = cmd + ' -c ' + str(comport)\n\n if ifwi in range(1, 9):\n cmd = cmd + ' -i ' + str(ifwi)\n os.system(cmd)","def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()","def start(self):\n if self.isRunning():\n raise Exception('DhcpClientAlreadyStarted')\n cmd = ['sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S']\n if self._logger is not None:\n self._logger.debug('Running command ' + str(cmd))\n #self._slave_dhcp_client_proc = robot.libraries.Process.Process()\n #self._slave_dhcp_client_proc.start_process('sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S')\n self._slave_dhcp_client_proc = subprocess.Popen(cmd)#, stdout=open(os.devnull, 'wb'), stderr=subprocess.STDOUT)\n self._slave_dhcp_client_pid = self._slave_dhcp_client_proc.pid\n self.addSlavePid(self._slave_dhcp_client_proc.pid) # Add the PID of the child to the list of subprocesses (note: we get sudo's PID here, not the slave PID, that we will get later on via D-Bus (see RemoteDhcpClientControl.getPid())","def relay(self, pid, remote, local=0):\n\t\tif self.client is None:\n\t\t\traise UsageError(\"Not connected!\")\n\t\treturn self.client.relay_port(pid, remote, local)","def open(self):\n # Move all of the connection arguments into connect_args\n connect_args = {}\n\n # check for mode\n if self.get_option('port') is None:\n if self.get_option('mode') == 'telnet':\n connect_args['port'] = 23\n elif self.get_option('mode') == 'serial':\n connect_args['port'] = '/dev/ttyUSB0'\n else:\n connect_args['port'] = 830\n else:\n connect_args['port'] = self.get_option('port')\n\n if (self.get_option('mode') == 'telnet' or\n self.get_option('mode') == 'serial'):\n if self.get_option('baud') is None:\n # Default baud if serial or telnet mode\n connect_args['baud'] = 9600\n if self.get_option('attempts') is None:\n # Default attempts if serial or telnet mode\n connect_args['attempts'] = 10\n\n connect_args['host'] = self.get_option('host')\n # connect_args['port'] = self.get_option('port')\n connect_args['user'] = self.get_option('remote_user')\n connect_args['passwd'] = self.get_option('password')\n connect_args['ssh_private_key_file'] = self.get_option('private_key_file')\n connect_args['ssh_config'] = self.get_option('pyez_ssh_config')\n connect_args['timeout'] = self.get_option('persistent_connect_timeout')\n try:\n log_connect_args = dict(connect_args)\n log_connect_args[\"passwd\"] = \"NOT_LOGGING_PARAMETER\"\n\n self.queue_message(\"vvvv\", \"Creating device parameters: %s\" % log_connect_args)\n timeout = connect_args.pop(\"timeout\")\n self.dev = jnpr.junos.device.Device(**connect_args)\n self.queue_message(\"vvvv\", \"Opening device.\")\n self.dev.open()\n self.queue_message(\"vvvv\", \"Device opened.\")\n\n self.dev.timeout = self.get_option('persistent_command_timeout')\n self.queue_message(\"vvvv\", \"Setting default device timeout to %d.\" % timeout)\n # Exceptions raised by close() or open() are all sub-classes of\n # ConnectError, so this should catch all connection-related exceptions\n # raised from PyEZ.\n except pyez_exception.ConnectError as ex:\n raise AnsibleError(\"Unable to make a PyEZ connection: %s\" % (str(ex)))","def test_port_create_with_binding_information_fallback(self):\n with self.network() as network:\n with self.subnet(network=network,\n ip_version=constants.IP_VERSION_6,\n cidr='2001:db8:0:0::/64') as subnet:\n segment = self._test_create_segment(\n network_id=network['network']['id'],\n physical_network='physnet',\n network_type=constants.TYPE_VLAN)\n\n self._validate_l2_adjacency(network['network']['id'], is_adjacent=True)\n\n # Map the host to the segment\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n\n res = self.deserialize(self.fmt, response)\n self._validate_immediate_ip_allocation(res['port']['id'])\n\n # Since the subnet is not on a segment, fall back to it\n self._assert_one_ip_in_subnet(response, subnet['subnet']['cidr'])","def run(self, network_create_args=None,\n port_create_args=None, ports_per_network=1):\n network = self._get_or_create_network(**(network_create_args or {}))\n for i in range(ports_per_network):\n self.neutron.create_port(network[\"id\"], **(port_create_args or {}))\n\n self.neutron.list_ports()","def test_port_create_on_unconnected_host(self):\n network, segment, _subnet = self._create_test_segment_with_subnet()\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n\n self.assertEqual(webob.exc.HTTPConflict.code, response.status_int)\n self.assertEqual(segment_exc.HostNotConnectedToAnySegment.__name__,\n res['NeutronError']['type'])\n\n # Ensure that mapping the segment to other hosts doesn't trip it up\n self._setup_host_mappings([(segment['segment']['id'], 'otherhost')])\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n\n self.assertEqual(webob.exc.HTTPConflict.code, response.status_int)\n self.assertEqual(segment_exc.HostNotConnectedToAnySegment.__name__,\n res['NeutronError']['type'])","def test_port_create_on_multiconnected_host(self):\n network, segments, subnets = self._create_test_segments_with_subnets(2)\n\n # This host is bound to multiple hosts\n self._setup_host_mappings([(segments[0]['segment']['id'], 'fakehost'),\n (segments[1]['segment']['id'], 'fakehost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n self.deserialize(self.fmt, response)\n\n # multi segments supported since Antelope.\n self.assertEqual(webob.exc.HTTPCreated.code, response.status_int)","def test_port_create_with_binding_information(self):\n network, segments, subnets = self._create_test_segments_with_subnets(3)\n\n # Map the host to the middle segment (by mocking host/segment mapping)\n self._setup_host_mappings([\n (segments[1]['segment']['id'], 'fakehost'),\n (segments[1]['segment']['id'], 'otherhost'),\n (segments[0]['segment']['id'], 'thirdhost')])\n\n response = self._create_port(self.fmt,\n net_id=network['network']['id'],\n tenant_id=network['network']['tenant_id'],\n is_admin=True,\n arg_list=(portbindings.HOST_ID,),\n **{portbindings.HOST_ID: 'fakehost'})\n res = self.deserialize(self.fmt, response)\n self._validate_immediate_ip_allocation(res['port']['id'])\n\n # Since host mapped to middle segment, IP must come from middle subnet\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])","def network_create_end(self, payload):\n network_id = payload['network']['id']\n self.enable_dhcp_helper(network_id)","def create_and_bridge_iperf_virtual_interface(self):\n\n for i in self._nodes.items():\n node = i[1]\n\n # Show the current bridge and interface configuration\n print(\"\\nThis the current bridge configuration:\")\n ifaces = VPPUtil.show_bridge(node)\n question = \"\\nWould you like to keep this configuration [Y/n]? \"\n answer = self._ask_user_yn(question, \"y\")\n if answer == \"y\":\n self._sockfilename = \"/var/run/vpp/{}.sock\".format(\n ifaces[0][\"name\"].replace(\"/\", \"_\")\n )\n if os.path.exists(self._sockfilename):\n continue\n\n # Create a script that builds a bridge configuration with\n # physical interfaces and virtual interfaces\n ints_with_vints = self._iperf_vm_questions(node)\n content = \"\"\n for intf in ints_with_vints:\n vhoststr = \"\\n\".join(\n [\n \"comment { The following command creates the socket }\",\n \"comment { and returns a virtual interface }\",\n \"comment {{ create vhost-user socket \"\n \"/var/run/vpp/sock{}.sock server }}\\n\".format(intf[\"bridge\"]),\n ]\n )\n\n setintdnstr = \"set interface state {} down\\n\".format(intf[\"name\"])\n\n setintbrstr = \"set interface l2 bridge {} {}\\n\".format(\n intf[\"name\"], intf[\"bridge\"]\n )\n setvintbrstr = \"set interface l2 bridge {} {}\\n\".format(\n intf[\"virtualinterface\"], intf[\"bridge\"]\n )\n\n # set interface state VirtualEthernet/0/0/0 up\n setintvststr = \"set interface state {} up\\n\".format(\n intf[\"virtualinterface\"]\n )\n\n # set interface state VirtualEthernet/0/0/0 down\n setintupstr = \"set interface state {} up\\n\".format(intf[\"name\"])\n\n content += (\n vhoststr\n + setintdnstr\n + setintbrstr\n + setvintbrstr\n + setintvststr\n + setintupstr\n )\n\n # Write the content to the script\n rootdir = node[\"rootdir\"]\n filename = rootdir + \"/vpp/vpp-config/scripts/create_iperf_vm\"\n with open(filename, \"w+\") as sfile:\n sfile.write(content)\n\n # Execute the script\n cmd = \"vppctl exec {}\".format(filename)\n (ret, stdout, stderr) = VPPUtil.exec_command(cmd)\n if ret != 0:\n logging.debug(stderr)\n\n print(\"\\nA script as been created at {}\".format(filename))\n print(\"This script can be run using the following:\")\n print(\"vppctl exec {}\\n\".format(filename))","def createPort():\n\n starting_interval = 0\n ending_interval = 65535\n port = randint(starting_interval, ending_interval)\n return port","def elAddNetworkConfigurationWithDhcp(self, device):\n commandSection = self.sectionByName(\"command\")\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\n deviceMatch = re.match(r\"([^0-9]+)([0-9])\", device)\n if deviceMatch:\n # e.g. \"eth0\"\n devicePrefix = deviceMatch.group(1)\n deviceNumber = deviceMatch.group(2)\n deviceNumber = int(deviceNumber)\n for i in range(8, deviceNumber - 1, -1):\n deviceI = devicePrefix + str(i)\n deviceIPlus1 = devicePrefix + str(i + 1)\n # move up by one device each network configuration\n commandSection.string = re.sub(r\"(?m)^([ \\t]*network[ \\t]+.*--device[ \\t]*(?:=|[ \\t])[ \\t]*)\" + re.escape(deviceI) + r\"(.*)$\",\n r\"\\g<1>\" + deviceIPlus1 + r\"\\g<2>\",\n commandSection.string)\n # not --noipv6\n networkConfiguration = \"network --device=\" + device + \" --bootproto=dhcp --onboot=yes --activate\"\n if deviceMatch and deviceNumber == 0:\n # having configuration of eth0 first appears to be more conducive to overall success,\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\n # \"... in installer environment. Device of the first network command is activated if network is required,\n # e.g. in case of network installation ...\",\n commandSection.string = networkConfiguration + \"\\n\" \\\n + \"#\\n\" \\\n + commandSection.string\n else:\n commandSection.string = commandSection.string \\\n + \"#\\n\" \\\n + networkConfiguration + \"\\n\"","def setHostRoute( self, ip, intf ):\n return self.cmd( 'route add -host ' + ip + ' dev ' + intf )","def launch_proxy(local_port, remote_port, remote_ip):\n\n path = os.path.abspath(os.path.dirname(__file__))\n ws = os.path.join(path, \"../../proxy/bin/wistar_proxy.py\")\n\n wistar_proxy_path = os.path.abspath(ws)\n\n cmd = \"/usr/bin/env python %s --local-port=%s --remote-ip=%s --remote-port=%s &\" % (wistar_proxy_path,\n local_port,\n remote_ip,\n remote_port)\n\n logger.debug(cmd)\n\n proc = subprocess.Popen(cmd, shell=True, close_fds=True)\n time.sleep(1)\n return proc.pid","def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\n self._ifname = ifname\n self._slave_dhcp_process = None\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\n self._new_lease_event = threading.Event() # At initialisation, event is cleared","def test_add_port():\n platform = DockerPlatform(None, None)\n platform.pre_build()\n\n node1 = Node(identifier='host1', type='host')\n host1 = platform.add_node(node1)\n assert platform.nmlnode_node_map[node1.identifier] is not None\n\n # Add ports\n p1 = BidirectionalPort(identifier='p1')\n platform.add_biport(node1, p1)\n p2 = BidirectionalPort(identifier='p2')\n platform.add_biport(node1, p2)\n p3 = BidirectionalPort(identifier='p3')\n platform.add_biport(node1, p3)\n\n # Add link\n link = BidirectionalLink(identifier='link')\n platform.add_bilink((node1, p1), (node1, p2), link)\n\n platform.post_build()\n\n result = host1('ip link list')\n\n platform.destroy()\n\n assert 'p1: <BROADCAST,MULTICAST> ' in result\n assert 'p2: <BROADCAST,MULTICAST> ' in result\n assert 'p3: <BROADCAST,MULTICAST> ' in result","def dhcp_release(ifname):\n\n logging.debug('Releasing %s...', ifname)\n\n try:\n subprocess.call(['dhcpcd', '--release', ifname])\n except OSError, err:\n if err.errno != errno.ENOENT:\n raise err\n\n try:\n call_timeout(['dhclient', '-1', '-r', ifname], timeout=5)\n except OSError, err:\n if err.errno == errno.ENOENT:\n logging.critical('Neither dhcpcd nor dhclient were found.')\n else:\n raise err","def port(self, arguments):\n instance_name = arguments['<instance>']\n instance_name = self.activate(instance_name)\n\n vmrun = VMrun(self.vmx, user=self.user, password=self.password)\n for network in vmrun.listHostNetworks().split('\\n'):\n network = network.split()\n if len(network) > 2 and network[2] == 'nat':\n print(vmrun.listPortForwardings(network[1]))\n break\n else:\n puts_err(colored.red(\"Cannot find a nat network\"))","def make_port(self, options=mock_options):\n maker = self.port_maker(sys.platform)\n if not maker:\n return None\n\n port = maker(options=options)\n if hasattr(options, \"results_directory\"):\n port._options.results_directory = port.results_directory()\n return port","def run(self, network_create_args=None,\n port_create_args=None, ports_per_network=1):\n network = self._get_or_create_network(**(network_create_args or {}))\n for i in range(ports_per_network):\n port = self.neutron.create_port(\n network[\"id\"], **(port_create_args or {}))\n\n self.neutron.get_port(port[\"id\"])","def test_shell():\n platform = DockerPlatform(None, None)\n platform.pre_build()\n\n # Add node\n node1 = Node(identifier='host1', type='host')\n host1 = platform.add_node(node1)\n\n # Add ports\n p1 = BidirectionalPort(identifier='p1')\n platform.add_biport(host1, p1)\n p2 = BidirectionalPort(identifier='p2')\n platform.add_biport(host1, p2)\n p3 = BidirectionalPort(identifier='p3')\n platform.add_biport(host1, p3)\n\n platform.post_build()\n\n reply = host1('echo \"var\"')\n\n platform.destroy()\n\n assert 'var' in reply","def create_port(self, network_name, port_name=\"port\",\n security_groups=None, net_id=None, **kwargs):\n LOG_OBJ.debug(\"Creating Port : \")\n\n if net_id is None:\n net_id = self.get_net_id(network_name)\n if not isinstance(net_id, unicode):\n return\n\n _url = \"http://\" + self.host_ip + \":9696/v2.0/ports.json\"\n _headers = {'x-auth-token': self.project_info[\"token_project\"],\n 'content-type': 'application/json'}\n _port_info = {\"port\": {\"network_id\": net_id,\n \"tenant_id\": self.project_info[\"project_id\"],\n \"name\": port_name,\n \"admin_state_up\": True,\n \"port_security_enabled\": kwargs.get(\n 'port_security_enabled', True)\n }}\n if security_groups is not None:\n _port_info[\"port\"][\"security_groups\"] = security_groups\n _body = json.dumps(_port_info)\n response = self.request(\"POST\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\" no response from Server\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Create port Failed with status %s\" %\n response.status)\n return response.status\n output = json.loads(response.data)\n\n LOG_OBJ.info(\"Port Details:%s\" % output['port'])\n return output['port']['id']","def build_address(ip_address, port):\n return f\"tcp://{ip_address}:{port}\"","def port_nic():","def ports(self, account_id):\n from pureport_client.commands.accounts.ports import Command\n return Command(self.client, account_id)","def init_tunnel(server_address, remote_port, local_port=22):\n def get_pid(appstring):\n # print 'determining PID'\n processes = subprocess.check_output(['ps', '-fx'])\n for line in processes.splitlines():\n if appstring in line:\n pid = line.split()[0]\n return pid\n\n global PID\n cmd = ['ssh', '-f', '-N', '-T', '-R{}:127.0.0.1:{}'.format(remote_port,local_port), server_address]\n try:\n proc = Popen(cmd, preexec_fn=os.setsid)\n proc.wait() # otherwise the PID won't be correct. proc.pid is of no help.\n PID = get_pid(' '.join(cmd))\n return True\n except Exception as e:\n print e\n return False","def mainProv(event, portId, ipAddr=''):\n\tif event == 'create':\n\t\thostName = genHostname(ipAddr)\n\t\tktabUpdate(hostName,action='create')\n\t\tdnsUpdate(portId=portId, ipAddr=ipAddr, action='create')\n\tif event == 'destroy':\n\t\thostName = dnsUpdate(portId, action='delete')\n\t\tktabUpdate(hostName, action='delete')","def port_factory_method(self):\n pass","def _spawn_vapv(self, hostnames, lb):\n identifier = self.openstack_connector.get_identifier(lb)\n # Initialize lists of items to clean up if operation fails\n port_ids = []\n security_groups = []\n vms = []\n try: # For rolling back objects if failure occurs...\n # Create ports...\n ports = {}\n if cfg.CONF.lbaas_settings.management_mode == \"FLOATING_IP\":\n # Primary data port (floating IP)\n (port, sec_grp, mgmt_ip) = self.openstack_connector.create_port(\n lb, hostnames[0], create_floating_ip=True, cluster=True,\n identifier=identifier\n )\n ports[hostnames[0]] = {\n \"ports\": {\n \"data\": port,\n \"mgmt\": None\n },\n \"mgmt_ip\": mgmt_ip,\n \"cluster_ip\": port['fixed_ips'][0]['ip_address']\n }\n port_ids.append(port['id'])\n security_groups = [sec_grp]\n # Secondary data port (floating IP)\n (port, junk, mgmt_ip) = self.openstack_connector.create_port(\n lb, hostnames[1], security_group=sec_grp,\n create_floating_ip=True, cluster=True\n )\n ports[hostnames[1]] = {\n \"ports\": {\n \"data\": port,\n \"mgmt\": None\n },\n \"mgmt_ip\": mgmt_ip,\n \"cluster_ip\": port['fixed_ips'][0]['ip_address']\n }\n port_ids.append(port['id'])\n elif cfg.CONF.lbaas_settings.management_mode == \"MGMT_NET\":\n # Primary data port (management network)\n (data_port, data_sec_grp, junk) = self.openstack_connector.create_port(\n lb, hostnames[0], cluster=True, identifier=identifier\n )\n # Primary mgmt port (management network)\n (mgmt_port, mgmt_sec_grp, mgmt_ip) = self.openstack_connector.create_port(\n lb, hostnames[0], mgmt_port=True, cluster=True, identifier=identifier\n )\n ports[hostnames[0]] = {\n \"ports\": {\n \"data\": data_port,\n \"mgmt\": mgmt_port\n },\n \"mgmt_ip\": mgmt_ip,\n \"cluster_ip\": mgmt_ip\n }\n security_groups = [data_sec_grp, mgmt_sec_grp]\n port_ids.append(data_port['id'])\n port_ids.append(mgmt_port['id'])\n # Secondary data port (management network)\n (data_port, sec_grp, junk) = self.openstack_connector.create_port(\n lb, hostnames[1], security_group=data_sec_grp, cluster=True\n )\n # Secondary mgmt port (management network)\n (mgmt_port, junk, mgmt_ip) = self.openstack_connector.create_port(\n lb, hostnames[1], mgmt_port=True, security_group=mgmt_sec_grp,\n cluster=True\n )\n ports[hostnames[1]] = {\n \"ports\": {\n \"data\": data_port,\n \"mgmt\": mgmt_port\n },\n \"mgmt_ip\": mgmt_ip,\n \"cluster_ip\": mgmt_ip\n }\n port_ids.append(data_port['id'])\n port_ids.append(mgmt_port['id'])\n\n # Create instances...\n try:\n bandwidth = lb.bandwidth\n if bandwidth == 0:\n raise AttributeError()\n except AttributeError:\n bandwidth = self._get_setting(\n lb.tenant_id, \"services_director_settings\", \"bandwidth\"\n )\n avoid = None\n for host in hostnames:\n # Launch vAPV...\n vm = self.openstack_connector.create_vapv(\n host, lb, ports[host]['ports'], avoid\n )\n vms.append(vm['id'])\n # Set params for next iteration...\n if cfg.CONF.lbaas_settings.allow_different_host_hint is True:\n avoid = vm['id']\n\n except Exception as e:\n if cfg.CONF.lbaas_settings.roll_back_on_error is True:\n self.openstack_connector.clean_up(\n instances=vms,\n security_groups=security_groups,\n ports=port_ids\n )\n raise e","def __init__(self, ifname):\n\n self._dbus_loop = gobject.MainLoop()\n self._bus = dbus.SystemBus()\n wait_bus_owner_timeout = 5 # Wait for 5s to have an owner for the bus name we are expecting\n logger.debug('Going to wait for an owner on bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n while not self._bus.name_has_owner(RemoteDhcpClientControl.DBUS_NAME):\n time.sleep(0.2)\n wait_bus_owner_timeout -= 0.2\n if wait_bus_owner_timeout <= 0: # We timeout without having an owner for the expected bus name\n raise Exception('No owner found for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n \n logger.debug('Got an owner for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\n gobject.threads_init() # Allow the mainloop to run as an independent thread\n dbus.mainloop.glib.threads_init()\n \n dbus_object_name = RemoteDhcpClientControl.DBUS_OBJECT_ROOT + '/' + str(ifname)\n logger.debug('Going to communicate with object ' + dbus_object_name)\n self._dhcp_client_proxy = self._bus.get_object(RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE, dbus_object_name)\n self._dbus_iface = dbus.Interface(self._dhcp_client_proxy, RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE)\n \n logger.debug(\"Connected to D-Bus\")\n self._dhcp_client_proxy.connect_to_signal(\"IpConfigApplied\",\n self._handleIpConfigApplied,\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\n \n self._dhcp_client_proxy.connect_to_signal(\"LeaseLost\",\n self._handleLeaseLost,\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\n \n #Lionel: the following line is used for D-Bus debugging only\n #self._bus.add_signal_receiver(catchall_signal_handler, interface_keyword='dbus_interface', member_keyword='member')\n self._dbus_loop_thread = threading.Thread(target = self._loopHandleDbus) # Start handling D-Bus messages in a background thread\n self._dbus_loop_thread.setDaemon(True) # D-Bus loop should be forced to terminate when main program exits\n self._dbus_loop_thread.start()\n \n self._bus.watch_name_owner(RemoteDhcpClientControl.DBUS_NAME, self._handleBusOwnerChanged) # Install a callback to run when the bus owner changes\n \n self._callback_new_lease_mutex = threading.Lock() # This mutex protects writes to the _callback_new_lease attribute\n self._callback_new_lease = None\n \n self._exit_unlock_event = threading.Event() # Create a new threading event that will allow the exit() method to wait for the child to terminate properly\n self._getversion_unlock_event = threading.Event() # Create a new threading event that will allow the GetVersion() D-Bus call below to execute within a timed limit \n\n self.status = DhcpLeaseStatus.DhcpLeaseStatus()\n\n self._getversion_unlock_event.clear()\n self._remote_version = ''\n self._dbus_iface.GetVersion(reply_handler = self._getVersionUnlock, error_handler = self._getVersionError)\n if not self._getversion_unlock_event.wait(10): # We give 10s for slave to answer the GetVersion() request\n logfile = tempfile.NamedTemporaryFile(prefix='TimeoutOnGetVersion-', suffix='.log', delete=False)\n if logfile:\n print('Saving TimeoutOnGetVersion environment dump to file \"' + logfile.name + '\"', file=sys.stderr)\n print('TimeoutOnGetVersion', file=logfile)\n subprocess.call('ps -ef', stdout=logfile, shell=True)\n subprocess.call('perl ./dbus-introspect.pl --system com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1', stdout=logfile, shell=True)\n subprocess.call('dbus-send --system --type=method_call --print-reply --dest=com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1 com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary.GetVersion', stdout=logfile, shell=True)\n logfile.close()\n raise Exception('TimeoutOnGetVersion')\n else:\n logger.debug('Slave version: ' + self._remote_version)","def create_host(self, wwpns, hostname):\n\n if not wwpns or len(wwpns) == 0 or not hostname or len(hostname) == 0:\n ex_args = {'wwpns': wwpns,\n 'hostname': hostname}\n raise SVCCreateHostParameterError(**ex_args)\n\n ports = ':'.join(wwpns)\n # get the host shortname.\n hostname_str = hostname.split('.')[0]\n LOG.debug(\"enter: create_host(): wwpns=%(wwpns)s\"\n \" hostname=%(hostname)s\"\n % {'wwpns': ports, 'hostname': hostname_str})\n\n rand_id = str(random.randint(0, 99999999)).zfill(8)\n host_name = '%s-%s' % (self._hostname_prefix(hostname_str), rand_id)\n\n cmd = 'mkhost -name %(host_name)s -hbawwpn %(ports)s -force' % locals()\n\n output, err_output = self._svc_command(cmd)\n\n if err_output:\n # err_output should be a list type\n if isinstance(err_output, types.ListType):\n err_msg = err_output[0]\n else:\n err_msg = err_output\n err_code = err_msg.split()[0]\n\n if err_code and err_code == 'CMMVC6035E':\n # host has been defined on the storage, but we don't see it.\n # return None and ask caller to run cfgdev to relogin to SAN\n # and retry get_host_from_wwpns().\n return None\n\n msg = (_(\"create_host() failure cmd=%(cmd)s, error:%(err_output)s.\"\n \" Make sure host and storage are zoned properly and check\"\n \" SAN fabric connectivity\") % locals())\n\n LOG.exception(msg)\n ex_args = {'host_name': hostname_str,\n 'err_output': err_output}\n raise SVCCreateHostFailed(**ex_args)\n\n return host_name","def start_traffic_pktgen(\n vm_fix,\n src_min_ip='',\n src_max_ip='',\n dest_ip='',\n dest_min_port='',\n dest_max_port=''):\n vm_fix.logger.info(\"Sending traffic...\")\n try:\n cmd = '~/pktgen_new.sh %s %s %s %s %s' % (src_min_ip,\n src_max_ip,\n dest_ip,\n dest_min_port,\n dest_max_port)\n vm_fix.run_cmd_on_vm(cmds=[cmd], as_sudo=True)\n except Exception as e:\n vm_fix.logger.exception(\"Got exception at start_traffic as %s\" % (e))","def create(self, params):\n return self.make_client_call(\n 'create_vpn_connection_route', params)","def InitNetwork(this_ip,fpga_ip,udp_port,timeout):\r\n global this_fpga_ip\r\n global this_sock\r\n global this_udp_port\r\n \r\n sock = socket.socket(socket.AF_INET, # Internet\r\n socket.SOCK_DGRAM) # UDP \r\n sock.settimeout(timeout)\r\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 1024*1024)\r\n sock.bind((this_ip, udp_port))\r\n this_fpga_ip = fpga_ip\r\n this_udp_port = udp_port\r\n this_sock = sock\r\n return sock","def test_createPort(self):\n store = Store()\n factory = DummyFactory(store=store)\n self.assertSuccessStatus(\n self._makeConfig(store),\n [\"create\", \"--strport\", \"tcp:8080\",\n \"--factory-identifier\", str(factory.storeID)])\n self.assertEqual(\"Created.\\n\", sys.stdout.getvalue())\n [port] = list(store.query(StringEndpointPort))\n self.assertEqual(u'tcp:8080', port.description)\n self.assertEqual(list(store.interfacesFor(port)), [IService])","def run(self, network_create_args=None,\n port_create_args=None, ports_per_network=1):\n network = self._get_or_create_network(**(network_create_args or {}))\n for i in range(ports_per_network):\n port = self.neutron.create_port(\n network[\"id\"], **(port_create_args or {}))\n\n self.neutron.delete_port(port[\"id\"])","def plug_port_into_network(self, device_id, host_id, port_id,\n net_id, tenant_id, port_name, device_owner,\n sg, orig_sg, vnic_type, segments=None,\n switch_bindings=None, vlan_type=None):","def write_option_to_dhcp_server(connection_obj, data):\n line_number = basic_obj.get_file_number_with_regex(connection_obj, data.search_pattern, data.dhcp_config_file)\n option = data.option_string # \"option dhcp6.boot-file-url \"\n option_path = data.option_url\n st.log(\"#####LINE NUMBER{}\".format(line_number))\n option_config = \"'{} \\\"{}\\\";'\".format(option, option_path)\n if int(line_number) > 0:\n # line_number = data.line_number if line_number in data else 60\n basic_obj.delete_line_using_line_number(connection_obj, line_number, data.dhcp_config_file)\n basic_obj.write_to_file(connection_obj, option_config, data.dhcp_config_file, device=\"server\")\n # basic_obj.write_to_file_to_line(connection_obj, option_config, line_number, data.dhcp_config_file, device=\"server\")\n line_number = basic_obj.get_file_number_with_regex(connection_obj, data.search_pattern, data.dhcp_config_file)\n st.log(\"#####LINE NUMBER{}\".format(line_number))\n if line_number <= 0:\n st.log(\"Written content in file {} not found\".format(data[\"dhcp_config_file\"]))\n st.report_fail(\"content_not_found\")","def _make_lcdproc(\n lcd_host, lcd_port, retry_config,\n charset=DEFAULT_LCDPROC_CHARSET, lcdd_debug=False):\n\n class ServerSpawner(utils.AutoRetryCandidate):\n \"\"\"Spawn the server, using auto-retry.\"\"\"\n\n @utils.auto_retry\n def connect(self):\n return lcdrunner.LcdProcServer(\n lcd_host, lcd_port, charset=charset, debug=lcdd_debug)\n\n spawner = ServerSpawner(retry_config=retry_config, logger=logger)\n\n try:\n return spawner.connect()\n except socket.error as e:\n logger.error('Unable to connect to lcdproc %s:%s : %r', lcd_host, lcd_port, e)\n raise SystemExit(1)","def config_dhcpv6_options(ssh_conn_obj, ztp_params, config_params, options=dict(), cli_type=\"\"):\n cli_type = st.get_ui_type(config_params.dut, cli_type=cli_type)\n retry_count = config_params.retry_count if \"retry_count\" in config_params and config_params.retry_count else 0\n iteration = config_params.iteration if \"iteration\" in config_params and config_params.iteration else 300\n delay = config_params.delay if \"delay\" in config_params and config_params.delay else 3\n expect_reboot = True if \"expect_reboot\" in options and options [\"expect_reboot\"] else False\n st.log(config_params)\n if \"func_name\" in config_params:\n syslog_file_names = [\"syslog_1_{}\".format(config_params.func_name), \"syslog_{}\".format(config_params.func_name)]\n if \"json_content\" in config_params:\n file_path = basic_obj.write_to_json_file(config_params.json_content)\n st.log(file_path)\n if file_path:\n destination_path = \"{}{}/{}\".format(config_params.home_path, ztp_params.config_path, config_params.ztp_file)\n st.log(destination_path)\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\n config_params.option_59_url = \"http://[{}]{}/{}\".format(config_params.static_ip, ztp_params.config_path, config_params.ztp_file)\n config_params.search_pattern = r'\\s*option\\s+dhcp6.boot-file-url\\s+\"\\S+\";'\n write_option_59_to_dhcp_server(ssh_conn_obj, config_params)\n basic_obj.service_operations(ssh_conn_obj, config_params.dhcp6_service_name, \"restart\", \"server\")\n if not verify_dhcpd_service_status(ssh_conn_obj, config_params.dhcpd6_pid):\n st.log(\"{} service is running which is not expected\".format(config_params.dhcp6_service_name))\n st.report_fail(\"service_running_not_expected\", config_params.dhcp6_service_name)\n reboot_type = config_params.reboot_type if \"reboot_type\" in config_params and config_params.reboot_type else \"normal\"\n if \"ztp_operation\" in config_params:\n config_params.ztp_operation = \"reboot\" if cli_type == \"klish\" else config_params.ztp_operation\n if config_params.ztp_operation == \"reboot\":\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\n elif config_params.ztp_operation == \"run\":\n ztp_operations(config_params.dut, config_params.ztp_operation)\n else:\n st.log(\"ZTP operation is not mentioned hence rebooting the device ...\")\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\n if \"reboot_on_success\" in options and options[\"reboot_on_success\"]:\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=options[\"reboot_on_success\"], cli_type=cli_type)\n else:\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\n if not result:\n if \"logs_path\" in config_params and \"func_name\" in config_params:\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n if \"reboot_on_success\" in options and options[\"reboot_on_success\"]:\n reboot_obj.config_reload(config_params.dut)\n st.wait(5)\n if not ip_obj.ping(config_params.dut, config_params.static_ip, family=\"ipv6\"):\n st.log(\"Pinging to DHCP server failed from DUT, issue either with DUT or server\")\n # intf_obj.enable_dhcp_on_interface(config_params.dut, config_params.network_port, \"v6\")\n if not verify_ztp_status(config_params.dut, retry_count, iteration, delay, cli_type=cli_type):\n if \"logs_path\" in config_params and \"func_name\" in config_params:\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\n st.log(\"ZTP status verification failed\")\n st.report_fail(\"ztp_status_verification_failed\")\n verify_ztp_filename_logs(config_params.dut, config_params)\n if \"ztp_log_string\" in config_params and config_params.ztp_log_string:\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path, config_params.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(config_params.ztp_log_path, config_params.ztp_log_string))\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path_1, config_params.ztp_log_string):\n st.log(\"ZTP log {} verification failed for message {}\".format(config_params.ztp_log_path_1, config_params.ztp_log_string))\n st.report_fail(\"ztp_log_verification_failed\", config_params.ztp_log_path_1, config_params.ztp_log_string)\n if \"result\" in config_params and config_params.result == \"pass\":\n st.report_pass(\"test_case_passed\")","def create_host(self, port=20298):\n # We use UDP to broadcast the host\n self.host = Networking.Host(0.5, 0.5, 5)\n\n \"\"\"\n # find unused ip address\n for i in range(255, 0, -1):W\n for j in range(255, 0, -1):\n try:\n print(\"Checking for free address at 192.168.\"+str(i)+\".\"+str(j))\n # try to connect to the specified ip address\n socket.gethostbyaddr(\"192.168.\"+str(i)+\".\"+str(j))\n except socket.error:\n print(\"Address is unused\")\n # if we cannot connect to the address, that means the ip address is unused\n server_ip = \"192.168.\"+str(i)+\".\"+str(j)\n break\n if server_ip is not None:\n break\n \"\"\"\n\n # port = self.get_open_port()\n # listen to all address\n server_ip = \"0.0.0.0\"\n client_ip = \"localhost\"\n\n try:\n self.host.connect(server_ip, port)\n self.host.accepting_allow()\n\n # Clear the broadcast blocker\n # self.stop_broadcast.clear()\n # broadcaster = threading.Thread(target=self.broadcast_game, args=(None, self.stop_broadcast))\n # broadcaster.start()\n\n # The host also acts as a client\n self.join_host(client_ip, 20298, GameStateModel.instance().host)\n except MastermindErrorSocket:\n logger.error(\"Failed to create a host\")","def add_port(bridge, port, external_id=None):\n _run('ip', 'link', 'set', port, 'up')\n _run('ovs-vsctl', 'add-port', bridge, port)\n if external_id:\n ports = SimpleOVSDB('ovs-vsctl', 'port')\n for port in ports.find('name={}'.format(port)):\n ports.set(port['_uuid'],\n 'external_ids:{}'.format(external_id[0]),\n external_id[1])","def port():","def test_build_command_daemon(self):\n actual_result = IperfClientCommandBuilder()\\\n .set_server_ip(SERVER_IP)\\\n .build_client_command()\n self.assertListEqual(actual_result, ['iperf', '-c', '192.168.1.1'])","def cmd_port (self, line):\r\n info = line[1].split (',')\r\n ip = '.'.join (info[:4])\r\n port = int(info[4])*256 + int(info[5])\r\n # how many data connections at a time?\r\n # I'm assuming one for now...\r\n # TODO: we should (optionally) verify that the\r\n # ip number belongs to the client. [wu-ftpd does this?]\r\n self.client_addr = (ip, port)\r\n self.respond ('200 PORT command successful.')","def do_nic_create(cc, args):\n dct = utils.to_attrs_dict(args.attributes[0], VALID_FIELDS)\n _validate(dct)\n result = cc.nic.post(dct)\n cliutils.print_dict(result)","def AddPortFlag(parser, required=False):\n help_text = \"\"\"\\\n Network port of the database.\n \"\"\"\n parser.add_argument('--port', help=help_text, required=required, type=int)","def poe_recycle(baseurl, cookie_header, port):\n logging.info(\"Starting Power recycle for Port {}\".format(port))\n cmd = \"configure terminal\"\n common.anycli(baseurl, cmd, cookie_header)\n cmd = \"interface \" + port\n common.anycli(baseurl, cmd, cookie_header)\n cmd = \"no power-over-ethernet\"\n common.anycli(baseurl, cmd, cookie_header)\n logging.info(\"Power disabled for Port {}\".format(port))\n cmd = \"power-over-ethernet\"\n common.anycli(baseurl, cmd, cookie_header)\n logging.info(\"Power enabled for Port {}\".format(port))","def main():\n cli = DhcpClientCLI()\n\n parser = argparse.ArgumentParser(\n description='Management CLI for Mobility DHCP Client',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n # Add sub commands\n subparsers = parser.add_subparsers(title='subcommands', dest='cmd')\n\n # List\n subparser = subparsers.add_parser(\n 'list_dhcp_records',\n help='Lists all records from Redis',\n )\n subparser.set_defaults(func=cli.list_all_record)\n\n # Add\n subparser = subparsers.add_parser(\n 'add_rec',\n help='Add ip allocation record',\n )\n subparser.add_argument(\n 'mac', help='Mac address, e.g. \"8a:00:00:00:0b:11\"',\n type=str,\n )\n subparser.add_argument(\n 'ip', help='IP address, e.g. \"1.1.1.1\"',\n type=ip_address,\n )\n\n subparser.add_argument(\n 'state',\n help='DHCP protocol state 1 to 7, e.g. \"1\"',\n type=int,\n )\n subparser.add_argument(\n 'subnet',\n help='IP address subnet, e.g. \"1.1.1.0/24\"',\n type=ipaddress.ip_network,\n )\n\n subparser.add_argument('dhcp', help='DHCP IP address, e.g. \"1.1.1.100\"')\n subparser.add_argument('lease', help='Lease time in seconds, e.g. \"100\"')\n subparser.set_defaults(func=cli.add_record)\n\n # del\n subparser = subparsers.add_parser(\n 'del_rec',\n help='Add ip allocation record',\n )\n subparser.add_argument('mac', help='Mac address, e.g. \"8a:00:00:00:0b:11\"')\n subparser.set_defaults(func=cli.del_record)\n\n # set default gw\n subparser = subparsers.add_parser(\n 'set_default_gw',\n help='Set default GW',\n )\n subparser.add_argument('ip', help='IP address, e.g. \"1.1.1.1\"')\n\n subparser.set_defaults(func=cli.set_deafult_gw)\n\n # set gw mac\n subparser = subparsers.add_parser(\n 'set_gw_mac',\n help='Set GW Mac address',\n )\n subparser.add_argument('mac', help='Mac address, e.g. \"8a:00:00:00:0b:11\"')\n\n subparser.set_defaults(func=cli.set_deafult_gw)\n\n # Parse the args\n args = parser.parse_args()\n if not args.cmd:\n parser.print_usage()\n sys.exit(1)\n\n # Execute the sub-command function\n args.func(args)","def __init__(self, host, port=2345):\n self.host = host\n self.port = port\n self.set_command_list()","def create_port_postcommit(self, mech_context):\n\n LOG.debug(\"create_port_postcommit: called\")\n port = mech_context.current\n port_id = port['id']\n network_id = port['network_id']\n tenant_id = port['tenant_id']\n host_id = mech_context._binding.host\n context = mech_context._plugin_context\n try:\n network = seamicro_db.get_network(context, network_id)\n except Exception:\n LOG.exception(\n _LE(\"SeaMicro Mechanism: failed to get network %s from db\"),\n network_id)\n raise Exception(\n _(\"SeaMicro Mechanism: failed to get network %s from db\"),\n network_id)\n\n vlan_id = network['vlan']\n switch_ip, server_id, nics = _get_switch_info(self._switch, host_id)\n if switch_ip is not None and server_id is not None and nics is not None:\n try:\n interfaces = self.client[switch_ip].interfaces.list()\n for interface in interfaces:\n interface.add_tagged_vlan(vlan_id)\n\n server = self.client[switch_ip].servers.get(server_id)\n if nics:\n server.set_tagged_vlan(vlan_id, nics=nics)\n else:\n server.set_tagged_vlan(vlan_id)\n except seamicro_client_exception.ClientException as ex:\n LOG.exception(\n _LE(\"SeaMicro driver: failed to create port\"\n \" with the following error: %(error)s\"),\n {'error': ex.message})\n seamicro_db.delete_port(context, port_id)\n raise Exception(\n _(\"SeaMicro Mechanism: create_port_postcommit failed\"))\n\n LOG.info(\n _LI(\"created port (postcommit): port_id=%(port_id)s\"\n \" network_id=%(network_id)s tenant_id=%(tenant_id)s\"\n \" switch_ip=%(switch_ip)s server_id=%(server_id)s\"),\n {'port_id': port_id,\n 'network_id': network_id, 'tenant_id': tenant_id,\n 'switch_ip': switch_ip, 'server_id': server_id})","def run(self):\n self.network_ctrl.connect_with_remote_system()\n cmd = self.create_command(self.on_or_off, self.port)\n self.network_ctrl.send_command(cmd)\n\n check = self._port_status(self.port)\n result = self.network_ctrl.send_command(check)\n result = result[0]\n if self.on_or_off:\n if result == \"1\":\n self.router.mode = Mode.normal\n logging.info(\"[+] Successfully switched on port \" + str(self.port))\n else:\n self.router.mode = Mode.unknown\n logging.info(\"[-] Error switching on port \" + str(self.port))\n else:\n if result == \"0\":\n self.router.mode = Mode.off\n logging.info(\"[+] Successfully switched off port \" + str(self.port))\n else:\n self.router.mode = Mode.unknown\n logging.info(\"[-] Error switching off port \" + str(self.port))\n\n self.network_ctrl.exit()"],"string":"[\n \"def create_dhcp_port(self, port):\\n LOG.debug(\\\"create_dhcp_port: %s\\\", port)\\n port['port']['id'] = port['port']['network_id']\\n\\n # The following MAC address will be assigned to the Linux dummy\\n # interface that\\n # networking_calico.agent.linux.interface.RoutedInterfaceDriver\\n # creates. Therefore it will never actually be used or involved in the\\n # sending or receiving of any real data. Hence it should not matter\\n # that we use a hardcoded value here, and the same value on every\\n # networking-calico compute host. The '2' bit of the first byte means\\n # 'locally administered', which makes sense for a hardcoded value like\\n # this and distinguishes it from the space of managed MAC addresses.\\n port['port']['mac_address'] = '02:00:00:00:00:00'\\n port['port']['device_owner'] = constants.DEVICE_OWNER_DHCP\\n return dhcp.DictModel(port['port'])\",\n \"async def create(context, name_or_id,\\n neutronclient,\\n network_id,\\n subnet_id=None,\\n ip_addresses=None,\\n admin_state_up=True,\\n security_groups=None,\\n use_existing=False):\\n\\n if not use_existing:\\n port_dict = {\\n 'port': {\\n 'admin_state_up': admin_state_up,\\n 'name': name_or_id,\\n 'network_id': network_id,\\n }\\n }\\n fixed_ips = []\\n if subnet_id:\\n subnet = {'subnet_id': subnet_id}\\n if ip_addresses:\\n subnet.update({'ip_address': ip_addresses})\\n fixed_ips.append(subnet)\\n port_dict['port']['fixed_ips'] = fixed_ips\\n if security_groups:\\n port_dict['port']['security_groups'] = security_groups\\n\\n context.logger.info('Creating port with identifiers: {0}'\\n .format(str(port_dict)))\\n port = neutronclient.create_port(body=port_dict)\\n\\n else:\\n context.logger.info('Using existing port \\\"{0}\\\".'\\n .format(name_or_id))\\n port = neutronclient.show_port(name_or_id)\\n\\n return port['port']\",\n \"def launch (no_flow = False,\\n network = \\\"192.168.0.0/24\\\", # Address range\\n first = 1, last = None, count = None, # Address range\\n ip = \\\"192.168.0.254\\\",\\n router = (), # Auto\\n dns = (), # Auto\\n dpid = None, # All\\n ports = None, # All\\n __INSTANCE__ = None):\\n def fixint (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n return int(i)\\n def fix (i):\\n i = str(i)\\n if i.lower() == \\\"none\\\": return None\\n if i.lower() == \\\"true\\\": return None\\n if i == '()': return ()\\n return i\\n first,last,count = map(fixint,(first,last,count))\\n router,dns = map(fix,(router,dns))\\n\\n if ports is not None:\\n ports = ports.split(\\\",\\\")\\n ports = set(int(p) if p.isdigit() else p for p in ports)\\n\\n pool = SimpleAddressPool(network = network, first = first, last = last,\\n count = count)\\n\\n inst = DHCPD(install_flow = not no_flow, pool = pool,\\n ip_address = ip, router_address = router,\\n dns_address = dns, dpid = dpid, ports = ports)\\n\\n if __INSTANCE__[0] == 0:\\n # First or only instance\\n core.register(inst)\\n\\n log.debug(\\\"DHCP serving a%s\\\", str(pool)[2:-1])\",\n \"def create_command(self, on_or_off: bool, port: int):\\n return self.power_strip.create_command(port, on_or_off)\",\n \"def port_create(request, network_id, **kwargs):\\n LOG.debug(\\\"port_create(): netid=%(network_id)s, kwargs=%(kwargs)s\\\",\\n {'network_id': network_id, 'kwargs': kwargs})\\n kwargs = unescape_port_kwargs(**kwargs)\\n body = {'port': {'network_id': network_id}}\\n if 'tenant_id' not in kwargs:\\n kwargs['tenant_id'] = request.user.project_id\\n body['port'].update(kwargs)\\n port = neutronclient(request).create_port(body=body).get('port')\\n return Port(port)\",\n \"def cmd_port(args):\",\n \"def create(self, name, network_id, fixed_ips, host_id=None, profile=None,\\n vnic_type=None, device_owner=None, device_id=None, \\n security_groups=None, mac_address=None, tenant_id=None):\\n data = {\\n \\\"port\\\": {\\n \\\"network_id\\\": network_id,\\n \\\"name\\\": name,\\n \\\"admin_state_up\\\": True,\\n \\\"fixed_ips\\\": fixed_ips,\\n }\\n }\\n if tenant_id is not None:\\n data[u'port'][u'tenant_id'] = tenant_id\\n if host_id is not None:\\n data['port']['binding:host_id'] = host_id\\n if profile is not None:\\n data['port']['binding:profile'] = profile\\n if host_id is not None:\\n data['port']['binding:vnic_type'] = vnic_type\\n if device_owner is not None:\\n data['port']['device_owner'] = device_owner\\n if device_id is not None:\\n data['port']['device_id'] = device_id\\n if security_groups is not None:\\n data['port']['security_groups'] = security_groups\\n if mac_address is not None:\\n data[u'allowed_address_pairs'] = [{u'mac_address':mac_address,\\n u'ip_address':fixed_ips[0][u'ip_address']}]\\n\\n path = '%s/ports' % self.ver\\n res = self.client.call(path, 'POST', data=json.dumps(data), \\n token=self.manager.identity.token)\\n self.logger.debug('Create openstack port: %s' % truncate(res))\\n return res[0]['port']\",\n \"def create_dhcp_relay(self, iface_name='global', server_ip=None, fwd_iface_name=None):\\n pass\",\n \"def create_port(self, context, port):\\n LOG.debug(_(\\\"NeutronRestProxyV2: create_port() called\\\"))\\n\\n # Update DB in new session so exceptions rollback changes\\n with context.session.begin(subtransactions=True):\\n port[\\\"port\\\"][\\\"admin_state_up\\\"] = False\\n dhcp_opts = port['port'].get(edo_ext.EXTRADHCPOPTS, [])\\n new_port = super(NeutronRestProxyV2, self).create_port(context,\\n port)\\n if (portbindings.HOST_ID in port['port']\\n and 'id' in new_port):\\n host_id = port['port'][portbindings.HOST_ID]\\n porttracker_db.put_port_hostid(context, new_port['id'],\\n host_id)\\n self._process_port_create_extra_dhcp_opts(context, new_port,\\n dhcp_opts)\\n new_port = self._extend_port_dict_binding(context, new_port)\\n net = super(NeutronRestProxyV2,\\n self).get_network(context, new_port[\\\"network_id\\\"])\\n\\n if self.add_meta_server_route:\\n if new_port['device_owner'] == 'network:dhcp':\\n destination = METADATA_SERVER_IP + '/32'\\n self._add_host_route(context, destination, new_port)\\n\\n # create on network ctrl\\n mapped_port = self._map_state_and_status(new_port)\\n self.servers.rest_create_port(net, mapped_port)\\n\\n # connect device to network, if present\\n device_id = port[\\\"port\\\"].get(\\\"device_id\\\")\\n if device_id:\\n try:\\n self.servers.rest_plug_interface(net[\\\"tenant_id\\\"], net[\\\"id\\\"],\\n new_port, device_id)\\n except RemoteRestError:\\n with excutils.save_and_reraise_exception():\\n port_update = {\\\"port\\\": {\\\"status\\\": \\\"ERROR\\\"}}\\n super(NeutronRestProxyV2, self).update_port(\\n context,\\n new_port[\\\"id\\\"],\\n port_update\\n )\\n # Set port state up and return that port\\n port_update = {\\\"port\\\": {\\\"admin_state_up\\\": True}}\\n new_port = super(NeutronRestProxyV2, self).update_port(context,\\n new_port[\\\"id\\\"],\\n port_update)\\n return self._extend_port_dict_binding(context, new_port)\",\n \"def create_port(self, body=None):\\r\\n return self.post(self.ports_path, body=body)\",\n \"def release_dhcp_port(self, network_id, device_id):\\n LOG.debug(\\\"release_dhcp_port: %s %s\\\", network_id, device_id)\",\n \"def remote_createTunnel(self, name, targetIP):\\r\\n if name not in self._bridges:\\r\\n raise InternalError('Bridge does not exist.')\\r\\n\\r\\n key = (name, targetIP)\\r\\n\\r\\n if key in self._uid:\\r\\n raise InternalError('Tunnel already exists.')\\r\\n\\r\\n while 1:\\r\\n uid = randomString(self._UID_LEN)\\r\\n\\r\\n if uid not in self._uid.itervalues():\\r\\n break\\r\\n\\r\\n self._uid[key] = uid\\r\\n port = 'gre-{0}'.format(uid)\\r\\n\\r\\n return execute(('/usr/bin/ovs-vsctl', 'add-port', 'br-{0}'.format(name),\\r\\n port, '--', 'set', 'interface', port, 'type=gre',\\r\\n 'options:remote_ip={0}'.format(targetIP)),\\r\\n reactor=self._reactor)\",\n \"def create(self):\\n\\t\\treturn handle_to_object(call_sdk_function('PrlPortFwd_Create'))\",\n \"def dhcp(self, dhcp):\\n\\n self._dhcp = dhcp\",\n \"def create(clients, context, name='test port'):\\n network_id = context['network_id']\\n logger.info(\\\"Taking action port.create_for_server{}.\\\".format(name))\\n neutron = clients.get_neutron()\\n body = {\\n \\\"port\\\": {\\n \\\"network_id\\\": network_id,\\n \\\"name\\\": name\\n }\\n }\\n resp = neutron.create_port(body=body)\\n port = resp['port']\\n context['port_id'] = port['id']\\n context.setdefault('stack', []).append({'port_id': port['id']})\",\n \"def configureDHCP():\\n dhcpStart = config.get(\\\"hotspot\\\", \\\"dhcpstart\\\")\\n dhcpEnd = config.get(\\\"hotspot\\\", \\\"dhcpend\\\")\\n dnsmasqConfig = f\\\"\\\"\\\"#PI Hotspot config\\ndomain-needed\\nbogus-priv\\ndhcp-option=option:dns-server\\ndhcp-authoritative\\ndhcp-range={dhcpStart},{dhcpEnd},1h\\n\\\"\\\"\\\"\\n confFile = open(\\\"/etc/dnsmasq.conf\\\", \\\"w\\\")\\n confFile.write(dnsmasqConfig)\\n confFile.close()\",\n \"def setup_dhcp_env(device):\\n raise NotImplementedError\",\n \"def create_port(self, context, port):\\n LOG.debug(_(\\\"QuantumRestProxyV2: create_port() called\\\"))\\n\\n # Update DB\\n port[\\\"port\\\"][\\\"admin_state_up\\\"] = False\\n new_port = super(QuantumRestProxyV2, self).create_port(context, port)\\n net = super(QuantumRestProxyV2,\\n self).get_network(context, new_port[\\\"network_id\\\"])\\n\\n # create on networl ctrl\\n try:\\n resource = PORT_RESOURCE_PATH % (net[\\\"tenant_id\\\"], net[\\\"id\\\"])\\n data = {\\n \\\"port\\\": {\\n \\\"id\\\": new_port[\\\"id\\\"],\\n \\\"state\\\": \\\"ACTIVE\\\",\\n }\\n }\\n ret = self.servers.post(resource, data)\\n if not self.servers.action_success(ret):\\n raise RemoteRestError(ret[2])\\n\\n # connect device to network, if present\\n if port[\\\"port\\\"].get(\\\"device_id\\\"):\\n self._plug_interface(context,\\n net[\\\"tenant_id\\\"], net[\\\"id\\\"],\\n new_port[\\\"id\\\"], new_port[\\\"id\\\"] + \\\"00\\\")\\n except RemoteRestError as e:\\n LOG.error(_(\\\"QuantumRestProxyV2: Unable to create remote port: \\\"\\n \\\"%s\\\"), e.message)\\n super(QuantumRestProxyV2, self).delete_port(context,\\n new_port[\\\"id\\\"])\\n raise\\n\\n # Set port state up and return that port\\n port_update = {\\\"port\\\": {\\\"admin_state_up\\\": True}}\\n return super(QuantumRestProxyV2, self).update_port(context,\\n new_port[\\\"id\\\"],\\n port_update)\",\n \"def create_relay08_cmd(port_id, stime=0.5, comport='/dev/ttyUSB4'):\\n with open(\\\"runner/operation_lib/host_passwd\\\") as pwdfp:\\n host_passwd = pwdfp.read()\\n\\n cmd = 'echo '+host_passwd+' | sudo -S python runner/operation_lib/base_lib/relay08_ctrl_button.py'\\n if port_id in range(1, 9):\\n cmd = cmd + ' -p ' + str(port_id)\\n else:\\n print '******please input correct port_id which in (1~8)********'\\n sys.exit()\\n\\n if not stime == 0.5:\\n cmd = cmd + ' -t ' + str(stime)\\n\\n if not comport == '/dev/ttyUSB4':\\n cmd = cmd + ' -c ' + str(comport)\\n os.system(cmd)\",\n \"def release_dhcp_port(self, network_id, device_id):\\n return self.call(self.context,\\n self.make_msg('release_dhcp_port',\\n network_id=network_id,\\n device_id=device_id,\\n host=self.host),\\n topic=self.topic)\",\n \"def start_srv(start, process):\\n if not \\\"conf_option\\\" in world.cfg:\\n world.cfg[\\\"conf_option\\\"] = \\\"\\\"\\n\\n world.cfg['log_file'] = build_log_path()\\n fabric_sudo_command('cat /dev/null >' + world.cfg['log_file'])\\n world.cfg[\\\"dhcp_log_file\\\"] = world.cfg['log_file']\\n\\n log = \\\"local7\\\"\\n if world.f_cfg.isc_dhcp_log_facility != \\\"\\\":\\n log = world.f_cfg.isc_dhcp_log_facility\\n\\n world.cfg['log_facility'] = '''\\\\nlog-facility {log};\\\\n'''.format(**locals())\\n\\n add_defaults()\\n cfg_write()\\n log.debug(\\\"Start ISC-DHCP with generated config:\\\")\\n convert_cfg_file(world.cfg[\\\"cfg_file\\\"])\\n fabric_send_file(world.cfg[\\\"cfg_file\\\"] + '_processed', world.cfg[\\\"cfg_file\\\"] + '_processed')\\n copy_configuration_file(world.cfg[\\\"cfg_file\\\"] + '_processed')\\n remove_local_file(world.cfg[\\\"cfg_file\\\"])\\n #set_ethernet_interface()\\n stop_srv()\\n\\n world.cfg['leases'] = build_leases_path()\\n\\n #fabric_sudo_command('echo y |rm ' + world.cfg['leases'])\\n fabric_sudo_command('touch ' + world.cfg['leases'])\\n\\n result = fabric_sudo_command('(' + os.path.join(world.f_cfg.software_install_path, 'sbin/dhcpd') + ' -cf server.cfg_processed'\\n + ' -lf ' + world.cfg['leases']\\n + '&); sleep ' + str(world.f_cfg.sleep_time_1) + ';')\\n\\n check_process_result(start, result, process)\\n\\n # clear configs in case we would like make couple configs in one test\\n world.cfg[\\\"conf_time\\\"] = \\\"\\\"\\n world.cfg[\\\"log_facility\\\"] = \\\"\\\"\\n world.cfg[\\\"custom_lines\\\"] = \\\"\\\"\\n world.cfg[\\\"conf_option\\\"] = \\\"\\\"\\n world.cfg[\\\"conf_vendor\\\"] = \\\"\\\"\",\n \"async def test_dhcp(hass: HomeAssistant) -> None:\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN,\\n data=dhcp.DhcpServiceInfo(\\n ip=\\\"1.2.3.4\\\", macaddress=\\\"01:23:45:67:89:ab\\\", hostname=\\\"mock_hostname\\\"\\n ),\\n context={\\\"source\\\": config_entries.SOURCE_DHCP},\\n )\\n\\n assert result.get(\\\"type\\\") == FlowResultType.FORM\\n assert result.get(\\\"step_id\\\") == \\\"user\\\"\",\n \"def dhcp_callback(self, state, target_mac=None, target_ip=None, exception=None):\\n self.record_result('dhcp', info=target_mac, ip=target_ip, state=state, exception=exception)\\n self.target_mac = target_mac\\n self.target_ip = target_ip\\n if exception:\\n self._state_transition(_STATE.ERROR, _STATE.DHCP)\\n self.runner.target_set_error(self.port_set, exception)\\n else:\\n self._state_transition(_STATE.BASE, _STATE.DHCP)\",\n \"def allocate_hosting_port(self, context, router_id, port_db, network_type,\\n hosting_device_id):\\n pass\",\n \"def create_host(self, conf, tenant_id, network_id, params):\\n\\t\\tpass\",\n \"def _create_router_port(self, method, api, header, data):\\n self._execute_api(method, api, header, data)\",\n \"def _NewConnection(self, *args, **kwargs):\\n return Connection(*args, dhcp_method=Connection.DHCP_DHCPCD, **kwargs)\",\n \"def _gen_cmd(cmd, address):\\n family = {4: 'inet', 6: 'inet6'}[address[0].version]\\n args = ['addr', cmd, '%s/%s' % (address[0], address[1])]\\n if family == 'inet' and cmd == 'add':\\n args += ['brd', '+']\\n args += ['dev', real_ifname]\\n if family == 'inet6':\\n args = ['-6'] + args\\n return args\",\n \"def create(openstack_resource):\\n # Update port config before create port\\n _update_port_config(openstack_resource.config)\\n\\n # Create port\\n created_resource = openstack_resource.create()\\n ipv4_list, ipv6_list = _get_fixed_ips_from_port(created_resource)\\n fixed_ips = ipv4_list + ipv6_list\\n _export_ips_to_port_instance(ipv4_list, ipv6_list)\\n\\n # Handle runtime properties\\n update_runtime_properties(\\n {\\n RESOURCE_ID: created_resource.id,\\n 'fixed_ips': fixed_ips,\\n 'mac_address': created_resource.mac_address,\\n 'allowed_address_pairs': created_resource.allowed_address_pairs,\\n }\\n )\",\n \"def process_create_port(self, context, port, result):\\n\\n # Process extension data\\n port_ext = self._create_port_ext(result, port, context=context)\\n switchports = self._update_switchports(result, port,\\n session=context.session)\\n self._find_port_dict_extensions(result, None, port_ext=port_ext,\\n switchports=switchports,\\n session=context.session)\\n\\n # Validate we can actually configure this port\\n if result[\\\"commit\\\"]:\\n self._validate_port_can_commit(result, None,\\n session=context.session)\",\n \"def _NewConnection(self, *args, **kwargs):\\n kwargs.setdefault('dhcp_method', Connection.DHCP_DHCPCD)\\n\\n return Connection(*args, **kwargs)\",\n \"def setup_dhcp_config(self, board_config):\\n raise NotImplementedError\",\n \"def create_test_port(**kw):\\n port = get_test_port(**kw)\\n # Let DB generate ID if it isn't specified explicitly\\n if 'id' not in kw:\\n del port['id']\\n dbapi = db_api.get_instance()\\n return dbapi.create_port(port)\",\n \"def create_port_postcommit(self, context):\\n if self.rpc_handler is None:\\n return\\n port = self._get_port_info(context)\\n if port is not None:\\n try:\\n self.rpc_handler.create_port(port)\\n except:\\n pass\",\n \"def create_dhcp_pool(options, vsm_obj, range, default_gateway):\\n edge = Edge(vsm_obj, '4.0')\\n edge_id = get_edge(vsm_obj)\\n edge.id = edge_id\\n\\n dhcp_py_dict = {\\n 'enabled': True,\\n 'logging': {'loglevel': 'info', 'enable': False},\\n 'ippools': [\\n {\\n 'autoconfiguredns': True,\\n 'defaultGateway': default_gateway,\\n 'iprange': range,\\n }\\n ],\\n }\\n dhcp_client = DHCP(edge)\\n print(\\\"Creating dhcp ippool with range %s\\\" % range)\\n dhcp_schema_object = dhcp_client.get_schema_object(dhcp_py_dict)\\n existing_dhcp_schema = dhcp_client.read()\\n if existing_dhcp_schema and existing_dhcp_schema.ipPools:\\n print \\\"append dhcp ippool to existing list\\\"\\n dhcp_schema_object.ipPools = existing_dhcp_schema.ipPools + \\\\\\n dhcp_schema_object.ipPools\\n result = dhcp_client.create(dhcp_schema_object)\\n\\n if (result[0].response.status != 204):\\n r_vars = vars(result[0])\\n print(\\\"Create IP Pool error: %s\\\" % result[0].response.reason)\\n print ', '.join(\\\"%s: %s\\\" % item for item in r_vars.items())\\n return False\\n return True\",\n \"def start_daemon(*,\\n port: int=0,\\n udp_port: int=0,\\n websocket_port: int=0,\\n label: str,\\n method: str='npx',\\n verbose: int=0,\\n host: str='',\\n public_url: str='',\\n noudp: bool=False,\\n isbootstrap: bool=False,\\n nomulticast: bool=False,\\n static_config: str='',\\n node_arg: List[str]=[]\\n):\\n from kachery_p2p import __version__\\n\\n if _probe_daemon() is not None:\\n raise Exception('Cannot start daemon. Already running.')\\n\\n api_port = _api_port()\\n api_host = _api_host()\\n config_dir = os.getenv('KACHERY_P2P_CONFIG_DIR', f'{pathlib.Path.home()}/.kachery-p2p')\\n\\n start_args = []\\n if isbootstrap:\\n start_args.append(f'--isbootstrap')\\n if noudp:\\n start_args.append(f'--noudp')\\n if nomulticast:\\n start_args.append(f'--nomulticast')\\n start_args.append(f'--verbose {verbose}')\\n if host:\\n start_args.append(f'--host {host}')\\n if public_url:\\n start_args.append(f'--public-url {public_url}')\\n if websocket_port > 0:\\n start_args.append(f'--websocket-port {websocket_port}')\\n if udp_port is not None:\\n start_args.append(f'--udp-port {udp_port}')\\n if static_config:\\n start_args.append(f'--static-config {static_config}')\\n start_args.append(f'--label {label}')\\n start_args.append(f'--http-port {port}')\\n\\n assert method in ['npx', 'dev'], f'Invalid method for start_daemon: {method}'\\n\\n thisdir = os.path.dirname(os.path.realpath(__file__))\\n if method == 'npx':\\n try:\\n subprocess.check_call(['npx', 'check-node-version', '--print', '--node', '>=12'])\\n except:\\n raise Exception('Please install nodejs version >=12. This is required in order to run kachery-p2p-daemon.')\\n \\n \\n for na in node_arg:\\n start_args.append(f'--node-arg={na}')\\n\\n npm_package = f'{thisdir}/kachery-p2p-daemon-0.7.1.tgz'\\n if not os.path.exists(npm_package):\\n raise Exception(f'No such file: {npm_package}')\\n \\n ss = ShellScript(f'''\\n #!/bin/bash\\n set -ex\\n\\n export KACHERY_P2P_API_PORT=\\\"{api_port}\\\"\\n export KACHERY_P2P_API_HOST=\\\"{api_host}\\\"\\n export KACHERY_P2P_CONFIG_DIR=\\\"{config_dir}\\\"\\n npm install -g -y {npm_package}\\n exec kachery-p2p-daemon start {' '.join(start_args)}\\n ''')\\n ss.start()\\n try:\\n retcode = ss.wait()\\n finally:\\n ss.stop()\\n ss.kill()\\n elif method == 'dev':\\n ss = ShellScript(f'''\\n #!/bin/bash\\n set -ex\\n\\n export KACHERY_P2P_API_PORT=\\\"{api_port}\\\"\\n export KACHERY_P2P_API_HOST=\\\"{api_host}\\\"\\n export KACHERY_P2P_CONFIG_DIR=\\\"{config_dir}\\\"\\n cd {thisdir}/../daemon\\n # exec node_modules/ts-node/dist/bin.js {' '.join(node_arg)} ./src/cli.ts start {' '.join(start_args)}\\n exec node {' '.join(node_arg)} -r ts-node/register ./src/cli.ts start {' '.join(start_args)}\\n ''')\\n ss.start()\\n try:\\n ss.wait()\\n finally:\\n ss.stop()\\n ss.kill()\\n else:\\n raise Exception(f'Invalid method for starting daemon: {method}')\",\n \"def start(self, ifname = None):\\n \\n if not self._slave_dhcp_process is None:\\n raise Exception('DhcpClientAlreadyStarted')\\n \\n if not ifname is None:\\n self._ifname = ifname\\n \\n if self._ifname is None:\\n raise Exception('NoInterfaceProvided')\\n \\n self._slave_dhcp_process = SlaveDhcpClientProcess(dhcp_client_daemon_exec_path=self._dhcp_client_daemon_exec_path, ifname=self._ifname, logger=logger)\\n self._slave_dhcp_process.start()\\n self._new_lease_event.clear()\\n self._dhcp_client_ctrl = RemoteDhcpClientControl(ifname=self._ifname) # Create a RemoteDhcpClientControl object that symbolizes the control on the remote process (over D-Bus)\\n self._dhcp_client_ctrl.notifyNewLease(self._got_new_lease) # Ask underlying RemoteDhcpClientControl object to call self._new_lease_retrieved() as soon as we get a new lease \\n logger.debug('DHCP client started on ' + self._ifname)\\n slave_pid = self._dhcp_client_ctrl.getRemotePid()\\n if slave_pid is None:\\n logger.warn('Could not get remote process PID')\\n raise('RemoteCommunicationError')\\n else:\\n logger.debug('Slave has PID ' + str(slave_pid)) \\n self._slave_dhcp_process.addSlavePid(slave_pid)\\n\\n self._dhcp_client_ctrl.sendDiscover()\",\n \"def test_port_create_with_binding_and_no_subnets(self):\\n with self.network() as network:\\n segment = self._test_create_segment(\\n network_id=network['network']['id'],\\n physical_network='physnet',\\n network_type=constants.TYPE_VLAN)\\n\\n # Map the host to the segment\\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n\\n # No subnets, so no allocation. But, it shouldn't be an error.\\n self.assertEqual(0, len(res['port']['fixed_ips']))\",\n \"def port_cmd(self):\\n print_debug(\\\"Executing PORT\\\")\\n # PORT creates a new connection from server to client.\\n sock = new_socket()\\n self.port_connection(sock)\\n # Get required parameters for PORT command.\\n port_params, host_ip, host_port = self.parse_port_req(sock)\\n print_debug(\\\"PARAMS: \\\" + port_params)\\n command = \\\"PORT %s\\\\r\\\\n\\\" % port_params\\n msg_rec = self.send_and_log(self.s, command)\\n print_debug(msg_rec)\\n return msg_rec, sock\",\n \"def _RunDHCPCD(self, **kwargs):\\n del kwargs\\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\\n interface=self.interface)\\n # -K: Don't receive link messages for carrier status. You should\\n # only have to use this with buggy device drivers or running\\n # dhcpcd through a network manager.\\n # -c: Location to the hooks file. If the default location happens to be\\n # empty, dhcpcd will fail. So we set the hooks file to /dev/null.\\n dhcp_command = ('dhcpcd -K -t {timeout} -c /dev/null {interface}').format(\\n timeout=self._dhcp_timeout,\\n interface=self.interface)\\n dhcp_timeout_command = 'timeout {timeout} {cmd}'.format(\\n timeout=self._dhcp_timeout,\\n cmd=dhcp_command)\\n force_kill_command = 'pgrep dhcpcd | xargs -r kill -9'\\n\\n logging.info('Killing any existing dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n logging.info('Clearing any existing ifconfig networks...')\\n self._device.Call(clear_ifconfig_command)\\n\\n logging.info('Starting dhcpcd...')\\n self._device.CheckCall(dhcp_timeout_command)\\n\\n logging.info('Verifying IP address...')\\n ip = self._LeasedIP()\\n if not ip:\\n self._device.Call(force_kill_command)\\n raise WiFiError('DHCP bind failed')\\n logging.info('Success: bound to IP %s', ip)\\n\\n yield ip # We have bound an IP; yield back to the caller.\\n\\n logging.info('Killing any remaining dhcpcd processes...')\\n self._device.Call(force_kill_command)\\n\\n yield # We have released the IP.\",\n \"def _send_port_command(self, host: str, port: int):\\n hbytes = host.split('.')\\n pbytes = [repr(port//256), repr(port%256)]\\n cmd = \\\"PORT \\\" + \\\",\\\".join(hbytes + pbytes) + CRLF\\n response = self.send_cmd(cmd.encode())\\n return response\",\n \"def main():\\n\\n args = TrafficScriptArg(['tx_src_ip', 'tx_dst_ip'])\\n\\n tx_if = args.get_arg('tx_if')\\n rx_if = args.get_arg('rx_if')\\n\\n rxq = RxQueue(rx_if)\\n txq = TxQueue(tx_if)\\n\\n tx_src_ip = args.get_arg('tx_src_ip')\\n tx_dst_ip = args.get_arg('tx_dst_ip')\\n\\n sent_packets = []\\n\\n dhcp_discover = Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\") / \\\\\\n IP(src=tx_src_ip, dst=tx_dst_ip) / \\\\\\n UDP(sport=UDP_SERVICES.bootpc, dport=UDP_SERVICES.bootps) / \\\\\\n BOOTP(op=1,) / \\\\\\n DHCP(options=[(\\\"message-type\\\", \\\"discover\\\"),\\n \\\"end\\\"])\\n\\n sent_packets.append(dhcp_discover)\\n txq.send(dhcp_discover)\\n\\n for _ in range(10):\\n dhcp_discover = rxq.recv(2)\\n if is_discover(dhcp_discover):\\n break\\n else:\\n raise RuntimeError(\\\"DHCP DISCOVER Rx timeout\\\")\\n\\n sys.exit(0)\",\n \"def create_port_forward_rule(self, ipaddressid, protocol, virtualmachineid,\\n privateport, privateendport,\\n publicport, publicendport): \\n params = {'command':'createPortForwardingRule',\\n 'ipaddressid':ipaddressid,\\n 'protocol':protocol,\\n 'privateport':privateport,\\n 'privateendport':privateendport,\\n 'publicport':publicport,\\n 'publicendport':publicendport,\\n 'virtualmachineid':virtualmachineid,\\n 'openfirewall':False} \\n\\n try:\\n response = self.send_request(params)\\n res = json.loads(response)\\n clsk_job_id = res['createportforwardingruleresponse']['jobid']\\n self.logger.debug('Start job - createPortForwardingRule: %s' % res)\\n return clsk_job_id\\n except KeyError as ex:\\n raise ClskError('Error parsing json data: %s' % ex)\\n except ApiError as ex:\\n raise ClskError(ex)\",\n \"def set_dhcp_conn(nic):\\n nic.EnableDHCP()\\n # After static DNS servers are specified to start using Dynamic Host\\n # Configuration Protocol (DHCP) instead of static DNS servers,\\n # you can call the method without supplying \\\"in\\\" parameters.\\n nic.SetDNSServerSearchOrder()\",\n \"def test_build_command_port(self):\\n actual_result = IperfServerCommandBuilder()\\\\\\n .set_port(IPERF_PORT)\\\\\\n .build_server_command()\\n self.assertListEqual(actual_result, ['iperf', '-s', '-p', '22'])\",\n \"def host_create(self,hostname,hostip,groupid,templateid):\\n data = json.dumps(\\n {\\n \\\"jsonrpc\\\": \\\"2.0\\\",\\n \\\"method\\\": \\\"host.create\\\",\\n \\\"params\\\": {\\n \\\"host\\\": hostname,\\n \\\"interfaces\\\": [\\n {\\n \\\"type\\\": 1,\\n \\\"main\\\": 1,\\n \\\"useip\\\": 1,\\n \\\"ip\\\": hostip,\\n \\\"dns\\\": \\\"\\\",\\n \\\"port\\\": \\\"10050\\\"\\n }\\n ],\\n \\\"groups\\\": groupid, \\n \\\"templates\\\":templateid,\\n\\n },\\n \\\"auth\\\": self.authID,\\n \\\"id\\\": 1 \\n })\\n res = self.get_data(data)['result']\\n if (res !=0) and (len(res) != 0):\\n return res\\n else:\\n return 0\",\n \"def create_relay08_cmd_ifwi(port_id, ifwi, stime=0.5, comport='/dev/ttyUSB4', host_passwd='123456'):\\n cmd = 'echo '+host_passwd+' | sudo -S python runner/operation_lib/base_lib/relay08_ctrl_button.py'\\n if port_id in range(1, 9):\\n cmd = cmd + ' -p ' + str(port_id)\\n else:\\n print '******please input correct port_id which in (1~8)********'\\n sys.exit()\\n\\n if not stime == 0.5:\\n print 'stime ==== ', stime\\n cmd = cmd + ' -t ' + str(stime)\\n if not comport == '/dev/ttyUSB4':\\n cmd = cmd + ' -c ' + str(comport)\\n\\n if ifwi in range(1, 9):\\n cmd = cmd + ' -i ' + str(ifwi)\\n os.system(cmd)\",\n \"def dhcp_agent_network_add(self, dhcp_net_info):\\n self.turn_on_dhcp_check()\",\n \"def start(self):\\n if self.isRunning():\\n raise Exception('DhcpClientAlreadyStarted')\\n cmd = ['sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S']\\n if self._logger is not None:\\n self._logger.debug('Running command ' + str(cmd))\\n #self._slave_dhcp_client_proc = robot.libraries.Process.Process()\\n #self._slave_dhcp_client_proc.start_process('sudo', self._slave_dhcp_client_path, '-i', self._ifname, '-A', '-S')\\n self._slave_dhcp_client_proc = subprocess.Popen(cmd)#, stdout=open(os.devnull, 'wb'), stderr=subprocess.STDOUT)\\n self._slave_dhcp_client_pid = self._slave_dhcp_client_proc.pid\\n self.addSlavePid(self._slave_dhcp_client_proc.pid) # Add the PID of the child to the list of subprocesses (note: we get sudo's PID here, not the slave PID, that we will get later on via D-Bus (see RemoteDhcpClientControl.getPid())\",\n \"def relay(self, pid, remote, local=0):\\n\\t\\tif self.client is None:\\n\\t\\t\\traise UsageError(\\\"Not connected!\\\")\\n\\t\\treturn self.client.relay_port(pid, remote, local)\",\n \"def open(self):\\n # Move all of the connection arguments into connect_args\\n connect_args = {}\\n\\n # check for mode\\n if self.get_option('port') is None:\\n if self.get_option('mode') == 'telnet':\\n connect_args['port'] = 23\\n elif self.get_option('mode') == 'serial':\\n connect_args['port'] = '/dev/ttyUSB0'\\n else:\\n connect_args['port'] = 830\\n else:\\n connect_args['port'] = self.get_option('port')\\n\\n if (self.get_option('mode') == 'telnet' or\\n self.get_option('mode') == 'serial'):\\n if self.get_option('baud') is None:\\n # Default baud if serial or telnet mode\\n connect_args['baud'] = 9600\\n if self.get_option('attempts') is None:\\n # Default attempts if serial or telnet mode\\n connect_args['attempts'] = 10\\n\\n connect_args['host'] = self.get_option('host')\\n # connect_args['port'] = self.get_option('port')\\n connect_args['user'] = self.get_option('remote_user')\\n connect_args['passwd'] = self.get_option('password')\\n connect_args['ssh_private_key_file'] = self.get_option('private_key_file')\\n connect_args['ssh_config'] = self.get_option('pyez_ssh_config')\\n connect_args['timeout'] = self.get_option('persistent_connect_timeout')\\n try:\\n log_connect_args = dict(connect_args)\\n log_connect_args[\\\"passwd\\\"] = \\\"NOT_LOGGING_PARAMETER\\\"\\n\\n self.queue_message(\\\"vvvv\\\", \\\"Creating device parameters: %s\\\" % log_connect_args)\\n timeout = connect_args.pop(\\\"timeout\\\")\\n self.dev = jnpr.junos.device.Device(**connect_args)\\n self.queue_message(\\\"vvvv\\\", \\\"Opening device.\\\")\\n self.dev.open()\\n self.queue_message(\\\"vvvv\\\", \\\"Device opened.\\\")\\n\\n self.dev.timeout = self.get_option('persistent_command_timeout')\\n self.queue_message(\\\"vvvv\\\", \\\"Setting default device timeout to %d.\\\" % timeout)\\n # Exceptions raised by close() or open() are all sub-classes of\\n # ConnectError, so this should catch all connection-related exceptions\\n # raised from PyEZ.\\n except pyez_exception.ConnectError as ex:\\n raise AnsibleError(\\\"Unable to make a PyEZ connection: %s\\\" % (str(ex)))\",\n \"def test_port_create_with_binding_information_fallback(self):\\n with self.network() as network:\\n with self.subnet(network=network,\\n ip_version=constants.IP_VERSION_6,\\n cidr='2001:db8:0:0::/64') as subnet:\\n segment = self._test_create_segment(\\n network_id=network['network']['id'],\\n physical_network='physnet',\\n network_type=constants.TYPE_VLAN)\\n\\n self._validate_l2_adjacency(network['network']['id'], is_adjacent=True)\\n\\n # Map the host to the segment\\n self._setup_host_mappings([(segment['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n\\n res = self.deserialize(self.fmt, response)\\n self._validate_immediate_ip_allocation(res['port']['id'])\\n\\n # Since the subnet is not on a segment, fall back to it\\n self._assert_one_ip_in_subnet(response, subnet['subnet']['cidr'])\",\n \"def run(self, network_create_args=None,\\n port_create_args=None, ports_per_network=1):\\n network = self._get_or_create_network(**(network_create_args or {}))\\n for i in range(ports_per_network):\\n self.neutron.create_port(network[\\\"id\\\"], **(port_create_args or {}))\\n\\n self.neutron.list_ports()\",\n \"def test_port_create_on_unconnected_host(self):\\n network, segment, _subnet = self._create_test_segment_with_subnet()\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n\\n self.assertEqual(webob.exc.HTTPConflict.code, response.status_int)\\n self.assertEqual(segment_exc.HostNotConnectedToAnySegment.__name__,\\n res['NeutronError']['type'])\\n\\n # Ensure that mapping the segment to other hosts doesn't trip it up\\n self._setup_host_mappings([(segment['segment']['id'], 'otherhost')])\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n\\n self.assertEqual(webob.exc.HTTPConflict.code, response.status_int)\\n self.assertEqual(segment_exc.HostNotConnectedToAnySegment.__name__,\\n res['NeutronError']['type'])\",\n \"def test_port_create_on_multiconnected_host(self):\\n network, segments, subnets = self._create_test_segments_with_subnets(2)\\n\\n # This host is bound to multiple hosts\\n self._setup_host_mappings([(segments[0]['segment']['id'], 'fakehost'),\\n (segments[1]['segment']['id'], 'fakehost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n self.deserialize(self.fmt, response)\\n\\n # multi segments supported since Antelope.\\n self.assertEqual(webob.exc.HTTPCreated.code, response.status_int)\",\n \"def test_port_create_with_binding_information(self):\\n network, segments, subnets = self._create_test_segments_with_subnets(3)\\n\\n # Map the host to the middle segment (by mocking host/segment mapping)\\n self._setup_host_mappings([\\n (segments[1]['segment']['id'], 'fakehost'),\\n (segments[1]['segment']['id'], 'otherhost'),\\n (segments[0]['segment']['id'], 'thirdhost')])\\n\\n response = self._create_port(self.fmt,\\n net_id=network['network']['id'],\\n tenant_id=network['network']['tenant_id'],\\n is_admin=True,\\n arg_list=(portbindings.HOST_ID,),\\n **{portbindings.HOST_ID: 'fakehost'})\\n res = self.deserialize(self.fmt, response)\\n self._validate_immediate_ip_allocation(res['port']['id'])\\n\\n # Since host mapped to middle segment, IP must come from middle subnet\\n self._assert_one_ip_in_subnet(response, subnets[1]['subnet']['cidr'])\",\n \"def network_create_end(self, payload):\\n network_id = payload['network']['id']\\n self.enable_dhcp_helper(network_id)\",\n \"def create_and_bridge_iperf_virtual_interface(self):\\n\\n for i in self._nodes.items():\\n node = i[1]\\n\\n # Show the current bridge and interface configuration\\n print(\\\"\\\\nThis the current bridge configuration:\\\")\\n ifaces = VPPUtil.show_bridge(node)\\n question = \\\"\\\\nWould you like to keep this configuration [Y/n]? \\\"\\n answer = self._ask_user_yn(question, \\\"y\\\")\\n if answer == \\\"y\\\":\\n self._sockfilename = \\\"/var/run/vpp/{}.sock\\\".format(\\n ifaces[0][\\\"name\\\"].replace(\\\"/\\\", \\\"_\\\")\\n )\\n if os.path.exists(self._sockfilename):\\n continue\\n\\n # Create a script that builds a bridge configuration with\\n # physical interfaces and virtual interfaces\\n ints_with_vints = self._iperf_vm_questions(node)\\n content = \\\"\\\"\\n for intf in ints_with_vints:\\n vhoststr = \\\"\\\\n\\\".join(\\n [\\n \\\"comment { The following command creates the socket }\\\",\\n \\\"comment { and returns a virtual interface }\\\",\\n \\\"comment {{ create vhost-user socket \\\"\\n \\\"/var/run/vpp/sock{}.sock server }}\\\\n\\\".format(intf[\\\"bridge\\\"]),\\n ]\\n )\\n\\n setintdnstr = \\\"set interface state {} down\\\\n\\\".format(intf[\\\"name\\\"])\\n\\n setintbrstr = \\\"set interface l2 bridge {} {}\\\\n\\\".format(\\n intf[\\\"name\\\"], intf[\\\"bridge\\\"]\\n )\\n setvintbrstr = \\\"set interface l2 bridge {} {}\\\\n\\\".format(\\n intf[\\\"virtualinterface\\\"], intf[\\\"bridge\\\"]\\n )\\n\\n # set interface state VirtualEthernet/0/0/0 up\\n setintvststr = \\\"set interface state {} up\\\\n\\\".format(\\n intf[\\\"virtualinterface\\\"]\\n )\\n\\n # set interface state VirtualEthernet/0/0/0 down\\n setintupstr = \\\"set interface state {} up\\\\n\\\".format(intf[\\\"name\\\"])\\n\\n content += (\\n vhoststr\\n + setintdnstr\\n + setintbrstr\\n + setvintbrstr\\n + setintvststr\\n + setintupstr\\n )\\n\\n # Write the content to the script\\n rootdir = node[\\\"rootdir\\\"]\\n filename = rootdir + \\\"/vpp/vpp-config/scripts/create_iperf_vm\\\"\\n with open(filename, \\\"w+\\\") as sfile:\\n sfile.write(content)\\n\\n # Execute the script\\n cmd = \\\"vppctl exec {}\\\".format(filename)\\n (ret, stdout, stderr) = VPPUtil.exec_command(cmd)\\n if ret != 0:\\n logging.debug(stderr)\\n\\n print(\\\"\\\\nA script as been created at {}\\\".format(filename))\\n print(\\\"This script can be run using the following:\\\")\\n print(\\\"vppctl exec {}\\\\n\\\".format(filename))\",\n \"def createPort():\\n\\n starting_interval = 0\\n ending_interval = 65535\\n port = randint(starting_interval, ending_interval)\\n return port\",\n \"def elAddNetworkConfigurationWithDhcp(self, device):\\n commandSection = self.sectionByName(\\\"command\\\")\\n # see http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Installation_Guide/s1-kickstart2-options.html\\n deviceMatch = re.match(r\\\"([^0-9]+)([0-9])\\\", device)\\n if deviceMatch:\\n # e.g. \\\"eth0\\\"\\n devicePrefix = deviceMatch.group(1)\\n deviceNumber = deviceMatch.group(2)\\n deviceNumber = int(deviceNumber)\\n for i in range(8, deviceNumber - 1, -1):\\n deviceI = devicePrefix + str(i)\\n deviceIPlus1 = devicePrefix + str(i + 1)\\n # move up by one device each network configuration\\n commandSection.string = re.sub(r\\\"(?m)^([ \\\\t]*network[ \\\\t]+.*--device[ \\\\t]*(?:=|[ \\\\t])[ \\\\t]*)\\\" + re.escape(deviceI) + r\\\"(.*)$\\\",\\n r\\\"\\\\g<1>\\\" + deviceIPlus1 + r\\\"\\\\g<2>\\\",\\n commandSection.string)\\n # not --noipv6\\n networkConfiguration = \\\"network --device=\\\" + device + \\\" --bootproto=dhcp --onboot=yes --activate\\\"\\n if deviceMatch and deviceNumber == 0:\\n # having configuration of eth0 first appears to be more conducive to overall success,\\n # and also, per http://fedoraproject.org/wiki/Anaconda/Kickstart#network, supposedly\\n # \\\"... in installer environment. Device of the first network command is activated if network is required,\\n # e.g. in case of network installation ...\\\",\\n commandSection.string = networkConfiguration + \\\"\\\\n\\\" \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + commandSection.string\\n else:\\n commandSection.string = commandSection.string \\\\\\n + \\\"#\\\\n\\\" \\\\\\n + networkConfiguration + \\\"\\\\n\\\"\",\n \"def setHostRoute( self, ip, intf ):\\n return self.cmd( 'route add -host ' + ip + ' dev ' + intf )\",\n \"def launch_proxy(local_port, remote_port, remote_ip):\\n\\n path = os.path.abspath(os.path.dirname(__file__))\\n ws = os.path.join(path, \\\"../../proxy/bin/wistar_proxy.py\\\")\\n\\n wistar_proxy_path = os.path.abspath(ws)\\n\\n cmd = \\\"/usr/bin/env python %s --local-port=%s --remote-ip=%s --remote-port=%s &\\\" % (wistar_proxy_path,\\n local_port,\\n remote_ip,\\n remote_port)\\n\\n logger.debug(cmd)\\n\\n proc = subprocess.Popen(cmd, shell=True, close_fds=True)\\n time.sleep(1)\\n return proc.pid\",\n \"def __init__(self, dhcp_client_daemon_exec_path, ifname = None):\\n self._dhcp_client_daemon_exec_path = dhcp_client_daemon_exec_path\\n self._ifname = ifname\\n self._slave_dhcp_process = None\\n self._dhcp_client_ctrl = None # Slave DHCP client process not started\\n self._new_lease_event = threading.Event() # At initialisation, event is cleared\",\n \"def test_add_port():\\n platform = DockerPlatform(None, None)\\n platform.pre_build()\\n\\n node1 = Node(identifier='host1', type='host')\\n host1 = platform.add_node(node1)\\n assert platform.nmlnode_node_map[node1.identifier] is not None\\n\\n # Add ports\\n p1 = BidirectionalPort(identifier='p1')\\n platform.add_biport(node1, p1)\\n p2 = BidirectionalPort(identifier='p2')\\n platform.add_biport(node1, p2)\\n p3 = BidirectionalPort(identifier='p3')\\n platform.add_biport(node1, p3)\\n\\n # Add link\\n link = BidirectionalLink(identifier='link')\\n platform.add_bilink((node1, p1), (node1, p2), link)\\n\\n platform.post_build()\\n\\n result = host1('ip link list')\\n\\n platform.destroy()\\n\\n assert 'p1: <BROADCAST,MULTICAST> ' in result\\n assert 'p2: <BROADCAST,MULTICAST> ' in result\\n assert 'p3: <BROADCAST,MULTICAST> ' in result\",\n \"def dhcp_release(ifname):\\n\\n logging.debug('Releasing %s...', ifname)\\n\\n try:\\n subprocess.call(['dhcpcd', '--release', ifname])\\n except OSError, err:\\n if err.errno != errno.ENOENT:\\n raise err\\n\\n try:\\n call_timeout(['dhclient', '-1', '-r', ifname], timeout=5)\\n except OSError, err:\\n if err.errno == errno.ENOENT:\\n logging.critical('Neither dhcpcd nor dhclient were found.')\\n else:\\n raise err\",\n \"def port(self, arguments):\\n instance_name = arguments['<instance>']\\n instance_name = self.activate(instance_name)\\n\\n vmrun = VMrun(self.vmx, user=self.user, password=self.password)\\n for network in vmrun.listHostNetworks().split('\\\\n'):\\n network = network.split()\\n if len(network) > 2 and network[2] == 'nat':\\n print(vmrun.listPortForwardings(network[1]))\\n break\\n else:\\n puts_err(colored.red(\\\"Cannot find a nat network\\\"))\",\n \"def make_port(self, options=mock_options):\\n maker = self.port_maker(sys.platform)\\n if not maker:\\n return None\\n\\n port = maker(options=options)\\n if hasattr(options, \\\"results_directory\\\"):\\n port._options.results_directory = port.results_directory()\\n return port\",\n \"def run(self, network_create_args=None,\\n port_create_args=None, ports_per_network=1):\\n network = self._get_or_create_network(**(network_create_args or {}))\\n for i in range(ports_per_network):\\n port = self.neutron.create_port(\\n network[\\\"id\\\"], **(port_create_args or {}))\\n\\n self.neutron.get_port(port[\\\"id\\\"])\",\n \"def test_shell():\\n platform = DockerPlatform(None, None)\\n platform.pre_build()\\n\\n # Add node\\n node1 = Node(identifier='host1', type='host')\\n host1 = platform.add_node(node1)\\n\\n # Add ports\\n p1 = BidirectionalPort(identifier='p1')\\n platform.add_biport(host1, p1)\\n p2 = BidirectionalPort(identifier='p2')\\n platform.add_biport(host1, p2)\\n p3 = BidirectionalPort(identifier='p3')\\n platform.add_biport(host1, p3)\\n\\n platform.post_build()\\n\\n reply = host1('echo \\\"var\\\"')\\n\\n platform.destroy()\\n\\n assert 'var' in reply\",\n \"def create_port(self, network_name, port_name=\\\"port\\\",\\n security_groups=None, net_id=None, **kwargs):\\n LOG_OBJ.debug(\\\"Creating Port : \\\")\\n\\n if net_id is None:\\n net_id = self.get_net_id(network_name)\\n if not isinstance(net_id, unicode):\\n return\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":9696/v2.0/ports.json\\\"\\n _headers = {'x-auth-token': self.project_info[\\\"token_project\\\"],\\n 'content-type': 'application/json'}\\n _port_info = {\\\"port\\\": {\\\"network_id\\\": net_id,\\n \\\"tenant_id\\\": self.project_info[\\\"project_id\\\"],\\n \\\"name\\\": port_name,\\n \\\"admin_state_up\\\": True,\\n \\\"port_security_enabled\\\": kwargs.get(\\n 'port_security_enabled', True)\\n }}\\n if security_groups is not None:\\n _port_info[\\\"port\\\"][\\\"security_groups\\\"] = security_groups\\n _body = json.dumps(_port_info)\\n response = self.request(\\\"POST\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\" no response from Server\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Create port Failed with status %s\\\" %\\n response.status)\\n return response.status\\n output = json.loads(response.data)\\n\\n LOG_OBJ.info(\\\"Port Details:%s\\\" % output['port'])\\n return output['port']['id']\",\n \"def build_address(ip_address, port):\\n return f\\\"tcp://{ip_address}:{port}\\\"\",\n \"def port_nic():\",\n \"def ports(self, account_id):\\n from pureport_client.commands.accounts.ports import Command\\n return Command(self.client, account_id)\",\n \"def init_tunnel(server_address, remote_port, local_port=22):\\n def get_pid(appstring):\\n # print 'determining PID'\\n processes = subprocess.check_output(['ps', '-fx'])\\n for line in processes.splitlines():\\n if appstring in line:\\n pid = line.split()[0]\\n return pid\\n\\n global PID\\n cmd = ['ssh', '-f', '-N', '-T', '-R{}:127.0.0.1:{}'.format(remote_port,local_port), server_address]\\n try:\\n proc = Popen(cmd, preexec_fn=os.setsid)\\n proc.wait() # otherwise the PID won't be correct. proc.pid is of no help.\\n PID = get_pid(' '.join(cmd))\\n return True\\n except Exception as e:\\n print e\\n return False\",\n \"def mainProv(event, portId, ipAddr=''):\\n\\tif event == 'create':\\n\\t\\thostName = genHostname(ipAddr)\\n\\t\\tktabUpdate(hostName,action='create')\\n\\t\\tdnsUpdate(portId=portId, ipAddr=ipAddr, action='create')\\n\\tif event == 'destroy':\\n\\t\\thostName = dnsUpdate(portId, action='delete')\\n\\t\\tktabUpdate(hostName, action='delete')\",\n \"def port_factory_method(self):\\n pass\",\n \"def _spawn_vapv(self, hostnames, lb):\\n identifier = self.openstack_connector.get_identifier(lb)\\n # Initialize lists of items to clean up if operation fails\\n port_ids = []\\n security_groups = []\\n vms = []\\n try: # For rolling back objects if failure occurs...\\n # Create ports...\\n ports = {}\\n if cfg.CONF.lbaas_settings.management_mode == \\\"FLOATING_IP\\\":\\n # Primary data port (floating IP)\\n (port, sec_grp, mgmt_ip) = self.openstack_connector.create_port(\\n lb, hostnames[0], create_floating_ip=True, cluster=True,\\n identifier=identifier\\n )\\n ports[hostnames[0]] = {\\n \\\"ports\\\": {\\n \\\"data\\\": port,\\n \\\"mgmt\\\": None\\n },\\n \\\"mgmt_ip\\\": mgmt_ip,\\n \\\"cluster_ip\\\": port['fixed_ips'][0]['ip_address']\\n }\\n port_ids.append(port['id'])\\n security_groups = [sec_grp]\\n # Secondary data port (floating IP)\\n (port, junk, mgmt_ip) = self.openstack_connector.create_port(\\n lb, hostnames[1], security_group=sec_grp,\\n create_floating_ip=True, cluster=True\\n )\\n ports[hostnames[1]] = {\\n \\\"ports\\\": {\\n \\\"data\\\": port,\\n \\\"mgmt\\\": None\\n },\\n \\\"mgmt_ip\\\": mgmt_ip,\\n \\\"cluster_ip\\\": port['fixed_ips'][0]['ip_address']\\n }\\n port_ids.append(port['id'])\\n elif cfg.CONF.lbaas_settings.management_mode == \\\"MGMT_NET\\\":\\n # Primary data port (management network)\\n (data_port, data_sec_grp, junk) = self.openstack_connector.create_port(\\n lb, hostnames[0], cluster=True, identifier=identifier\\n )\\n # Primary mgmt port (management network)\\n (mgmt_port, mgmt_sec_grp, mgmt_ip) = self.openstack_connector.create_port(\\n lb, hostnames[0], mgmt_port=True, cluster=True, identifier=identifier\\n )\\n ports[hostnames[0]] = {\\n \\\"ports\\\": {\\n \\\"data\\\": data_port,\\n \\\"mgmt\\\": mgmt_port\\n },\\n \\\"mgmt_ip\\\": mgmt_ip,\\n \\\"cluster_ip\\\": mgmt_ip\\n }\\n security_groups = [data_sec_grp, mgmt_sec_grp]\\n port_ids.append(data_port['id'])\\n port_ids.append(mgmt_port['id'])\\n # Secondary data port (management network)\\n (data_port, sec_grp, junk) = self.openstack_connector.create_port(\\n lb, hostnames[1], security_group=data_sec_grp, cluster=True\\n )\\n # Secondary mgmt port (management network)\\n (mgmt_port, junk, mgmt_ip) = self.openstack_connector.create_port(\\n lb, hostnames[1], mgmt_port=True, security_group=mgmt_sec_grp,\\n cluster=True\\n )\\n ports[hostnames[1]] = {\\n \\\"ports\\\": {\\n \\\"data\\\": data_port,\\n \\\"mgmt\\\": mgmt_port\\n },\\n \\\"mgmt_ip\\\": mgmt_ip,\\n \\\"cluster_ip\\\": mgmt_ip\\n }\\n port_ids.append(data_port['id'])\\n port_ids.append(mgmt_port['id'])\\n\\n # Create instances...\\n try:\\n bandwidth = lb.bandwidth\\n if bandwidth == 0:\\n raise AttributeError()\\n except AttributeError:\\n bandwidth = self._get_setting(\\n lb.tenant_id, \\\"services_director_settings\\\", \\\"bandwidth\\\"\\n )\\n avoid = None\\n for host in hostnames:\\n # Launch vAPV...\\n vm = self.openstack_connector.create_vapv(\\n host, lb, ports[host]['ports'], avoid\\n )\\n vms.append(vm['id'])\\n # Set params for next iteration...\\n if cfg.CONF.lbaas_settings.allow_different_host_hint is True:\\n avoid = vm['id']\\n\\n except Exception as e:\\n if cfg.CONF.lbaas_settings.roll_back_on_error is True:\\n self.openstack_connector.clean_up(\\n instances=vms,\\n security_groups=security_groups,\\n ports=port_ids\\n )\\n raise e\",\n \"def __init__(self, ifname):\\n\\n self._dbus_loop = gobject.MainLoop()\\n self._bus = dbus.SystemBus()\\n wait_bus_owner_timeout = 5 # Wait for 5s to have an owner for the bus name we are expecting\\n logger.debug('Going to wait for an owner on bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n while not self._bus.name_has_owner(RemoteDhcpClientControl.DBUS_NAME):\\n time.sleep(0.2)\\n wait_bus_owner_timeout -= 0.2\\n if wait_bus_owner_timeout <= 0: # We timeout without having an owner for the expected bus name\\n raise Exception('No owner found for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n \\n logger.debug('Got an owner for bus name ' + RemoteDhcpClientControl.DBUS_NAME)\\n gobject.threads_init() # Allow the mainloop to run as an independent thread\\n dbus.mainloop.glib.threads_init()\\n \\n dbus_object_name = RemoteDhcpClientControl.DBUS_OBJECT_ROOT + '/' + str(ifname)\\n logger.debug('Going to communicate with object ' + dbus_object_name)\\n self._dhcp_client_proxy = self._bus.get_object(RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE, dbus_object_name)\\n self._dbus_iface = dbus.Interface(self._dhcp_client_proxy, RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE)\\n \\n logger.debug(\\\"Connected to D-Bus\\\")\\n self._dhcp_client_proxy.connect_to_signal(\\\"IpConfigApplied\\\",\\n self._handleIpConfigApplied,\\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\\n \\n self._dhcp_client_proxy.connect_to_signal(\\\"LeaseLost\\\",\\n self._handleLeaseLost,\\n dbus_interface = RemoteDhcpClientControl.DBUS_SERVICE_INTERFACE,\\n message_keyword='dbus_message') # Handle the IpConfigApplied signal\\n \\n #Lionel: the following line is used for D-Bus debugging only\\n #self._bus.add_signal_receiver(catchall_signal_handler, interface_keyword='dbus_interface', member_keyword='member')\\n self._dbus_loop_thread = threading.Thread(target = self._loopHandleDbus) # Start handling D-Bus messages in a background thread\\n self._dbus_loop_thread.setDaemon(True) # D-Bus loop should be forced to terminate when main program exits\\n self._dbus_loop_thread.start()\\n \\n self._bus.watch_name_owner(RemoteDhcpClientControl.DBUS_NAME, self._handleBusOwnerChanged) # Install a callback to run when the bus owner changes\\n \\n self._callback_new_lease_mutex = threading.Lock() # This mutex protects writes to the _callback_new_lease attribute\\n self._callback_new_lease = None\\n \\n self._exit_unlock_event = threading.Event() # Create a new threading event that will allow the exit() method to wait for the child to terminate properly\\n self._getversion_unlock_event = threading.Event() # Create a new threading event that will allow the GetVersion() D-Bus call below to execute within a timed limit \\n\\n self.status = DhcpLeaseStatus.DhcpLeaseStatus()\\n\\n self._getversion_unlock_event.clear()\\n self._remote_version = ''\\n self._dbus_iface.GetVersion(reply_handler = self._getVersionUnlock, error_handler = self._getVersionError)\\n if not self._getversion_unlock_event.wait(10): # We give 10s for slave to answer the GetVersion() request\\n logfile = tempfile.NamedTemporaryFile(prefix='TimeoutOnGetVersion-', suffix='.log', delete=False)\\n if logfile:\\n print('Saving TimeoutOnGetVersion environment dump to file \\\"' + logfile.name + '\\\"', file=sys.stderr)\\n print('TimeoutOnGetVersion', file=logfile)\\n subprocess.call('ps -ef', stdout=logfile, shell=True)\\n subprocess.call('perl ./dbus-introspect.pl --system com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1', stdout=logfile, shell=True)\\n subprocess.call('dbus-send --system --type=method_call --print-reply --dest=com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary /com/legrandelectric/RobotFrameworkIPC/DhcpClientLibrary/eth1 com.legrandelectric.RobotFrameworkIPC.DhcpClientLibrary.GetVersion', stdout=logfile, shell=True)\\n logfile.close()\\n raise Exception('TimeoutOnGetVersion')\\n else:\\n logger.debug('Slave version: ' + self._remote_version)\",\n \"def create_host(self, wwpns, hostname):\\n\\n if not wwpns or len(wwpns) == 0 or not hostname or len(hostname) == 0:\\n ex_args = {'wwpns': wwpns,\\n 'hostname': hostname}\\n raise SVCCreateHostParameterError(**ex_args)\\n\\n ports = ':'.join(wwpns)\\n # get the host shortname.\\n hostname_str = hostname.split('.')[0]\\n LOG.debug(\\\"enter: create_host(): wwpns=%(wwpns)s\\\"\\n \\\" hostname=%(hostname)s\\\"\\n % {'wwpns': ports, 'hostname': hostname_str})\\n\\n rand_id = str(random.randint(0, 99999999)).zfill(8)\\n host_name = '%s-%s' % (self._hostname_prefix(hostname_str), rand_id)\\n\\n cmd = 'mkhost -name %(host_name)s -hbawwpn %(ports)s -force' % locals()\\n\\n output, err_output = self._svc_command(cmd)\\n\\n if err_output:\\n # err_output should be a list type\\n if isinstance(err_output, types.ListType):\\n err_msg = err_output[0]\\n else:\\n err_msg = err_output\\n err_code = err_msg.split()[0]\\n\\n if err_code and err_code == 'CMMVC6035E':\\n # host has been defined on the storage, but we don't see it.\\n # return None and ask caller to run cfgdev to relogin to SAN\\n # and retry get_host_from_wwpns().\\n return None\\n\\n msg = (_(\\\"create_host() failure cmd=%(cmd)s, error:%(err_output)s.\\\"\\n \\\" Make sure host and storage are zoned properly and check\\\"\\n \\\" SAN fabric connectivity\\\") % locals())\\n\\n LOG.exception(msg)\\n ex_args = {'host_name': hostname_str,\\n 'err_output': err_output}\\n raise SVCCreateHostFailed(**ex_args)\\n\\n return host_name\",\n \"def start_traffic_pktgen(\\n vm_fix,\\n src_min_ip='',\\n src_max_ip='',\\n dest_ip='',\\n dest_min_port='',\\n dest_max_port=''):\\n vm_fix.logger.info(\\\"Sending traffic...\\\")\\n try:\\n cmd = '~/pktgen_new.sh %s %s %s %s %s' % (src_min_ip,\\n src_max_ip,\\n dest_ip,\\n dest_min_port,\\n dest_max_port)\\n vm_fix.run_cmd_on_vm(cmds=[cmd], as_sudo=True)\\n except Exception as e:\\n vm_fix.logger.exception(\\\"Got exception at start_traffic as %s\\\" % (e))\",\n \"def create(self, params):\\n return self.make_client_call(\\n 'create_vpn_connection_route', params)\",\n \"def InitNetwork(this_ip,fpga_ip,udp_port,timeout):\\r\\n global this_fpga_ip\\r\\n global this_sock\\r\\n global this_udp_port\\r\\n \\r\\n sock = socket.socket(socket.AF_INET, # Internet\\r\\n socket.SOCK_DGRAM) # UDP \\r\\n sock.settimeout(timeout)\\r\\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 1024*1024)\\r\\n sock.bind((this_ip, udp_port))\\r\\n this_fpga_ip = fpga_ip\\r\\n this_udp_port = udp_port\\r\\n this_sock = sock\\r\\n return sock\",\n \"def test_createPort(self):\\n store = Store()\\n factory = DummyFactory(store=store)\\n self.assertSuccessStatus(\\n self._makeConfig(store),\\n [\\\"create\\\", \\\"--strport\\\", \\\"tcp:8080\\\",\\n \\\"--factory-identifier\\\", str(factory.storeID)])\\n self.assertEqual(\\\"Created.\\\\n\\\", sys.stdout.getvalue())\\n [port] = list(store.query(StringEndpointPort))\\n self.assertEqual(u'tcp:8080', port.description)\\n self.assertEqual(list(store.interfacesFor(port)), [IService])\",\n \"def run(self, network_create_args=None,\\n port_create_args=None, ports_per_network=1):\\n network = self._get_or_create_network(**(network_create_args or {}))\\n for i in range(ports_per_network):\\n port = self.neutron.create_port(\\n network[\\\"id\\\"], **(port_create_args or {}))\\n\\n self.neutron.delete_port(port[\\\"id\\\"])\",\n \"def plug_port_into_network(self, device_id, host_id, port_id,\\n net_id, tenant_id, port_name, device_owner,\\n sg, orig_sg, vnic_type, segments=None,\\n switch_bindings=None, vlan_type=None):\",\n \"def write_option_to_dhcp_server(connection_obj, data):\\n line_number = basic_obj.get_file_number_with_regex(connection_obj, data.search_pattern, data.dhcp_config_file)\\n option = data.option_string # \\\"option dhcp6.boot-file-url \\\"\\n option_path = data.option_url\\n st.log(\\\"#####LINE NUMBER{}\\\".format(line_number))\\n option_config = \\\"'{} \\\\\\\"{}\\\\\\\";'\\\".format(option, option_path)\\n if int(line_number) > 0:\\n # line_number = data.line_number if line_number in data else 60\\n basic_obj.delete_line_using_line_number(connection_obj, line_number, data.dhcp_config_file)\\n basic_obj.write_to_file(connection_obj, option_config, data.dhcp_config_file, device=\\\"server\\\")\\n # basic_obj.write_to_file_to_line(connection_obj, option_config, line_number, data.dhcp_config_file, device=\\\"server\\\")\\n line_number = basic_obj.get_file_number_with_regex(connection_obj, data.search_pattern, data.dhcp_config_file)\\n st.log(\\\"#####LINE NUMBER{}\\\".format(line_number))\\n if line_number <= 0:\\n st.log(\\\"Written content in file {} not found\\\".format(data[\\\"dhcp_config_file\\\"]))\\n st.report_fail(\\\"content_not_found\\\")\",\n \"def _make_lcdproc(\\n lcd_host, lcd_port, retry_config,\\n charset=DEFAULT_LCDPROC_CHARSET, lcdd_debug=False):\\n\\n class ServerSpawner(utils.AutoRetryCandidate):\\n \\\"\\\"\\\"Spawn the server, using auto-retry.\\\"\\\"\\\"\\n\\n @utils.auto_retry\\n def connect(self):\\n return lcdrunner.LcdProcServer(\\n lcd_host, lcd_port, charset=charset, debug=lcdd_debug)\\n\\n spawner = ServerSpawner(retry_config=retry_config, logger=logger)\\n\\n try:\\n return spawner.connect()\\n except socket.error as e:\\n logger.error('Unable to connect to lcdproc %s:%s : %r', lcd_host, lcd_port, e)\\n raise SystemExit(1)\",\n \"def config_dhcpv6_options(ssh_conn_obj, ztp_params, config_params, options=dict(), cli_type=\\\"\\\"):\\n cli_type = st.get_ui_type(config_params.dut, cli_type=cli_type)\\n retry_count = config_params.retry_count if \\\"retry_count\\\" in config_params and config_params.retry_count else 0\\n iteration = config_params.iteration if \\\"iteration\\\" in config_params and config_params.iteration else 300\\n delay = config_params.delay if \\\"delay\\\" in config_params and config_params.delay else 3\\n expect_reboot = True if \\\"expect_reboot\\\" in options and options [\\\"expect_reboot\\\"] else False\\n st.log(config_params)\\n if \\\"func_name\\\" in config_params:\\n syslog_file_names = [\\\"syslog_1_{}\\\".format(config_params.func_name), \\\"syslog_{}\\\".format(config_params.func_name)]\\n if \\\"json_content\\\" in config_params:\\n file_path = basic_obj.write_to_json_file(config_params.json_content)\\n st.log(file_path)\\n if file_path:\\n destination_path = \\\"{}{}/{}\\\".format(config_params.home_path, ztp_params.config_path, config_params.ztp_file)\\n st.log(destination_path)\\n basic_obj.copy_file_from_client_to_server(ssh_conn_obj, src_path=file_path, dst_path=destination_path)\\n config_params.option_59_url = \\\"http://[{}]{}/{}\\\".format(config_params.static_ip, ztp_params.config_path, config_params.ztp_file)\\n config_params.search_pattern = r'\\\\s*option\\\\s+dhcp6.boot-file-url\\\\s+\\\"\\\\S+\\\";'\\n write_option_59_to_dhcp_server(ssh_conn_obj, config_params)\\n basic_obj.service_operations(ssh_conn_obj, config_params.dhcp6_service_name, \\\"restart\\\", \\\"server\\\")\\n if not verify_dhcpd_service_status(ssh_conn_obj, config_params.dhcpd6_pid):\\n st.log(\\\"{} service is running which is not expected\\\".format(config_params.dhcp6_service_name))\\n st.report_fail(\\\"service_running_not_expected\\\", config_params.dhcp6_service_name)\\n reboot_type = config_params.reboot_type if \\\"reboot_type\\\" in config_params and config_params.reboot_type else \\\"normal\\\"\\n if \\\"ztp_operation\\\" in config_params:\\n config_params.ztp_operation = \\\"reboot\\\" if cli_type == \\\"klish\\\" else config_params.ztp_operation\\n if config_params.ztp_operation == \\\"reboot\\\":\\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\\n elif config_params.ztp_operation == \\\"run\\\":\\n ztp_operations(config_params.dut, config_params.ztp_operation)\\n else:\\n st.log(\\\"ZTP operation is not mentioned hence rebooting the device ...\\\")\\n basic_obj.remove_file(config_params.dut, config_params.config_db_path)\\n st.reboot(config_params.dut, reboot_type, skip_port_wait=True)\\n if \\\"reboot_on_success\\\" in options and options[\\\"reboot_on_success\\\"]:\\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, reboot_on_success=options[\\\"reboot_on_success\\\"], cli_type=cli_type)\\n else:\\n result = verify_ztp_status(config_params.dut, retry_count, iteration, delay, expect_reboot=expect_reboot, cli_type=cli_type)\\n if not result:\\n if \\\"logs_path\\\" in config_params and \\\"func_name\\\" in config_params:\\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n if \\\"reboot_on_success\\\" in options and options[\\\"reboot_on_success\\\"]:\\n reboot_obj.config_reload(config_params.dut)\\n st.wait(5)\\n if not ip_obj.ping(config_params.dut, config_params.static_ip, family=\\\"ipv6\\\"):\\n st.log(\\\"Pinging to DHCP server failed from DUT, issue either with DUT or server\\\")\\n # intf_obj.enable_dhcp_on_interface(config_params.dut, config_params.network_port, \\\"v6\\\")\\n if not verify_ztp_status(config_params.dut, retry_count, iteration, delay, cli_type=cli_type):\\n if \\\"logs_path\\\" in config_params and \\\"func_name\\\" in config_params:\\n capture_syslogs(config_params.dut, config_params.logs_path, syslog_file_names)\\n st.log(\\\"ZTP status verification failed\\\")\\n st.report_fail(\\\"ztp_status_verification_failed\\\")\\n verify_ztp_filename_logs(config_params.dut, config_params)\\n if \\\"ztp_log_string\\\" in config_params and config_params.ztp_log_string:\\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path, config_params.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(config_params.ztp_log_path, config_params.ztp_log_string))\\n if not basic_obj.poll_for_error_logs(config_params.dut, config_params.ztp_log_path_1, config_params.ztp_log_string):\\n st.log(\\\"ZTP log {} verification failed for message {}\\\".format(config_params.ztp_log_path_1, config_params.ztp_log_string))\\n st.report_fail(\\\"ztp_log_verification_failed\\\", config_params.ztp_log_path_1, config_params.ztp_log_string)\\n if \\\"result\\\" in config_params and config_params.result == \\\"pass\\\":\\n st.report_pass(\\\"test_case_passed\\\")\",\n \"def create_host(self, port=20298):\\n # We use UDP to broadcast the host\\n self.host = Networking.Host(0.5, 0.5, 5)\\n\\n \\\"\\\"\\\"\\n # find unused ip address\\n for i in range(255, 0, -1):W\\n for j in range(255, 0, -1):\\n try:\\n print(\\\"Checking for free address at 192.168.\\\"+str(i)+\\\".\\\"+str(j))\\n # try to connect to the specified ip address\\n socket.gethostbyaddr(\\\"192.168.\\\"+str(i)+\\\".\\\"+str(j))\\n except socket.error:\\n print(\\\"Address is unused\\\")\\n # if we cannot connect to the address, that means the ip address is unused\\n server_ip = \\\"192.168.\\\"+str(i)+\\\".\\\"+str(j)\\n break\\n if server_ip is not None:\\n break\\n \\\"\\\"\\\"\\n\\n # port = self.get_open_port()\\n # listen to all address\\n server_ip = \\\"0.0.0.0\\\"\\n client_ip = \\\"localhost\\\"\\n\\n try:\\n self.host.connect(server_ip, port)\\n self.host.accepting_allow()\\n\\n # Clear the broadcast blocker\\n # self.stop_broadcast.clear()\\n # broadcaster = threading.Thread(target=self.broadcast_game, args=(None, self.stop_broadcast))\\n # broadcaster.start()\\n\\n # The host also acts as a client\\n self.join_host(client_ip, 20298, GameStateModel.instance().host)\\n except MastermindErrorSocket:\\n logger.error(\\\"Failed to create a host\\\")\",\n \"def add_port(bridge, port, external_id=None):\\n _run('ip', 'link', 'set', port, 'up')\\n _run('ovs-vsctl', 'add-port', bridge, port)\\n if external_id:\\n ports = SimpleOVSDB('ovs-vsctl', 'port')\\n for port in ports.find('name={}'.format(port)):\\n ports.set(port['_uuid'],\\n 'external_ids:{}'.format(external_id[0]),\\n external_id[1])\",\n \"def port():\",\n \"def test_build_command_daemon(self):\\n actual_result = IperfClientCommandBuilder()\\\\\\n .set_server_ip(SERVER_IP)\\\\\\n .build_client_command()\\n self.assertListEqual(actual_result, ['iperf', '-c', '192.168.1.1'])\",\n \"def cmd_port (self, line):\\r\\n info = line[1].split (',')\\r\\n ip = '.'.join (info[:4])\\r\\n port = int(info[4])*256 + int(info[5])\\r\\n # how many data connections at a time?\\r\\n # I'm assuming one for now...\\r\\n # TODO: we should (optionally) verify that the\\r\\n # ip number belongs to the client. [wu-ftpd does this?]\\r\\n self.client_addr = (ip, port)\\r\\n self.respond ('200 PORT command successful.')\",\n \"def do_nic_create(cc, args):\\n dct = utils.to_attrs_dict(args.attributes[0], VALID_FIELDS)\\n _validate(dct)\\n result = cc.nic.post(dct)\\n cliutils.print_dict(result)\",\n \"def AddPortFlag(parser, required=False):\\n help_text = \\\"\\\"\\\"\\\\\\n Network port of the database.\\n \\\"\\\"\\\"\\n parser.add_argument('--port', help=help_text, required=required, type=int)\",\n \"def poe_recycle(baseurl, cookie_header, port):\\n logging.info(\\\"Starting Power recycle for Port {}\\\".format(port))\\n cmd = \\\"configure terminal\\\"\\n common.anycli(baseurl, cmd, cookie_header)\\n cmd = \\\"interface \\\" + port\\n common.anycli(baseurl, cmd, cookie_header)\\n cmd = \\\"no power-over-ethernet\\\"\\n common.anycli(baseurl, cmd, cookie_header)\\n logging.info(\\\"Power disabled for Port {}\\\".format(port))\\n cmd = \\\"power-over-ethernet\\\"\\n common.anycli(baseurl, cmd, cookie_header)\\n logging.info(\\\"Power enabled for Port {}\\\".format(port))\",\n \"def main():\\n cli = DhcpClientCLI()\\n\\n parser = argparse.ArgumentParser(\\n description='Management CLI for Mobility DHCP Client',\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n )\\n\\n # Add sub commands\\n subparsers = parser.add_subparsers(title='subcommands', dest='cmd')\\n\\n # List\\n subparser = subparsers.add_parser(\\n 'list_dhcp_records',\\n help='Lists all records from Redis',\\n )\\n subparser.set_defaults(func=cli.list_all_record)\\n\\n # Add\\n subparser = subparsers.add_parser(\\n 'add_rec',\\n help='Add ip allocation record',\\n )\\n subparser.add_argument(\\n 'mac', help='Mac address, e.g. \\\"8a:00:00:00:0b:11\\\"',\\n type=str,\\n )\\n subparser.add_argument(\\n 'ip', help='IP address, e.g. \\\"1.1.1.1\\\"',\\n type=ip_address,\\n )\\n\\n subparser.add_argument(\\n 'state',\\n help='DHCP protocol state 1 to 7, e.g. \\\"1\\\"',\\n type=int,\\n )\\n subparser.add_argument(\\n 'subnet',\\n help='IP address subnet, e.g. \\\"1.1.1.0/24\\\"',\\n type=ipaddress.ip_network,\\n )\\n\\n subparser.add_argument('dhcp', help='DHCP IP address, e.g. \\\"1.1.1.100\\\"')\\n subparser.add_argument('lease', help='Lease time in seconds, e.g. \\\"100\\\"')\\n subparser.set_defaults(func=cli.add_record)\\n\\n # del\\n subparser = subparsers.add_parser(\\n 'del_rec',\\n help='Add ip allocation record',\\n )\\n subparser.add_argument('mac', help='Mac address, e.g. \\\"8a:00:00:00:0b:11\\\"')\\n subparser.set_defaults(func=cli.del_record)\\n\\n # set default gw\\n subparser = subparsers.add_parser(\\n 'set_default_gw',\\n help='Set default GW',\\n )\\n subparser.add_argument('ip', help='IP address, e.g. \\\"1.1.1.1\\\"')\\n\\n subparser.set_defaults(func=cli.set_deafult_gw)\\n\\n # set gw mac\\n subparser = subparsers.add_parser(\\n 'set_gw_mac',\\n help='Set GW Mac address',\\n )\\n subparser.add_argument('mac', help='Mac address, e.g. \\\"8a:00:00:00:0b:11\\\"')\\n\\n subparser.set_defaults(func=cli.set_deafult_gw)\\n\\n # Parse the args\\n args = parser.parse_args()\\n if not args.cmd:\\n parser.print_usage()\\n sys.exit(1)\\n\\n # Execute the sub-command function\\n args.func(args)\",\n \"def __init__(self, host, port=2345):\\n self.host = host\\n self.port = port\\n self.set_command_list()\",\n \"def create_port_postcommit(self, mech_context):\\n\\n LOG.debug(\\\"create_port_postcommit: called\\\")\\n port = mech_context.current\\n port_id = port['id']\\n network_id = port['network_id']\\n tenant_id = port['tenant_id']\\n host_id = mech_context._binding.host\\n context = mech_context._plugin_context\\n try:\\n network = seamicro_db.get_network(context, network_id)\\n except Exception:\\n LOG.exception(\\n _LE(\\\"SeaMicro Mechanism: failed to get network %s from db\\\"),\\n network_id)\\n raise Exception(\\n _(\\\"SeaMicro Mechanism: failed to get network %s from db\\\"),\\n network_id)\\n\\n vlan_id = network['vlan']\\n switch_ip, server_id, nics = _get_switch_info(self._switch, host_id)\\n if switch_ip is not None and server_id is not None and nics is not None:\\n try:\\n interfaces = self.client[switch_ip].interfaces.list()\\n for interface in interfaces:\\n interface.add_tagged_vlan(vlan_id)\\n\\n server = self.client[switch_ip].servers.get(server_id)\\n if nics:\\n server.set_tagged_vlan(vlan_id, nics=nics)\\n else:\\n server.set_tagged_vlan(vlan_id)\\n except seamicro_client_exception.ClientException as ex:\\n LOG.exception(\\n _LE(\\\"SeaMicro driver: failed to create port\\\"\\n \\\" with the following error: %(error)s\\\"),\\n {'error': ex.message})\\n seamicro_db.delete_port(context, port_id)\\n raise Exception(\\n _(\\\"SeaMicro Mechanism: create_port_postcommit failed\\\"))\\n\\n LOG.info(\\n _LI(\\\"created port (postcommit): port_id=%(port_id)s\\\"\\n \\\" network_id=%(network_id)s tenant_id=%(tenant_id)s\\\"\\n \\\" switch_ip=%(switch_ip)s server_id=%(server_id)s\\\"),\\n {'port_id': port_id,\\n 'network_id': network_id, 'tenant_id': tenant_id,\\n 'switch_ip': switch_ip, 'server_id': server_id})\",\n \"def run(self):\\n self.network_ctrl.connect_with_remote_system()\\n cmd = self.create_command(self.on_or_off, self.port)\\n self.network_ctrl.send_command(cmd)\\n\\n check = self._port_status(self.port)\\n result = self.network_ctrl.send_command(check)\\n result = result[0]\\n if self.on_or_off:\\n if result == \\\"1\\\":\\n self.router.mode = Mode.normal\\n logging.info(\\\"[+] Successfully switched on port \\\" + str(self.port))\\n else:\\n self.router.mode = Mode.unknown\\n logging.info(\\\"[-] Error switching on port \\\" + str(self.port))\\n else:\\n if result == \\\"0\\\":\\n self.router.mode = Mode.off\\n logging.info(\\\"[+] Successfully switched off port \\\" + str(self.port))\\n else:\\n self.router.mode = Mode.unknown\\n logging.info(\\\"[-] Error switching off port \\\" + str(self.port))\\n\\n self.network_ctrl.exit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6433571","0.62138724","0.60005367","0.59957063","0.59321785","0.59223795","0.5902261","0.58771735","0.58316875","0.5814186","0.5716689","0.5710547","0.5704256","0.56838495","0.5680676","0.5598628","0.5588033","0.5549641","0.553411","0.55322653","0.55301565","0.551734","0.54636586","0.5462498","0.5433547","0.5430332","0.53799486","0.53652537","0.5353004","0.5348902","0.53482175","0.5347151","0.5337251","0.5328807","0.53045183","0.5289826","0.52837765","0.524912","0.52286345","0.5208736","0.5191345","0.5187583","0.51867497","0.5184616","0.5182544","0.5169315","0.51627743","0.5160893","0.512758","0.5105694","0.5100075","0.50942296","0.5088017","0.5080798","0.5068022","0.5067131","0.50668734","0.5044804","0.5032515","0.50283974","0.5027325","0.5022621","0.50212145","0.5018061","0.5007291","0.49995196","0.49949893","0.49760282","0.4973219","0.49714607","0.4971416","0.49601954","0.49586272","0.49524048","0.4944104","0.49288172","0.49165204","0.4915194","0.49137247","0.4907823","0.48992583","0.4886007","0.48844898","0.48821613","0.487944","0.4877342","0.4876693","0.48746285","0.4864976","0.4862498","0.48600572","0.48455563","0.48415238","0.48357993","0.48322344","0.48286074","0.4824773","0.48194277","0.48135522","0.48113218"],"string":"[\n \"0.6433571\",\n \"0.62138724\",\n \"0.60005367\",\n \"0.59957063\",\n \"0.59321785\",\n \"0.59223795\",\n \"0.5902261\",\n \"0.58771735\",\n \"0.58316875\",\n \"0.5814186\",\n \"0.5716689\",\n \"0.5710547\",\n \"0.5704256\",\n \"0.56838495\",\n \"0.5680676\",\n \"0.5598628\",\n \"0.5588033\",\n \"0.5549641\",\n \"0.553411\",\n \"0.55322653\",\n \"0.55301565\",\n \"0.551734\",\n \"0.54636586\",\n \"0.5462498\",\n \"0.5433547\",\n \"0.5430332\",\n \"0.53799486\",\n \"0.53652537\",\n \"0.5353004\",\n \"0.5348902\",\n \"0.53482175\",\n \"0.5347151\",\n \"0.5337251\",\n \"0.5328807\",\n \"0.53045183\",\n \"0.5289826\",\n \"0.52837765\",\n \"0.524912\",\n \"0.52286345\",\n \"0.5208736\",\n \"0.5191345\",\n \"0.5187583\",\n \"0.51867497\",\n \"0.5184616\",\n \"0.5182544\",\n \"0.5169315\",\n \"0.51627743\",\n \"0.5160893\",\n \"0.512758\",\n \"0.5105694\",\n \"0.5100075\",\n \"0.50942296\",\n \"0.5088017\",\n \"0.5080798\",\n \"0.5068022\",\n \"0.5067131\",\n \"0.50668734\",\n \"0.5044804\",\n \"0.5032515\",\n \"0.50283974\",\n \"0.5027325\",\n \"0.5022621\",\n \"0.50212145\",\n \"0.5018061\",\n \"0.5007291\",\n \"0.49995196\",\n \"0.49949893\",\n \"0.49760282\",\n \"0.4973219\",\n \"0.49714607\",\n \"0.4971416\",\n \"0.49601954\",\n \"0.49586272\",\n \"0.49524048\",\n \"0.4944104\",\n \"0.49288172\",\n \"0.49165204\",\n \"0.4915194\",\n \"0.49137247\",\n \"0.4907823\",\n \"0.48992583\",\n \"0.4886007\",\n \"0.48844898\",\n \"0.48821613\",\n \"0.487944\",\n \"0.4877342\",\n \"0.4876693\",\n \"0.48746285\",\n \"0.4864976\",\n \"0.4862498\",\n \"0.48600572\",\n \"0.48455563\",\n \"0.48415238\",\n \"0.48357993\",\n \"0.48322344\",\n \"0.48286074\",\n \"0.4824773\",\n \"0.48194277\",\n \"0.48135522\",\n \"0.48113218\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.48129672"},"document_rank":{"kind":"string","value":"99"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":797,"numItemsPerPage":100,"numTotalItems":95772,"offset":79700,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjI1ODg5NywianRpIjoiZTUyOGFhYjItOGRiNy00ZDUxLTk1ZDktZWQxMTk1MDZkMzNjIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyNjI0OTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.BCVJIIPbNcPKWh-38IluGx6CW_bg3CTcREFeYJsNgfFfbHSg9-uLQOpKV9NqT5GA6RPnH4ld4waRCDPrKqaHBA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Produces a string representing one QREL entry | def genQrelStr(queryId, docId, relGrade):
return f'{queryId} 0 {docId} {relGrade}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qrelEntry2Str(qrelEntry):\n return genQrelStr(qrelEntry.queryId, qrelEntry.docId, qrelEntry.relGrade)",
"def __str__(self) -> str:\n return '[Q]: {} || [A]: {} || [{}]'.format(self.tell, self.answer, self.created.isoformat())",
"def __str__(self):\n return f'Name: {self.name}\\nISBN: {self... | [
"0.75320774",
"0.656819",
"0.6402845",
"0.6374799",
"0.6345168",
"0.6345168",
"0.6322846",
"0.6321934",
"0.62942547",
"0.62687206",
"0.62434727",
"0.62388664",
"0.6235534",
"0.62252605",
"0.622481",
"0.6222384",
"0.6222352",
"0.62121457",
"0.6204588",
"0.620208",
"0.61845046"... | 0.618273 | 21 |
Convert a parsed QREL entry to string. | def qrelEntry2Str(qrelEntry):
return genQrelStr(qrelEntry.queryId, qrelEntry.docId, qrelEntry.relGrade) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parseQrelEntry(line):\n\n line = line.strip()\n parts = line.split()\n if len(parts) != 4:\n raise Exception('QREL entry format error, expecting just 4 white-space separted field in the entry: ' + line)\n\n return QrelEntry(queryId=parts[0], docId=parts[2], relGrade=int(parts[3]))",
"def f... | [
"0.617721",
"0.61493593",
"0.61304563",
"0.5781973",
"0.5565379",
"0.54844826",
"0.5433155",
"0.53735805",
"0.5332001",
"0.52714926",
"0.525061",
"0.5247311",
"0.5247303",
"0.5220838",
"0.5220101",
"0.51887167",
"0.51810044",
"0.5169519",
"0.5159208",
"0.51539856",
"0.5140406... | 0.7916747 | 0 |
Parse one QREL entry | def parseQrelEntry(line):
line = line.strip()
parts = line.split()
if len(parts) != 4:
raise Exception('QREL entry format error, expecting just 4 white-space separted field in the entry: ' + line)
return QrelEntry(queryId=parts[0], docId=parts[2], relGrade=int(parts[3])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_result_entry(result):\n entry = ParsedEntry()\n\n if \"content\" in result and len(result.content) > 0:\n entry.content = result.content[0].value\n # if not html, have to escape\n if result.content[0].type not in HTML_MIME_TYPES:\n entry.content = cgi.escape(entry.c... | [
"0.57672024",
"0.5712222",
"0.5657319",
"0.5631491",
"0.5623684",
"0.55867475",
"0.55786633",
"0.55580145",
"0.5541458",
"0.55277073",
"0.5502825",
"0.54569316",
"0.53761744",
"0.53471315",
"0.5338925",
"0.53375584",
"0.53002876",
"0.52926755",
"0.52745557",
"0.52745557",
"0.... | 0.73139864 | 0 |
Read and parse QRELs. | def readQrels(fileName):
ln = 0
res = []
with open(fileName) as f:
for line in tqdm(f, desc='loading qrels (by line)', leave=False):
ln += 1
line = line.strip()
if not line:
continue
try:
e = parseQrelEntry(line)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parseQrelEntry(line):\n\n line = line.strip()\n parts = line.split()\n if len(parts) != 4:\n raise Exception('QREL entry format error, expecting just 4 white-space separted field in the entry: ' + line)\n\n return QrelEntry(queryId=parts[0], docId=parts[2], relGrade=int(parts[3]))",
"def r... | [
"0.6319746",
"0.5800978",
"0.5731543",
"0.5555555",
"0.54871035",
"0.5411078",
"0.5388917",
"0.53783035",
"0.53458947",
"0.53004533",
"0.5274036",
"0.5265533",
"0.5158059",
"0.5128103",
"0.50959504",
"0.5082703",
"0.50777197",
"0.5048843",
"0.5048156",
"0.50440925",
"0.502144... | 0.65417624 | 0 |
Take a dictionary of document scores indexed by the document id and produce a list of (document id, score tuples) sorted in the order of decreasing scores. | def getSorteScoresFromScoreDict(queryRunDict):
return list(sorted(queryRunDict.items(), key=lambda x: (x[1], x[0]), reverse=True)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_score_ordered(scores, idx):\t\n\treturn [x[1][idx] for x in sorted(scores.items())]",
"def sorted_scores(scores):\n\treturn sorted(scores, key=lambda sailor: (total_score(sailor), sailor[1][0]))",
"def sort_words(word_dic):\n word_list = []\n for key, value in word_dic.items():\n word = ... | [
"0.6941447",
"0.6244914",
"0.6080136",
"0.60571605",
"0.5880722",
"0.5799608",
"0.5676046",
"0.5656475",
"0.5655577",
"0.55775243",
"0.5570335",
"0.5536112",
"0.5528473",
"0.5525277",
"0.55218947",
"0.5503588",
"0.54912657",
"0.5486251",
"0.54857135",
"0.54733294",
"0.5434229... | 0.72268873 | 0 |
Write a dictionarystored run to a file. The input is actually a dictionary of dictinoary. The outer dictionary is a set of queryspecific results indexed by the query id. And the internal dictionary is a set of document scores indexed by the document id. Before writing data, it is resorted within each query. | def writeRunDict(runDict, fileName):
with open(fileName, 'wt') as runfile:
for qid in runDict:
scores = getSorteScoresFromScoreDict(runDict[qid])
for i, (did, score) in enumerate(scores):
runfile.write(genRunEntryStr(qid, did, i + 1, score, FAKE_RUN_ID) + '\n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_results_to_disk(self, result_path,results):\n with open(result_path+\"/results.txt\",\"w+\") as out:\n\n for query_num in results:\n for doc_num in results[query_num]:\n out.write(str(query_num)+\" 0 \"+doc_num+\" 1 42.38 mt\\n\")\n out.close... | [
"0.64149755",
"0.6323092",
"0.63185096",
"0.623392",
"0.6117135",
"0.60469115",
"0.60431457",
"0.6037959",
"0.6010059",
"0.5962368",
"0.594754",
"0.5893706",
"0.58609116",
"0.5851925",
"0.58096147",
"0.58084553",
"0.580037",
"0.56999785",
"0.5693484",
"0.5654092",
"0.56539285... | 0.7100081 | 0 |
Write a list of QRELs to a file. | def writeQrels(qrelList, fileName):
with open(fileName, 'w') as f:
for e in qrelList:
f.write(qrelEntry2Str(e))
f.write('\n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(lst):\n # TODO",
"def write_to_databse(fileName):\n f = open(fileName)\n queries = eval(open(fileName).read())\n for q in queries:\n site.write(q)\n print \"Quries are saved:)\"",
"def store_list( q_frame, final, path_out):\n if not os.path.exists( path_out ):\n os.mak... | [
"0.6659388",
"0.65668094",
"0.64353186",
"0.64146936",
"0.63622636",
"0.6266605",
"0.62355745",
"0.6232225",
"0.6223006",
"0.6174213",
"0.6137193",
"0.6118019",
"0.61159784",
"0.61075747",
"0.61021453",
"0.6097921",
"0.60946554",
"0.60937405",
"0.60883534",
"0.6078855",
"0.60... | 0.82695436 | 0 |
A simple function to generate one run entry. | def genRunEntryStr(queryId, docId, rank, score, runId):
return f'{queryId} Q0 {docId} {rank} {score} {runId}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_run(arn=None):\n pass",
"def exe():\n e = entry()\n if e:\n return load(e)",
"def DistEntry():\n flags.StartMain(main)",
"def console_entry():\n #main()",
"def pretty_runlist_entry(num, max_num, command, arguments):\n basename = os.path.splitext(os.path.basename(command))[0]\... | [
"0.61637896",
"0.60849404",
"0.59185123",
"0.5910822",
"0.58969253",
"0.5850631",
"0.58331966",
"0.58331966",
"0.58331966",
"0.5811054",
"0.5810959",
"0.5786089",
"0.5732439",
"0.5696852",
"0.56688386",
"0.5663584",
"0.56446594",
"0.56446594",
"0.56446594",
"0.56446594",
"0.5... | 0.60984117 | 1 |
Read QRELs in the form of a dictionary where keys are query IDs. | def readQrelsDict(fileName):
result = {}
for e in readQrels(fileName):
result.setdefault(e.queryId, {})[e.docId] = int(e.relGrade)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dict_list(query_id, qrels):\n rel_list = []\n for query_dict in qrels:\n if int(query_dict['query_num']) == query_id:\n rel_list.append(query_dict)\n\n return rel_list",
"def query(self, q):\n for key in self.metadb.query(q):\n yield key, self.datadb[key]",
"def... | [
"0.6835832",
"0.6125994",
"0.58958644",
"0.58423054",
"0.5777831",
"0.57507735",
"0.5653117",
"0.5650223",
"0.5610089",
"0.5573849",
"0.55595124",
"0.5454953",
"0.5450803",
"0.5422582",
"0.5393296",
"0.53765213",
"0.53625804",
"0.53055394",
"0.52981114",
"0.5297954",
"0.52837... | 0.73858917 | 0 |
Read a run file in the form of a dictionary where keys are query IDs. | def readRunDict(fileName):
result = {}
with FileWrapper(fileName) as f:
for ln, line in enumerate(tqdm(f, desc='loading run (by line)', leave=False)):
line = line.strip()
if not line:
continue
fld = line.split()
if len(fld) != 6:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_run_info_from_file(file):\n\n with open(file) as f:\n raw = f.read()\n\n lines = raw.split('\\n')\n runs = []\n for line in lines:\n if line == '':\n continue\n comma_splits = line.split(',')\n train_acc = float(comma_splits[0].strip())\n test_acc ... | [
"0.6355188",
"0.59160393",
"0.5847533",
"0.58188874",
"0.58029336",
"0.5780881",
"0.5759118",
"0.57484883",
"0.56860626",
"0.56709135",
"0.565064",
"0.5644731",
"0.5634812",
"0.55844283",
"0.55820817",
"0.55554515",
"0.5534645",
"0.5521943",
"0.5497889",
"0.54698205",
"0.5465... | 0.7821929 | 0 |
Evaluate run stored in a file using QRELs stored in a file. | def evalRun(rerankRun, qrelsDict, metricFunc, debug=False):
resArr = []
for qid, scoreDict in rerankRun.items():
relsSortedByScores = []
val = 0
if qid in qrelsDict:
queryQrelDict = qrelsDict[qid]
for did, score in getSorteScoresFromScoreDict(scoreDict):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(pred_file, ref_file):\n ref_dict, pred_dict, query_dict, id_dict = build_pred_ref_dict(ref_file, pred_file, ref_file)\n total, acc, scores = res_eval_with_type_acc(query_dict, pred_dict, ref_dict, id_dict, save=False)\n em = calculate_exact_match(pred_dict, ref_dict)\n print('Comp Acc: {:.... | [
"0.597215",
"0.5928241",
"0.59157866",
"0.5755006",
"0.57497287",
"0.5712579",
"0.5587595",
"0.55446315",
"0.5504432",
"0.54999787",
"0.54995215",
"0.5442724",
"0.5438701",
"0.5433622",
"0.5417852",
"0.53815764",
"0.53815424",
"0.53552645",
"0.53552645",
"0.52808577",
"0.5260... | 0.5167078 | 30 |
Carry out internal or external evaluation. | def getEvalResults(useExternalEval,
evalMetric,
rerankRun,
qrelFile,
runFile=None,
useQrelCache=False):
if useExternalEval:
m = None
if evalMetric == METRIC_MAP:
m = 'map'
elif evalMet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self):\n pass",
"def evaluate(self):\n pass",
"def evaluate(self) :\n pass",
"def eval(self):\n pass",
"def eval(self):\n pass",
"def eval(self):\n pass",
"def evaluate():\n click.echo(\"Not implemented yet. In the future, this command will be u... | [
"0.66934925",
"0.66934925",
"0.64331764",
"0.62767583",
"0.62767583",
"0.62767583",
"0.62206227",
"0.60950506",
"0.60773194",
"0.60610324",
"0.6049888",
"0.602005",
"0.6007762",
"0.5988678",
"0.59778374",
"0.59670454",
"0.59487677",
"0.593412",
"0.5869118",
"0.5850919",
"0.58... | 0.0 | -1 |
Sort itemsets by list of ordered indices. Returns ranked itemsets. | def sort_itemsets(indices, data, itemsets, custom_index_values=None,
return_values=False):
values = []
custom_index_id = 0
for (name, order) in indices:
if name == 'custom':
try:
values.append([order * v for v in custom_index_values[custom_index_id]])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reorder_sets(sets):\n\n if len(sets) == 1:\n return sets\n\n s = set([])\n\n for ss in sets:\n for i in ss:\n s.add(i)\n\n tree = P(sets)\n\n\n for i in s:\n tree.set_contiguous(i)\n tree = flatten(tree)\n\n return tree.ordering()",
"def sort_indices(s... | [
"0.59840685",
"0.5871088",
"0.58037066",
"0.5756595",
"0.56122243",
"0.5593702",
"0.55214834",
"0.5464931",
"0.5447365",
"0.5439025",
"0.54177094",
"0.5398739",
"0.5395633",
"0.5359991",
"0.52936673",
"0.5288645",
"0.5272867",
"0.5250649",
"0.51981884",
"0.5194961",
"0.516619... | 0.75688815 | 0 |
Creates the python script necessary to submit the MEM jobs to the batch system | def createScript_sbatch(self):
tools_createScript_sbatch(
sbatch_script_file_name = self.sbatchFile_addMEM,
executable = self.executable_addMEM,
command_line_parameters = self.cfgFiles_addMEM_modified,
input_file_names = self.inputFiles,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scriptGen(self,tmpd='/tmp/jose',libRev='last',submode='qsub',\n redirect=1,PBSoptions=''):\n jobname=self.name\n outdir=self.outd\n qsubdir=scratchdir+'/qsub/'+todayDate() #subdirectory to deposit the script\n if not os.path.exists(qsubdir): pastry('/bin/mkdir -p '... | [
"0.6900363",
"0.6724484",
"0.654995",
"0.64530903",
"0.6366503",
"0.63467556",
"0.6325721",
"0.63027",
"0.6214599",
"0.6161553",
"0.6147396",
"0.6122376",
"0.61184967",
"0.606868",
"0.60532546",
"0.6047774",
"0.6047245",
"0.5977596",
"0.59358174",
"0.59101474",
"0.59067476",
... | 0.7109882 | 0 |
Adds the commands to Makefile that are necessary for running the MEM code | def addToMakefile_addMEM(self, lines_makefile):
if self.is_sbatch:
lines_makefile.append("sbatch_addMEM:")
lines_makefile.append("\t%s %s" % ("python", self.sbatchFile_addMEM))
lines_makefile.append("")
for key_file, output_file in self.outputFiles.items():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __instructions(self):\n\n self += comment('Intel Parallel Studio XE')\n self += packages(ospackages=self.__ospackages)\n self += copy(src=self.__tarball,\n dest=posixpath.join(self.__wd, self.__tarball_name))\n if self.__license and not '@' in self.__license:\n ... | [
"0.5848315",
"0.5823324",
"0.57588905",
"0.56961876",
"0.5667787",
"0.55985594",
"0.55985594",
"0.55985594",
"0.55985594",
"0.5595643",
"0.557142",
"0.55572766",
"0.55293703",
"0.5514112",
"0.545637",
"0.53870964",
"0.536825",
"0.5344839",
"0.53177613",
"0.5304923",
"0.527652... | 0.64476264 | 0 |
Add hadd targets to the Makefile | def addToMakefile_hadd(self, lines_makefile):
for hadd_out, hadd_in in self.hadd_records.iteritems():
hadd_in_files = hadd_in['output_files']
hadd_fileset_id = hadd_in['fileset_id']
process_name = hadd_in['process_name']
sbatch_hadd_file = os.path.join(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hadd_ex(new_name, files):\n \n l = len(files)\n if l == 1:\n print 'only one file specified, copying %s to %s' % (files[0], new_name)\n assert os.system('cp -p %s %s' % (files[0], new_name)) == 0 # JMTBAD check return code\n print '1 file copied to %s' % new_name\n return T... | [
"0.5552721",
"0.5400419",
"0.53885555",
"0.5352961",
"0.53026015",
"0.521227",
"0.5189179",
"0.5171282",
"0.5132894",
"0.508693",
"0.5052858",
"0.50106657",
"0.49353966",
"0.49309725",
"0.49256998",
"0.48918346",
"0.48828074",
"0.4844602",
"0.48313728",
"0.4828164",
"0.481639... | 0.708559 | 0 |
Creates Makefile that runs the MEM | def createMakefile(self, lines_makefile):
targets = self.hadd_records.keys()
tools_createMakefile(self.makefile, targets, lines_makefile, self.filesToClean, self.is_sbatch)
logging.info("Run it with:\tmake -f %s -j %i " % (self.makefile, self.num_parallel_jobs)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_memleaks():\n build()\n sh(\"%s psutil\\\\tests\\\\test_memleaks.py\" % PYTHON)",
"def addToMakefile_addMEM(self, lines_makefile):\n if self.is_sbatch:\n lines_makefile.append(\"sbatch_addMEM:\")\n lines_makefile.append(\"\\t%s %s\" % (\"python\", self.sbatchFile_addME... | [
"0.61680174",
"0.5935793",
"0.5723549",
"0.5639227",
"0.5620911",
"0.560683",
"0.55755347",
"0.5453317",
"0.5422136",
"0.5360101",
"0.5299333",
"0.5261882",
"0.5261035",
"0.52525085",
"0.52483064",
"0.52471286",
"0.5225567",
"0.51970416",
"0.5196871",
"0.51353836",
"0.5132524... | 0.5192178 | 19 |
Creates all necessary config files and runs the MEM either locally or on the batch system | def create(self):
for key in self.dirs.keys():
if type(self.dirs[key]) == dict:
for dir_type in self.dirs[key].keys():
create_if_not_exists(self.dirs[key][dir_type])
else:
create_if_not_exists(self.dirs[key])
# read the file i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def work(self):\n self.config_file = self.args.config\n self.init_config()\n self.init_db()\n\n self.kickoff()",
"def init(_config, _run):\n sacred.commands.print_config(_run)\n dump_config_and_makefile()\n\n print()\n print('Initialized storage dir. Now run these commands... | [
"0.61811984",
"0.5962312",
"0.5945038",
"0.5930454",
"0.5915416",
"0.58961785",
"0.58747905",
"0.58591765",
"0.5837817",
"0.58329153",
"0.5831141",
"0.5814066",
"0.58040845",
"0.58010054",
"0.5763761",
"0.5734302",
"0.5733068",
"0.57085186",
"0.56866676",
"0.5675613",
"0.5667... | 0.64692366 | 0 |
Runs all Ntuple addMEM jobs either locally or on the batch system. | def run(self):
record_software_state(self.sw_ver_file_cfg, self.sw_ver_file_out, DEPENDENCIES)
run_cmd(
"make -f %s -j %i 2>%s 1>%s" % \
(self.makefile, self.num_parallel_jobs, self.stderr_file_path, self.stdout_file_path),
False
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uge(jobs, threads, tmp_dir):\n # NOTE: add later for LOCUS cluster \n pass",
"def run_batch(cpu, dtypes, itypes, nt, datafile, N):\n for ((dt, size), it) in itertools.product(dtypes, itypes):\n base = 10**(1/10)\n n = base \n m = 2000000\n while n*size < N:\n i... | [
"0.5688985",
"0.55833566",
"0.54783225",
"0.53617525",
"0.5304808",
"0.5304808",
"0.52620924",
"0.5175494",
"0.5158173",
"0.51086617",
"0.5106277",
"0.5074169",
"0.50725704",
"0.50569516",
"0.50466245",
"0.5034408",
"0.50054663",
"0.4967807",
"0.49644434",
"0.4949124",
"0.494... | 0.0 | -1 |
Get String representation of Experimental method used file of interest. Use header for this information. | def experimental_method(pdb_path):
parser = PDBParser(get_header=True)
parser.get_structure('', pdb_path)
return parser.get_header()['structure_method'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extension(self) -> str:",
"def methodHelp(self, req, method):\n p = self.get_method(method)\n return '\\n'.join((p.signature, '', p.description))",
"def get_info_string(self) -> str:\n return \"Not implemented\"",
"def __str__(self):\n\n strme = \"fed method {} {} {} {}\"\\\n ... | [
"0.62963766",
"0.62665623",
"0.6215433",
"0.62099373",
"0.5915208",
"0.5867127",
"0.58381164",
"0.5806747",
"0.5784804",
"0.57736933",
"0.5766435",
"0.5748317",
"0.5711032",
"0.5711032",
"0.5709923",
"0.56892794",
"0.5686285",
"0.5677971",
"0.56770235",
"0.56703764",
"0.56672... | 0.5833955 | 7 |
Find available devices to run EGL on. It will return the minor numbers, The minor number for the device is such that the Nvidia device node file for each GPU will have the form /dev/nvidia[minor number]. Avail able only on Linux platform. | def get_available_devices():
executable_path = os.path.join(os.path.dirname(__file__), 'build')
try:
num_devices = int(subprocess.check_output(
["{}/query_devices".format(executable_path)]))
except subprocess.CalledProcessError as e:
return [0]
FNULL = open(os.devnull, 'w')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_gpus():\n try:\n re = subprocess.check_output([\"nvidia-smi\", \"-L\"], universal_newlines=True)\n except OSError:\n return []\n return range(len([i for i in re.split('\\n') if 'GPU' in i]))",
"def androidDetectDevices():\n\tdevices = []\n\ttry:\n\t\tif (sys.platform == 'win32'):\n... | [
"0.6562714",
"0.65426177",
"0.64418447",
"0.6440465",
"0.64399284",
"0.64399284",
"0.64344305",
"0.6399175",
"0.6397971",
"0.6376035",
"0.6363725",
"0.63479066",
"0.6269277",
"0.6267697",
"0.62502813",
"0.62355644",
"0.61364883",
"0.6132447",
"0.6094917",
"0.6072684",
"0.6059... | 0.77780443 | 0 |
Get the device index to use in pytorch The minor number for the device is such that the Nvidia device node file for each GPU will have the form /dev/nvidia[minor number]. Avail able only on Linux platform. | def get_cuda_device(minor_idx):
executable_path = os.path.join(os.path.dirname(__file__), 'build')
try:
num_devices = int(subprocess.check_output(
["{}/query_devices".format(executable_path)]))
except subprocess.CalledProcessError as e:
return 0
for i in range(num_devices):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def device_index(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"device_index\")",
"def device_num(self) -> str:\n return pulumi.get(self, \"device_num\")",
"def _current_device_index(self) -> int:\n device = PArray._get_current_device()\n if device is None: # not called in... | [
"0.72570884",
"0.72109413",
"0.71451735",
"0.68671006",
"0.68392485",
"0.68228555",
"0.68183947",
"0.6812957",
"0.6802684",
"0.6722583",
"0.67002505",
"0.6601767",
"0.6532514",
"0.6519109",
"0.6505232",
"0.6494063",
"0.6482684",
"0.6395324",
"0.6358353",
"0.634921",
"0.632758... | 0.8265997 | 0 |
Check if image file extension match 'ext' | def is_suffix_right(file: Path, extension: str):
ext = extension.lower()
fext = file.suffix.lower()[1:]
jpgs = {"jpg", "jpeg"}
if fext in jpgs and ext in jpgs:
return True
elif fext == ext:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_image_extension(filename):\n\t\n\t# We need to make a test for extention :\n\timport os\n\textension = os.path.splitext(filename)[1]\n\text_ok=['.apm','.bmp','.gif','.ico','.jpeg','.jpg','.odi','.pcx','.png','.ppm','.psd','.tga','.tif','.tiff','.wmf','.xcf','.APM','.BMP','.GIF','.ICO','.JPEG','.JPG','.OD... | [
"0.8510836",
"0.82460964",
"0.8242744",
"0.790605",
"0.78503174",
"0.773012",
"0.75900716",
"0.75041",
"0.74898267",
"0.7443009",
"0.7431577",
"0.74293053",
"0.74175483",
"0.7417413",
"0.73864245",
"0.73125875",
"0.7271719",
"0.71800363",
"0.70967096",
"0.70940334",
"0.708400... | 0.7415507 | 14 |
Set the window's configuration. | def configure_ui(self):
self.setWindowIcon(self.MAIN_ICON)
self.setWindowModality(Qt.ApplicationModal) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_window(self, width, height):\n self.configure_surface(width, height)",
"def SetWindow(self, w):\r\n\r\n self.window = w",
"def _configureWindow(self):\n if self._win_type == WindowType.IMMERSIVE:\n pg.setConfigOptions(\n foreground='d',\n ... | [
"0.75859606",
"0.71165055",
"0.70187974",
"0.7005744",
"0.6882975",
"0.67949784",
"0.6744869",
"0.6694935",
"0.66007435",
"0.6594297",
"0.6535419",
"0.6406824",
"0.6400676",
"0.63883495",
"0.63820267",
"0.63756794",
"0.6368127",
"0.636234",
"0.6302152",
"0.628131",
"0.626874"... | 0.56157684 | 89 |
This function provides correcting wrong paths to files and directories. It's a slot which called when both of edit lines is changing. If one of the paths is incorrect, then the error will be removed. | def correct_wrong_path(self):
sender = self.sender()
# This block provides removing an error under the edit line of the map's filename
if sender == self.mapsDirectoryLine and self.incorrect_map_filename:
self.VLayout.removeWidget(self.error_maps_lbl)
self.error_maps_lbl.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_paths(self):\r\n\t\tself.check_line_edits_and_refresh_filestate()\r\n\t\t# paths\r\n\t\tsource_img_filename = self.source_img_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_dir_name = self.sink_dir_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_db_name_entry_text = self.sink_db_name_entry.text()\r\... | [
"0.6434743",
"0.5851415",
"0.5772642",
"0.57233256",
"0.57132787",
"0.5556546",
"0.5547998",
"0.55355173",
"0.5520772",
"0.5487168",
"0.5478769",
"0.5449815",
"0.54244477",
"0.54151076",
"0.5398201",
"0.5370582",
"0.53543586",
"0.53484815",
"0.5334684",
"0.5310021",
"0.529975... | 0.84562945 | 0 |
This module enables or disables the accept button for this dialog window depending on whether there is text in the edit lines. | def enable_accept_button(self):
if self.mapsDirectoryLine.text() and self.troopsDirectoryLine.text():
self.acceptButton.setEnabled(True)
else:
self.acceptButton.setEnabled(False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_okButton_clicked(self):\n self.accept=True",
"def on_buttonBox_accepted(self):\n if len(self.lineInput.text()) == 0:\n self.reject()\n else:\n self.input = self.lineInput.text() \n self.accept()",
"def acceptClicked(self):\n if len(... | [
"0.7059548",
"0.66216075",
"0.64704233",
"0.63764644",
"0.62911826",
"0.61641425",
"0.61585987",
"0.61297405",
"0.59587705",
"0.5951562",
"0.5920374",
"0.58046377",
"0.57752407",
"0.57732236",
"0.5757981",
"0.5731169",
"0.56851196",
"0.5679839",
"0.566154",
"0.5607326",
"0.56... | 0.72327644 | 0 |
It's a slot which is called when the setting button is pressed This method opens the file manager and if a directory or a file is chosen, it fills edit lines | def files_manage(self):
sender = self.sender()
if sender == self.mapsDirectoryButton:
path_to_map, _ = QFileDialog.getOpenFileName(self,
caption="Открыть",
directory="/",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_File1_toolButton_clicked(self):\n my_file = QtWidgets.QFileDialog.getOpenFileName(self, u'打开文件', '/')\n if my_file[0]:\n self.File1_lineEdit.setText(my_file[0])\n else:\n QtWidgets.QMessageBox.warning(self, u'警告', u'请选择输入文件')",
"def onLoad (self):\n #productiv... | [
"0.7056106",
"0.69966835",
"0.68940663",
"0.68738866",
"0.6866713",
"0.6858465",
"0.6858465",
"0.685267",
"0.6840944",
"0.6735564",
"0.67051214",
"0.6688198",
"0.668139",
"0.66708505",
"0.6670207",
"0.66078514",
"0.6606934",
"0.6594669",
"0.657698",
"0.65762657",
"0.65483475"... | 0.7185551 | 0 |
It's slot which is called when the accept button is pressed, if it's enabled This method checks that a specified directory or a file is correct and calls the method of creating central widget in the parent widget. Else this method prints an error about that trouble under the edit line and sets particular flags, specify... | def accept(self):
map_filename = self.mapsDirectoryLine.text()
troops_directory = self.troopsDirectoryLine.text()
# This index will be a measure to insert error label under an edit line if an error will be occurred
index_map_label = self.VLayout.indexOf(self.map_label)
if not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accept(self, *args, **kwargs):\n QtGui.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor))\n \n mneRoot = self.ui.lineEditMneRoot.text()\n if str(mneRoot) is '':\n messageBox = QtGui.QMessageBox()\n messageBox.setText(\"Environment variables MNE_ROOT \... | [
"0.6549283",
"0.65129936",
"0.6496964",
"0.6342729",
"0.63302416",
"0.62414056",
"0.62278897",
"0.62035453",
"0.617841",
"0.61445165",
"0.6141491",
"0.6070426",
"0.6044493",
"0.596154",
"0.5927395",
"0.5913186",
"0.5898482",
"0.58805674",
"0.58754504",
"0.5850688",
"0.5835647... | 0.7866559 | 0 |
Create Order Consultation Standard Medical | def btn_create_order_con(self):
print()
print('btn_create_order_con')
# Init
# Search Partner
partner = tre_funcs.get_partner(self, self.patient.name)
# Search pricelist
pricelist = tre_funcs.get_pricelist(self)
# Search product
name = 'CONSULTA MEDICA'
price_list = '2019'
product = tre_func... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pl_create_order(self):\n\tprint()\n\tprint('Pl - Create Order')\n\n\n\tpartner = self.env['res.partner'].search([\n\t\t\t\t\t\t\t\t\t\t\t\t\t('name', '=', self.patient.name),\n\t\t\t\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t\t\t\t#order='appointment_date desc',\n\t\t\t\t\t\t\t\t\t\t\t\tlimit=1,)\n\n\n\t# Create ... | [
"0.5997403",
"0.5783128",
"0.5642396",
"0.5504628",
"0.541906",
"0.5365111",
"0.5362394",
"0.53545696",
"0.5257514",
"0.52405876",
"0.5236128",
"0.51716906",
"0.51556104",
"0.51476526",
"0.51275605",
"0.50959665",
"0.5093361",
"0.50858736",
"0.5084346",
"0.5078212",
"0.506985... | 0.5104012 | 15 |
Create Order Procedure 2019 From Recommendations | def btn_create_order_pro(self):
print()
print('treatment - btn_create_order_pro')
# Search Partner
partner = tre_funcs.get_partner(self, self.patient.name)
# Search pricelist
pricelist = tre_funcs.get_pricelist(self)
# Search product
# Create Product tuple
product_tup = []
#for service in self.se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_order(self):\n\tprint()\n\tprint('OH - pl_create_order')\n\n\t# Search Partner\n\tprint()\n\tprint('Search partner')\n\tpartner = self.env['res.partner'].search([\n\t\t\t\t\t\t\t\t\t\t\t\t\t('name', '=', self.patient.name),\n\t\t\t\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t\t\t\t#order='appointment_date de... | [
"0.6269989",
"0.62364376",
"0.6203065",
"0.6008139",
"0.6007918",
"0.58362144",
"0.56619346",
"0.56101316",
"0.5552725",
"0.551811",
"0.549911",
"0.5442458",
"0.54327625",
"0.5430995",
"0.5324714",
"0.5315934",
"0.52854973",
"0.5252717",
"0.5247154",
"0.5242529",
"0.5203079",... | 0.59989053 | 5 |
Create Service Opens a new form. For Reco choice. | def btn_create_reco(self):
print()
print('OH - btn_create_reco')
# Init
res_id = self.id
res_model = _model_treatment
view_id = self.env.ref('openhealth.treatment_2_form_view').id
# Open
return {
# Mandatory
'type': _model_action,
'name': 'Open Treatment Current',
# Window action
'prior... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_service(self):\n self.dlg = ServiceCreateDialog(iface=self.iface, backend=self.backend)\n self.dlg.setWindowFlags(Qt.WindowStaysOnTopHint)\n self.dlg.show()",
"def newService(self):\n for item in self.__service_list.selectedItems():\n item.setSelected(False)\n\n ... | [
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"0.63697183",
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"0.6088428",
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"0.6050262",
"0.60397744",
"0.6024221",
"0.59724677",
"0.59567046",
"0.59367496",
"0.5895749",
"0.5887126",
"0.58600026",
"0.5829561",
"0.58239305",
"0.5769992",
"0.576962... | 0.57344526 | 25 |
Generic method for creating Services. Compact. And easy to maintain. | def create_service_for_me(self, treatment_id, family, subfamily, physician_id):
print()
print('Create Service Generic - ', subfamily)
# init
model_dic = {
'all': _model_service,
#'co2': _model_ser_co2,
#'excilite': 'openhealth.service_excilite',
#'ipl': 'openhealth.service_ipl',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createService(data):\n return Service(data).create()",
"def create_service(self, url_data):\n data = {key: value[0] for key, value in url_data}\n\n publish_key = uuid.uuid4().hex\n service_id = uuid.uuid4().hex\n service_name = data['name']\n\n self.fastly_cache[service_... | [
"0.77442056",
"0.7001777",
"0.6965583",
"0.69324523",
"0.68919903",
"0.6704786",
"0.6639285",
"0.6636635",
"0.66325355",
"0.6612868",
"0.6570706",
"0.6569513",
"0.6558918",
"0.6532779",
"0.65322745",
"0.65269345",
"0.64953035",
"0.64921445",
"0.64893615",
"0.6462472",
"0.6455... | 0.62306726 | 41 |
drops all the tables defined in "drop_table_queries" query list | def drop_tables(cur, conn):
for query in drop_table_queries:
cur.execute(query)
conn.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop_tables(session):\n for query in drop_table_queries:\n session.execute(query)",
"def drop_tables(session):\n\n for query in drop_table_queries:\n session.execute(query)",
"def drop_tables (cur, conn):\n for query in drop_table_queries:\n cur.execute(query)\n conn.co... | [
"0.87738657",
"0.8704623",
"0.85902226",
"0.8486621",
"0.84703714",
"0.846386",
"0.8450791",
"0.8441526",
"0.8429193",
"0.8363235",
"0.81853676",
"0.8104734",
"0.8016257",
"0.800211",
"0.7919646",
"0.79034376",
"0.78497",
"0.7820362",
"0.77738655",
"0.7768701",
"0.77539575",
... | 0.8541708 | 10 |
creates all the tables defined in "create_table_queries" query list | def create_tables(cur, conn):
for query in create_table_queries:
cur.execute(query)
conn.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_tables(session):\n for query in create_table_queries:\n session.execute(query)",
"def create_tables(session):\n\n for query in create_table_queries:\n session.execute(query)",
"def create_tables(self):\n for query in table_create_sql:\n self.cursor.execute(query... | [
"0.86132455",
"0.8574697",
"0.8532623",
"0.8309845",
"0.8308152",
"0.83072835",
"0.8294059",
"0.8262667",
"0.82527596",
"0.8157106",
"0.81245124",
"0.80968326",
"0.7978261",
"0.7951848",
"0.79269755",
"0.79269755",
"0.7827138",
"0.775508",
"0.76359016",
"0.76054025",
"0.75461... | 0.8336363 | 11 |
connects to database specified in the config file, drops and creates staging, fact and dimension tables | def main():
config = configparser.ConfigParser()
config.read('dwh.cfg')
conn = psycopg2.connect("host={} dbname={} user={} password={} port={}".format(*config['CLUSTER'].values()))
cur = conn.cursor()
drop_tables(cur, conn)
create_tables(cur, conn)
conn.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n\n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n load_staging_tables(cur, conn)\n insert_tables(cur, conn)\n\n conn.close(... | [
"0.7464287",
"0.7464287",
"0.7455873",
"0.74330133",
"0.7373333",
"0.7258284",
"0.7227603",
"0.71913075",
"0.70940065",
"0.70294785",
"0.6913103",
"0.68359804",
"0.6630307",
"0.6609196",
"0.6601219",
"0.6571767",
"0.6568274",
"0.6510074",
"0.650525",
"0.64658636",
"0.64558053... | 0.71758443 | 10 |
Generate an array of all days with prices for every ticker. Some symbols may not have trading data on some days. This filters out those days, and creates and array of all prices and tickers. | def _getFilteredPrices(self):
# Create dict of dates to tickers to prices.
date_dict = {}
for stock in self.stock_dict.values():
for date in stock.ordered_date_dict.keys():
if date not in date_dict:
date_dict[date] = {}
date_dict[da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _construct_all_prices(self):\n d = dict([(s+'-', 0.0) for s in self.symbol_list] +\n [(s+'+', 0.0) for s in self.symbol_list])\n d['datetime'] = self.backtest_date\n return [d]",
"def gather_stock_data(tickers, save=True):\n prices = pd.DataFrame()\n ts = TimeSeries... | [
"0.67409295",
"0.67202425",
"0.6456439",
"0.64514035",
"0.62389207",
"0.62224364",
"0.60535693",
"0.601309",
"0.60105884",
"0.59593356",
"0.59551656",
"0.5949486",
"0.593729",
"0.5920068",
"0.5865663",
"0.5824194",
"0.57976747",
"0.57489455",
"0.5708646",
"0.5707145",
"0.5706... | 0.7080781 | 0 |
Generate the array of price changes. | def _getPriceChangeArray(self):
prices = self.price_array[1:]
prev_prices = self.price_array[:-1]
raw_price_changes = prices / prev_prices
expense_array = np.array(
[self.stock_dict[ticker].expense_ratio for ticker in self.tickers], dtype=np.float64)
expense_array = n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def price_history(self) -> np.ndarray:\n nb_transactions = len(self.game.transactions)\n nb_goods = self.game.configuration.nb_goods\n result = np.zeros((nb_transactions + 1, nb_goods), dtype=np.float32)\n\n temp_game = Game(self.game.configuration, self.game.initialization)\n\n ... | [
"0.66110533",
"0.66072136",
"0.6545294",
"0.61039895",
"0.5988195",
"0.5960361",
"0.5951982",
"0.5944508",
"0.5913564",
"0.58012235",
"0.58002996",
"0.57225096",
"0.5713424",
"0.57093674",
"0.5703792",
"0.56665474",
"0.5522286",
"0.55150926",
"0.54229176",
"0.54132736",
"0.54... | 0.77094185 | 0 |
Loads a json file if it nonempty. | def _json_from_file(file: IO[AnyStr]) -> Json:
if os.path.getsize(file.name) > 0:
return typing.cast(Json, json.load(file))
return {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load(self):\n if self.file_path.exists():\n with open(self.file_path) as fid:\n self.data = json.load(fid)",
"def load_json_file(self, file, default_content=None):\n if os.path.isfile(file) and os.path.getsize(file):\n with open(file, \"r\", encoding=\"utf-... | [
"0.762835",
"0.7599969",
"0.75057316",
"0.7405254",
"0.72807854",
"0.72572",
"0.71229345",
"0.70612323",
"0.7041013",
"0.69666064",
"0.6953141",
"0.693393",
"0.692284",
"0.68644524",
"0.68295234",
"0.6816347",
"0.6786634",
"0.6776473",
"0.67584807",
"0.6753719",
"0.6745626",
... | 0.6703026 | 23 |
Merges missing default settings into user settings. | def _merge_settings(default_settings: JsonValue, user_settings: JsonValue, use_default_values: bool) -> JsonValue:
if isinstance(default_settings, dict):
user_settings = typing.cast(Json, user_settings)
for key, default_value in default_settings.items():
if key not in user_settings:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadDefaults(self):\n # (025) Merged into settings.RawSettings.\n pass",
"def _inject_defaults(settings, defaults):\n new_settings = {}\n\n if defaults is None:\n return settings\n elif settings is None or len(settings) == 0:\n new_settings = defaults\n else:... | [
"0.72066826",
"0.70328254",
"0.699051",
"0.69179225",
"0.6817494",
"0.67214745",
"0.6703522",
"0.6643126",
"0.6551807",
"0.651397",
"0.6509964",
"0.64392966",
"0.6410869",
"0.6405035",
"0.63833135",
"0.631745",
"0.6305927",
"0.62964314",
"0.6293989",
"0.6207525",
"0.61430144"... | 0.7007204 | 2 |
Parses a json settings file and merges in missing defaults. | def load_settings(user_settings_file: IO[AnyStr], use_default_values: bool = True) -> Json:
default_settings = load_default_settings()
user_settings = load_settings_simple(user_settings_file)
return typing.cast(Json, _merge_settings(default_settings, user_settings, use_default_values)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_json_settings(file: str):\n with open(file) as f:\n return json.load(f)",
"def json_config_settings_source(settings: BaseSettings) -> Dict[str, Any]:\n full_path = Path(config_dir) / config_name\n logger.debug(f\"Parsing file: {full_path}\")\n if fileio.file_exists(str(fu... | [
"0.71374434",
"0.7061503",
"0.69584143",
"0.69001013",
"0.6724808",
"0.65126544",
"0.6474971",
"0.64652",
"0.64467615",
"0.64318335",
"0.6358839",
"0.6333143",
"0.6325857",
"0.6324122",
"0.63009304",
"0.6262618",
"0.6207379",
"0.6193889",
"0.6193376",
"0.6137778",
"0.60944533... | 0.72829187 | 0 |
Raises a value error if duplicates have been counted. | def _raise_if_duplicates(counts: Dict[str, int]) -> None:
duplicates: List[str] = []
for nickname, count in counts.items():
if count > 1:
duplicates.append(nickname)
if len(duplicates) > 0:
# TODO This is not always nickname
raise ValueError(f'\'nickname\' not unique {dup... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_value_error(self):\n with self.assertRaises(ValueError):\n Band.count(\n column=Band.name, distinct=[Band.name, Band.popularity]\n ).run_sync()",
"def validate_availability(self, value):\n cnt = Counter()\n if value:\n for i in value:\... | [
"0.70410717",
"0.6357471",
"0.605737",
"0.60037196",
"0.6002928",
"0.5907917",
"0.5853888",
"0.58534443",
"0.58284265",
"0.58045846",
"0.57278585",
"0.5720412",
"0.5718681",
"0.5674157",
"0.56520355",
"0.56154764",
"0.5604806",
"0.55942357",
"0.55827755",
"0.5576378",
"0.5554... | 0.6930402 | 1 |
Checks the integrity of the list of engine settings. | def check_engine_settings(engine_settings_list: List[Json]) -> None:
if not engine_settings_list:
raise ValueError('Engine list was empty')
nickname_counts: Dict[str, int] = {}
for i, engine_settings in enumerate(engine_settings_list):
for key in ['nickname', 'path']:
if key not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_settings(self):\n pass",
"def check_settings(self):\r\n pass",
"def check_all_settings(self):\r\n self.emit(SIGNAL('check_settings()'))",
"def checkSettings(self):\n client.checkSettings(self)\n # TODO: Check your settings. Example:\n #\n # if self... | [
"0.671201",
"0.6706726",
"0.6444116",
"0.6335558",
"0.6182346",
"0.6006436",
"0.5979961",
"0.5972534",
"0.5947237",
"0.59175533",
"0.59118605",
"0.59100354",
"0.58788013",
"0.5878257",
"0.5867958",
"0.5811573",
"0.5804538",
"0.57874286",
"0.56914705",
"0.5676122",
"0.56677115... | 0.62922466 | 4 |
Parses an engine options file into a dictionary. | def load_engine_options_simple(engine_options_file: IO[AnyStr]) -> engine.ConfigMapping:
options: engine.ConfigMapping = {}
name_counts: Dict[str, int] = {}
for line_in_file in engine_options_file.read().splitlines():
line = typing.cast(str, line_in_file.strip())
if not line or line.startswi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_options(self):\n options = dict()\n while True:\n line = self.rfile.readline().decode(\"utf8\").strip()\n if not line:\n break\n self.log.debug(\"Got line: %s\", line)\n if \":\" not in line:\n self.log.debug(\"Invalid ... | [
"0.7345696",
"0.6754468",
"0.65925574",
"0.6516578",
"0.63679355",
"0.6275019",
"0.6238644",
"0.62379575",
"0.62147844",
"0.6081465",
"0.6051997",
"0.6026707",
"0.601588",
"0.60051244",
"0.6001563",
"0.5991739",
"0.5986586",
"0.5982635",
"0.5954915",
"0.58947027",
"0.586558",... | 0.74689823 | 0 |
Parses an engine options file and merges in missing defaults. | def load_engine_options(
default_options: List[engine.Option],
options_file: IO[AnyStr],
exclude_default_values: bool = True) -> engine.ConfigMapping:
def is_empty(value: engine.ConfigValue):
return value is None or value == '<empty>'
engine_options: engine.ConfigMapping = {}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_engine_options_simple(engine_options_file: IO[AnyStr]) -> engine.ConfigMapping:\n options: engine.ConfigMapping = {}\n name_counts: Dict[str, int] = {}\n for line_in_file in engine_options_file.read().splitlines():\n line = typing.cast(str, line_in_file.strip())\n if not line or lin... | [
"0.68988574",
"0.6230676",
"0.6218802",
"0.6173075",
"0.6170455",
"0.6135077",
"0.61264294",
"0.60763556",
"0.59360474",
"0.5916492",
"0.5889376",
"0.5873683",
"0.5790578",
"0.573215",
"0.5720919",
"0.5708603",
"0.57029176",
"0.56948304",
"0.5682351",
"0.5680957",
"0.566325",... | 0.7057122 | 0 |
Checks the integrity of the engine options. | def check_engine_options(default_options: List[engine.Option], engine_options: engine.ConfigMapping) -> None:
def check_check(value: engine.ConfigValue):
if not isinstance(value, bool):
raise ValueError(f'Value \'{value}\' for \'{name}\' not a boolean')
def check_spin(option: engine.Option... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_options(options, parser):\n if not options.get('release_environment', None):\n print(\"release environment is required\")\n parser.print_help()\n return os.EX_USAGE\n\n return 0",
"def _verify_options(config: configuration.Config) -> None:\n\n if not config.config['species... | [
"0.66546845",
"0.63232875",
"0.63195765",
"0.62439066",
"0.619586",
"0.6171925",
"0.60823643",
"0.6081956",
"0.6080628",
"0.6076199",
"0.6055865",
"0.59797627",
"0.5952743",
"0.58518857",
"0.5831465",
"0.58297694",
"0.58243865",
"0.5821161",
"0.58197397",
"0.58147943",
"0.580... | 0.597804 | 12 |
Creates a string representation of an engine option to write to a file. | def _engine_option_string_and_comment(option: engine.Option, value: engine.ConfigValue) -> Tuple[str, str]:
if value is None:
value = ''
name_equals_val = f'{option.name}={value}'
if option.type == 'check' or option.type == 'string' or option.type == 'button':
return (name_equals_val, f'type... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_filename_from_options(opt):\n fs = '{}_emb_{}_hid_{}_de_{}_dd_{}_n_lyrs_{}_lr_{}'.format(\n opt.rnn_cell,\n opt.embedding_size, opt.hidden_size,\n opt.dropout_p_encoder, opt.dropout_p_decoder,\n opt.n_layers, opt.lr)\n\n if opt.optim is not None:\n fs += '_{}'.... | [
"0.6393773",
"0.6163098",
"0.59513646",
"0.5796339",
"0.570742",
"0.565985",
"0.5508458",
"0.5497294",
"0.54812104",
"0.5416437",
"0.5410234",
"0.53872186",
"0.53827995",
"0.5382446",
"0.5371444",
"0.5350297",
"0.53447205",
"0.5316445",
"0.531454",
"0.53034174",
"0.5295298",
... | 0.54163146 | 10 |
Create string representations of engine options to write to a file. | def engine_options_file_lines(default_options: List[engine.Option], user_options: engine.ConfigMapping) -> List[str]:
option_infos: List[Tuple[str, str]] = []
for option in default_options:
if option.is_managed() or option.type == 'button':
continue
value = user_options[option.name] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_filename_from_options(opt):\n fs = '{}_emb_{}_hid_{}_de_{}_dd_{}_n_lyrs_{}_lr_{}'.format(\n opt.rnn_cell,\n opt.embedding_size, opt.hidden_size,\n opt.dropout_p_encoder, opt.dropout_p_decoder,\n opt.n_layers, opt.lr)\n\n if opt.optim is not None:\n fs += '_{}'.... | [
"0.64722455",
"0.6019288",
"0.59901017",
"0.59489715",
"0.5912976",
"0.58811975",
"0.5802393",
"0.5760834",
"0.5759363",
"0.57260406",
"0.57209575",
"0.572049",
"0.56972736",
"0.55530196",
"0.5531413",
"0.5490391",
"0.5488484",
"0.54847646",
"0.5483813",
"0.54755",
"0.5463221... | 0.58010626 | 7 |
calculates the shape of a matrix | def matrix_shape(matrix):
shape = []
while isinstance(matrix, list):
shape.append(len(matrix))
matrix = matrix[0]
return shape | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matShape(mat):\n return (len(mat),len(mat[0]))",
"def matrix_shape(matrix):\n return [*get_length(matrix)]",
"def matrix_shape(matrix):\n return [*get_length(matrix)]",
"def matrix_shape(matrix):\n if not matrix:\n return None\n if len(matrix) is 0:\n return [0]\n if type(... | [
"0.8048291",
"0.7918595",
"0.7918595",
"0.77554053",
"0.7717326",
"0.75373983",
"0.74661434",
"0.7236693",
"0.71243274",
"0.6954595",
"0.6807329",
"0.6807329",
"0.6805609",
"0.66554475",
"0.65739745",
"0.64975715",
"0.6492027",
"0.64321846",
"0.6358989",
"0.634034",
"0.633726... | 0.78285253 | 3 |
Softmax crossentropy loss with masking. | def masked_softmax_cross_entropy(logits, labels, mask):
loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
loss *= mask
return tf.reduce_mean(loss) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def masked_softmax_cross_entropy(preds, labels, mask):\n loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels)\n mask = tf.cast(mask, dtype=tf.float32)\n mask /= tf.reduce_mean(mask)\n loss *= mask\n return tf.reduce_mean(loss)",
"def masked_softmax_cross_entropy(preds, labels, mask):\... | [
"0.8051919",
"0.7999608",
"0.7981055",
"0.7981055",
"0.7981055",
"0.7977726",
"0.793266",
"0.7565911",
"0.73568034",
"0.73176104",
"0.72984004",
"0.72922695",
"0.716306",
"0.71087337",
"0.70437163",
"0.70108",
"0.6905214",
"0.68819344",
"0.6834542",
"0.6830443",
"0.6830443",
... | 0.77933097 | 7 |
Softmax crossentropy loss with masking. | def masked_sigmoid_cross_entropy(logits, labels, mask):
labels = tf.cast(labels, dtype=tf.float32)
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)
loss=tf.reduce_mean(loss,axis=1)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def masked_softmax_cross_entropy(preds, labels, mask):\n loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels)\n mask = tf.cast(mask, dtype=tf.float32)\n mask /= tf.reduce_mean(mask)\n loss *= mask\n return tf.reduce_mean(loss)",
"def masked_softmax_cross_entropy(preds, labels, mask):\... | [
"0.8051919",
"0.7999608",
"0.7981055",
"0.7981055",
"0.7981055",
"0.7977726",
"0.793266",
"0.77933097",
"0.7565911",
"0.73568034",
"0.73176104",
"0.72984004",
"0.72922695",
"0.71087337",
"0.70437163",
"0.70108",
"0.6905214",
"0.68819344",
"0.6834542",
"0.6830443",
"0.6830443"... | 0.716306 | 13 |
Save timezoneaware values for created and updated fields. | def save(self, *args, **kwargs):
if self.pk is None:
self.created = timezone.now()
self.updated = timezone.now()
super(Base, self).save(*args, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, *args, **kwargs):\n if self.pk is None:\n self.created = timezone.now()\n self.updated = timezone.now()\n super(Base, self).save(*args, **kwargs)",
"def save(self, *args, **kwargs):\n if not self.id:\n self.create_date = timezone.now()\n sel... | [
"0.6898697",
"0.63491416",
"0.6327028",
"0.6312949",
"0.6275868",
"0.61563116",
"0.61563116",
"0.6146063",
"0.6146063",
"0.61329794",
"0.61329794",
"0.6125347",
"0.61214924",
"0.61214924",
"0.6013914",
"0.6012528",
"0.59893817",
"0.5989289",
"0.5966557",
"0.59067565",
"0.5903... | 0.69218004 | 0 |
Adds indent, quotes specified elements, joins them with ',' and surrounds with parentheses. | def commajoin(array, elements_to_quote, indent=0):
result = " " * indent + "("
for i in elements_to_quote:
if array[i] == '':
array[i] = "NULL"
else:
array[i] = "'" + str(array[i]) + "'"
j = 0
for i in array:
result += str(i)
if j != len(array) - 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _format_item_list(items, pad=\"'\", sep=', ', end_sep=' and '):\n result = ''\n items = [pad + item + pad for item in items]\n if items:\n if len(items) != 1:\n result = sep.join(items[:-1]) + end_sep + items[-1]\n else:\n result = items[0]\n return result",
"d... | [
"0.6104393",
"0.6081047",
"0.5986766",
"0.59865344",
"0.5851034",
"0.57388306",
"0.5737991",
"0.5690729",
"0.56882364",
"0.5627679",
"0.56094444",
"0.5571577",
"0.55558723",
"0.5547043",
"0.55461544",
"0.5525962",
"0.54631674",
"0.5434451",
"0.54077697",
"0.54077697",
"0.5362... | 0.68266034 | 0 |
Gets what column matches which language. | def getorder(columns, langs):
order = []
for lang in langs:
i = 0
for col in columns:
if col == lang:
order.append(i)
i += 1
if len(order) != len(langs):
print("Either missing a language or have a duplicate\n")
return order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_column(self, column_or_label):\n c = column_or_label\n if isinstance(c, collections.Hashable) and c in self.column_labels:\n return self[c]\n else:\n assert len(c) == self.num_rows, 'column length mismatch'\n return c",
"def _get_col(self, idx):\n ... | [
"0.62636393",
"0.61789846",
"0.6152711",
"0.60966796",
"0.6089271",
"0.6004885",
"0.59756434",
"0.5966933",
"0.59179723",
"0.59012645",
"0.58697855",
"0.5680194",
"0.5678792",
"0.5676237",
"0.56437963",
"0.5629243",
"0.56290823",
"0.56186384",
"0.5599697",
"0.559922",
"0.5598... | 0.0 | -1 |
Writes name inserts to given file. | def writenames(sql, names, indent=0, id_offset=0, lang_offset=0):
i = 0
for row in names:
j = 0
if i > 0:
sql.write(",\n")
for name in row:
if j > 0:
sql.write(",\n")
sql.write(commajoin([i+id_offset, j+lang_offset, name], [2], indent))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_to_file(file, name):\n with open(file, \"a\") as player_list:\n player_list.writelines(name)",
"def write_to_file(self, filename: str) -> None:",
"def write(self, filename):\n pass",
"def write(self, filename):\n pass",
"def write(self, fname):\n pass",
"def write... | [
"0.70986146",
"0.6759375",
"0.6651877",
"0.6651877",
"0.6646314",
"0.64887553",
"0.6326986",
"0.6294738",
"0.6240479",
"0.6192061",
"0.6166432",
"0.6140194",
"0.6105632",
"0.60816085",
"0.60777086",
"0.6034101",
"0.6021925",
"0.6008614",
"0.6003748",
"0.5986355",
"0.59844065"... | 0.5827592 | 29 |
Write and read lang data | def write_lang_city(sql):
langs = []
nametemp = []
langorder = []
countries = []
sql.write("INSERT INTO languages(language_id, name, iso2, iso3) VALUES\n")
with open("data/lang.csv", 'r', encoding='utf8') as csvfile:
reader = csv.reader(csvfile, delimiter=",", quoting=csv.QUOTE_MINIMAL)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setData(data, language=None):",
"def loadLanguage(request, lang):\n request.clock.start('loadLanguage')\n from MoinMoin import caching\n # farm notice: for persistent servers, only the first wiki requesting some language\n # gets its cache updated - a bit strange and redundant, but no problem.\n ... | [
"0.6238392",
"0.6186492",
"0.59919024",
"0.586811",
"0.58512664",
"0.58224833",
"0.5787843",
"0.574927",
"0.57183444",
"0.56823564",
"0.56302905",
"0.5599651",
"0.55802834",
"0.55779564",
"0.5515979",
"0.54899395",
"0.54727936",
"0.5460523",
"0.5426006",
"0.5410131",
"0.53640... | 0.49894106 | 57 |
Write and read food group data | def write_groups(sql, langs):
groups = []
nametemp = []
langorder = []
sql.write("INSERT INTO food_group(food_group_id) VALUES\n")
with open("data/groups.csv", 'r', encoding='utf8') as csvfile:
reader = csv.reader(csvfile, delimiter=",", quoting=csv.QUOTE_MINIMAL)
i = 0
for r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_data():",
"def store_grouped_data(data,path):\n i = 0\n for name, group in data:\n l = len(group)\n print name, \", \", l\n if l > 999:\n group.to_csv(path + \"//clean.events\"+ str(i), index=False)\n i += 1",
"def add_group_data(self, group_name):\n ... | [
"0.61627614",
"0.5857623",
"0.57573915",
"0.56267273",
"0.5603694",
"0.5557257",
"0.55482465",
"0.55239236",
"0.54822713",
"0.5476876",
"0.54660803",
"0.5411314",
"0.5379378",
"0.5371047",
"0.53069615",
"0.5306003",
"0.53000164",
"0.526094",
"0.52049595",
"0.51842505",
"0.516... | 0.5511003 | 8 |
Write and read diet data | def write_groups_diets(sql, langs):
groups = []
diets = []
nametemp = []
langorder = []
groups = write_groups(sql, langs)
sql.write("INSERT INTO global_diet(preset) VALUES\n")
with open("data/diets.csv", 'r', encoding='utf8') as csvfile:
reader = csv.reader(csvfile, delimiter=",... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_data():",
"def write(data):",
"def write( data ):",
"def write_infodata(self, data):\n if not self._wrt_defined:\n print \"Please, call set_write_cycle_time() first\"\n return False\n if len(data) != 4:\n print \"Infodata block is 4 byte\"\n ... | [
"0.62000924",
"0.56546474",
"0.56115943",
"0.55661196",
"0.5428569",
"0.53478754",
"0.5235032",
"0.5196143",
"0.5165036",
"0.5139188",
"0.51169264",
"0.5080054",
"0.50512236",
"0.5031987",
"0.5027093",
"0.5025451",
"0.49980384",
"0.49825385",
"0.49762046",
"0.4923382",
"0.490... | 0.0 | -1 |
simply converts all csv files in mock data into insert statements | def write_test_data(sql):
for fname in sorted(glob.glob("mock_data/*.csv")):
print(fname)
with open(fname, 'r', encoding='utf8') as csvfile:
reader = csv.reader(csvfile, delimiter=",", quoting=csv.QUOTE_MINIMAL)
i = 0
for row in reader:
if i == 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_data(data_type, data, db_cursor, database):\n for each_file in data:\n with open(f'{DATA_PATH}{each_file}.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n # do not process the heade... | [
"0.6758338",
"0.6746043",
"0.67334425",
"0.6713657",
"0.66608614",
"0.6618824",
"0.65960056",
"0.65747595",
"0.6549408",
"0.65186286",
"0.65053517",
"0.6415094",
"0.6390163",
"0.6388048",
"0.63780886",
"0.6363483",
"0.6359099",
"0.6326339",
"0.6316207",
"0.6316161",
"0.63077"... | 0.77306116 | 0 |
Converts csv/tsv files from data folders to populate queries. | def main():
langs = []
with open("sql/07_populate.sql", 'w', encoding='utf8') as sql:
sql.write("--this file is generated from csv files in data folder\n\n")
langs = write_lang_city(sql)
write_groups_diets(sql, langs)
with open("sql/10_populate_test_data.sql", 'w', encoding='utf8'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_files(file_path):\n # checking your current working directory\n cur_dir = os.getcwd()\n\n # Get your current folder and sub folder event data\n data_dir = os.path.join(cur_dir, 'event_data')\n\n # Create a for loop to create a list of files and collect each\n # file_path\n file_... | [
"0.666231",
"0.6443677",
"0.62016416",
"0.6157668",
"0.6114367",
"0.61079246",
"0.6103801",
"0.604604",
"0.6014093",
"0.596293",
"0.595828",
"0.5929185",
"0.5904661",
"0.58836704",
"0.5875933",
"0.5866737",
"0.5864637",
"0.58475685",
"0.58439934",
"0.5835151",
"0.58215326",
... | 0.5534379 | 71 |
PATCH /workers/ Changes the status of this worker. | def patch(self, identifier):
data = binary_status_schema(g.payload)
if data.get('status') == 'ACTIVE':
current_app.config['APPLICATION'].enable_worker(identifier)
elif data.get('status') == 'DEACTIVATED':
current_app.config['APPLICATION'].disable_worker(identifier)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def patch(self, id):\n try:\n task = update_status(get_db(), id, Status[api.payload[\"status\"]])\n if not task:\n api.abort(404, \"Invalid Task\")\n return task_to_dict(task)\n except ValueError:\n api.abort(422, \"Invalid Status\")",
"def... | [
"0.55095226",
"0.55028725",
"0.54557097",
"0.54305637",
"0.5403861",
"0.53791434",
"0.5377578",
"0.5347136",
"0.533915",
"0.533581",
"0.53261644",
"0.53202647",
"0.52790004",
"0.5251339",
"0.5215275",
"0.5204497",
"0.5191661",
"0.5185492",
"0.5157756",
"0.5133342",
"0.5130274... | 0.74830425 | 0 |
if cursor position is on the button, create button frame | def create_frame(self, x: int, y: int):
if self.clicked(x, y):
x, y, w, h = self.rect
self.frame = pygame.Rect(x - 5, y - 5, w + 10, h + 10)
else:
self.frame = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_buttons(frame):\n button0 = Button(frame, height=2, width=2, text=\" \",\n command=lambda: on_click(button0))\n button0.pack(side=LEFT)\n button1 = Button(frame, height=2, width=2, text=\" \",\n command=lambda: on_click(button1))\n button1.pack(side=LE... | [
"0.6814768",
"0.6679351",
"0.6611863",
"0.65939415",
"0.65918565",
"0.65649325",
"0.6505203",
"0.64517725",
"0.64417976",
"0.6412699",
"0.6411252",
"0.63671803",
"0.63670474",
"0.63500637",
"0.6341348",
"0.6330736",
"0.6324912",
"0.6308765",
"0.62971395",
"0.6278057",
"0.6218... | 0.6383105 | 11 |
set a proffesional looking matplotlib style | def set_style(usetex=False):
plt.rc('text', usetex=usetex)
plt.rc('font', family='Serif')
mpl.rcParams['figure.figsize'] = [10, 7]
mpl.rcParams['font.size'] = 17
mpl.rcParams['savefig.dpi'] = 150
mpl.rcParams['xtick.minor.visible'] = True
mpl.rcParams['ytick.minor.visible'] = True
mpl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def customize_mpl():\r\n print(\"Setting custom matplotlib visual style\")\r\n\r\n rcParams['figure.figsize'] = (10, 6)\r\n rcParams['figure.dpi'] = 150\r\n rcParams['axes.color_cycle'] = dark2_colors\r\n rcParams['lines.linewidth'] = 2\r\n rcParams['axes.grid'] = True\r\n rcParams['axes.facec... | [
"0.75982976",
"0.718391",
"0.7182594",
"0.7110354",
"0.70964205",
"0.6949332",
"0.6936319",
"0.67766124",
"0.67148894",
"0.6619341",
"0.6582537",
"0.657382",
"0.6467556",
"0.64657557",
"0.64460784",
"0.64151937",
"0.6413506",
"0.6411621",
"0.63958895",
"0.6392753",
"0.6353010... | 0.7470834 | 1 |
Get the cached binary distribution archive that was previously built for the given package (name, version) (and optionally URL). If no archive has been cached yet, a new binary distribution archive is created and added to the cache. | def get_binary_dist(package, version, directory, url=None, python='/usr/bin/python', prefix='/usr'):
tag = hashlib.sha1(str(version + url).encode()).hexdigest() if url else version
cache_file = os.path.join(binary_index, '%s:%s:%s.tar.gz' % (package, tag, get_python_version()))
if not os.path.isfile(cach... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cache(name):\n\n return get_component(CachingPackage.COMPONENT_NAME).get_cache(name)",
"def cabal_get(name, unpack_to, version=None):\n pkg = name\n if version is not None:\n pkg += \"==\"\n pkg += version\n\n return run([\"cabal\", \"get\", pkg, \"-d\", unpack_to])",
"def existing_archive(... | [
"0.62168354",
"0.60056436",
"0.58326787",
"0.5793394",
"0.5715711",
"0.567902",
"0.56164545",
"0.5613404",
"0.55722487",
"0.5547017",
"0.55175894",
"0.5502366",
"0.54943293",
"0.54713947",
"0.5448375",
"0.54424584",
"0.54364425",
"0.54203945",
"0.5413666",
"0.54098874",
"0.53... | 0.7490097 | 0 |
Convert a single, unpacked source distribution to a binary distribution. Raises an exception if it fails to create the binary distribution (probably because of missing binary dependencies like system libraries). | def build_binary_dist(package, version, directory, python='/usr/bin/python'):
build_timer = Timer()
# Make sure the source distribution contains a setup script.
setup_script = os.path.join(directory, 'setup.py')
if not os.path.isfile(setup_script):
msg = "Directory %s (%s %s) doesn't contai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n if self.formats != [\"gztar\"] and self.formats != [\"zip\"]:\n print(\"'setup.py sdist' unsupported format.\")\n sys.exit(1)\n\n if glob.glob(\"*.tar.gz\"):\n print(\"'setup.py sdist' remove existing *.tar.gz files from \"\n \"source directory.\")\n sys.exit(1... | [
"0.6166575",
"0.6010504",
"0.5875719",
"0.5833119",
"0.5795637",
"0.574525",
"0.56041056",
"0.55501443",
"0.55416507",
"0.54898053",
"0.5410071",
"0.5375483",
"0.53528816",
"0.53478336",
"0.5306484",
"0.5289251",
"0.52809817",
"0.5252052",
"0.5176424",
"0.51637757",
"0.514191... | 0.584589 | 3 |
Transform a binary distribution archive created with ``python setup.py bdist_dumb format=tar`` into a form that can be cached for future use. This comes down to making the pathnames inside the archive relative to the `prefix` that the binary distribution was built for. | def transform_binary_dist(archive_path, prefix='/usr'):
# Copy the tar archive file by file so we can rewrite the pathnames.
logger.debug("Transforming binary distribution: %s.", archive_path)
logger.debug("Using environment prefix: %s.", prefix)
archive = tarfile.open(archive_path, 'r')
for me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gz_tar(full_prefix):\n tarfile = os.path.join(outputdir, full_prefix + '.tar')\n try:\n with open(tarfile, 'rb') as f_in, gzip.open(tarfile + '.gz', 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n os.remove(tarfile)\n except Exception as e:\n log.error(\"Tarfile {0}... | [
"0.6624995",
"0.5973443",
"0.59267676",
"0.58510005",
"0.5808216",
"0.5689753",
"0.56756777",
"0.5619002",
"0.5613805",
"0.55668515",
"0.5519208",
"0.54786164",
"0.54553145",
"0.5450021",
"0.5443351",
"0.5413397",
"0.5370573",
"0.5354789",
"0.53475803",
"0.53347725",
"0.53303... | 0.7719703 | 0 |
Install a binary distribution created with ``python setup.py bdist`` into the given prefix (a directory like ``/usr``, ``/usr/local`` or a virtual environment). | def install_binary_dist(members, prefix, python='/usr/bin/python', enable_workarounds=True):
# TODO This is quite slow for modules like Django. Speed it up! Two choices:
# 1. Run the external tar program to unpack the archive. This will
# slightly complicate the fixing up of hashbangs.
# 2. Us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def install(self, spec, prefix):\n make(\"install\", parallel=False)",
"def install(self, args=None, target=\"install\"):\n args = args if args else []\n str_args = \" \".join(args)\n if \"DESTDIR=\" not in str_args:\n args.insert(0, \"DESTDIR={}\".format(unix_path(self._co... | [
"0.6706299",
"0.65126616",
"0.6295004",
"0.6238352",
"0.60443175",
"0.60068166",
"0.59957397",
"0.5921789",
"0.57315",
"0.5648719",
"0.56375676",
"0.55756116",
"0.5550347",
"0.55466676",
"0.5510487",
"0.5481524",
"0.54365534",
"0.54067636",
"0.53959805",
"0.5365939",
"0.53503... | 0.6652997 | 1 |
Rewrite the hashbang in an executable script so that the Python program inside the virtual environment is used instead of a system wide Python. | def fix_hashbang(python, contents):
# Separate the first line in the file from the remainder of the contents
# while preserving the end of line sequence (CR+LF or just an LF) and
# without having to split all lines in the file (there's no point).
lines = contents.splitlines()
hashbang = lines[0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_shebang(self, handle, interpreter):\n if detect_python_script(handle):\n lines = handle.readlines()\n lines[0] = b'#!' + interpreter.encode('ascii') + b'\\n'\n handle = BytesIO(b''.join(lines))\n handle.seek(0)\n return handle",
"def process_sh... | [
"0.732829",
"0.6517055",
"0.64587206",
"0.6419364",
"0.6301114",
"0.5835952",
"0.57975733",
"0.56519705",
"0.5598901",
"0.5556552",
"0.55436015",
"0.55386496",
"0.5365093",
"0.53436875",
"0.5277366",
"0.52546036",
"0.5249833",
"0.524669",
"0.5241603",
"0.52317417",
"0.5179354... | 0.66236895 | 1 |
Initialize a requirement object. | def __init__(self, requirement):
self.pip_requirement = requirement
self.setuptools_requirement = requirement.req
# In pip-accel 0.10.4 and earlier the list of requirements returned by
# unpack_source_dists() contained tuples in the following format.
self.old_interface = (se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, req, argv):\n self._req = req\n self._argv = argv\n\n # We use a separate temp dir for each requirement so requirements\n # (from different indices) that happen to have the same archive names\n # don't overwrite each other, leading to a security hole in which t... | [
"0.66898435",
"0.63760674",
"0.62675565",
"0.624149",
"0.62248087",
"0.62169856",
"0.61996937",
"0.61996937",
"0.61996937",
"0.61996937",
"0.61813813",
"0.61390203",
"0.6118997",
"0.61149776",
"0.6075584",
"0.60059315",
"0.5966681",
"0.5942747",
"0.5942747",
"0.5931561",
"0.5... | 0.6283552 | 2 |
The name of the Python package (a string). This is the name used to register a package on PyPI and the name reported by commands like ``pip | def name(self):
return self.setuptools_requirement.project_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_package_name(self):\n return self.name + '-' + self.version",
"def get_package_name(self):\n return self.name + '-' + self.version + '-' + self.release",
"def package_name(self) -> str:\n return pulumi.get(self, \"package_name\")",
"def package_name(self):\n return self._p... | [
"0.82315797",
"0.81423026",
"0.8140491",
"0.7894246",
"0.77295196",
"0.76153123",
"0.7543961",
"0.7487066",
"0.7313372",
"0.7311747",
"0.7251059",
"0.72119623",
"0.70979613",
"0.70932734",
"0.7083767",
"0.7043299",
"0.7035155",
"0.70278776",
"0.7024797",
"0.69871116",
"0.6969... | 0.64047766 | 63 |
The version of the package that ``pip`` wants to install based on the command line options that were given to ``pip`` (a string). Based on | def version(self):
return self.pip_requirement.installed_version | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_pip_versioned_package_string(\n library_name: str, version_string: str\n) -> str:\n return '%s==%s' % (library_name, version_string)",
"def get_version():\n\n version_string = version_from_versioneer()\n\n if not version_string:\n version_string = version_from_pip()\n\n return vers... | [
"0.66763216",
"0.6673236",
"0.63909316",
"0.6285159",
"0.62627167",
"0.622569",
"0.62129605",
"0.62005633",
"0.61937195",
"0.6155948",
"0.611179",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",
"0.6058138",... | 0.674038 | 0 |
The pathname of the directory containing the unpacked source distribution. This is the directory that contains a ``setup.py`` | def source_directory(self):
return self.pip_requirement.source_dir | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetPackageDirectory():\n return os.path.dirname(__file__)",
"def get_package_dir():\n return Path(__file__).parent",
"def get_install_dir(self):\n return EventGenerator.get_install_dir(self) + \"/madgraph5/src\"",
"def get_install_dir(self):\n return EventGenerator.get_install_dir(sel... | [
"0.7882261",
"0.78249943",
"0.7451742",
"0.74421036",
"0.73956454",
"0.73768854",
"0.7256142",
"0.7101482",
"0.70624197",
"0.7059278",
"0.694501",
"0.6943025",
"0.6912458",
"0.68570054",
"0.6847402",
"0.68168926",
"0.6816051",
"0.6813868",
"0.6797789",
"0.6795502",
"0.6794880... | 0.7516564 | 2 |
``True`` when the requirement is already installed, ``False`` otherwise. | def is_installed(self):
return bool(self.pip_requirement.satisfied_by) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_installed(self):\n return not self.dont_install",
"def already_installed(lib_spec, options, working_set=None):\n if 'path' in options:\n return False\n\n if not working_set:\n working_set=pkg_resources.WorkingSet(sys.path)\n\n try:\n working_set.require(lib_spec)\n ... | [
"0.77823555",
"0.7527283",
"0.7521783",
"0.7446945",
"0.7324295",
"0.72457904",
"0.721714",
"0.71614337",
"0.706286",
"0.70098406",
"0.69969386",
"0.69896317",
"0.69786394",
"0.69777703",
"0.6964472",
"0.6922492",
"0.69222534",
"0.6917151",
"0.69150466",
"0.69119006",
"0.6896... | 0.79604423 | 0 |
``True`` when the requirement is a transitive dependency (a dependency of a dependency) or ``False`` when the requirement is a direct dependency (specified on pip's command line or in a ``requirements.txt`` file). Based on | def is_transitive(self):
return isinstance(self.pip_requirement.comes_from, InstallRequirement) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def valid_dependency(self, dep):\r\n return True",
"def _sufficient_deps(cls, deps):\n if cls.MODEL_PACKAGE is None:\n return True\n else:\n for d in deps.conda:\n if cls.MODEL_PACKAGE in d:\n return True\n for d in deps.pip:\n ... | [
"0.7150804",
"0.67961425",
"0.67164946",
"0.65914494",
"0.6496702",
"0.6477763",
"0.633333",
"0.63137054",
"0.62766606",
"0.62700015",
"0.6266488",
"0.62340945",
"0.61870563",
"0.61679566",
"0.6163255",
"0.6157765",
"0.6050587",
"0.6031543",
"0.60234386",
"0.6016165",
"0.5978... | 0.7746789 | 0 |
Return a string identifying the currently running Python version. | def get_python_version():
return "py%i.%i" % (sys.version_info[0], sys.version_info[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_python_version() -> str:\n return \"{} {} on {}\".format(\n platform.python_implementation(),\n platform.python_version(),\n platform.system(),\n )",
"def pythonversionstr():\n return '{t[0]}.{t[1]}.{t[2]}'.format(t=platform.python_version_tuple())",
"def python_version(se... | [
"0.83359855",
"0.79305583",
"0.7837343",
"0.76527643",
"0.76474893",
"0.7407734",
"0.7407734",
"0.7396613",
"0.7377381",
"0.7366962",
"0.7349132",
"0.7344607",
"0.733353",
"0.72470134",
"0.72237986",
"0.7210526",
"0.72045714",
"0.71998715",
"0.7187953",
"0.7175396",
"0.714991... | 0.77854 | 3 |
Create a temporary working directory and a virtual environment where pipaccel can be tested in isolation (starting with an empty download cache, source index and binary index and no installed modules) and make sure pip and pipaccel use the directory. Also creates the directories for the download cache, the source index... | def setUp(self):
coloredlogs.install(level=logging.DEBUG)
# Create a temporary working directory.
self.working_directory = tempfile.mkdtemp()
# Create a temporary build directory.
self.build_directory = os.path.join(self.working_directory, 'build')
# Create a tempor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n coloredlogs.install(level=logging.DEBUG)\n # Create a temporary working directory.\n self.working_directory = tempfile.mkdtemp()\n # Create a temporary build directory.\n self.build_directory = os.path.join(self.working_directory, 'build')\n # Create a t... | [
"0.76585984",
"0.6998828",
"0.6592483",
"0.65521127",
"0.65169436",
"0.648992",
"0.632207",
"0.62966186",
"0.6259255",
"0.61760193",
"0.6119715",
"0.6043009",
"0.60395974",
"0.60066956",
"0.59853506",
"0.59688634",
"0.595255",
"0.5901899",
"0.5901053",
"0.589884",
"0.5891566"... | 0.7686593 | 0 |
A very basic test of the functions that make up the pipaccel command using the `virtualenv` package as a test case. | def runTest(self):
# We will test the downloading, conversion to binary distribution and
# installation of the virtualenv package (we simply need a package we
# know is available from PyPI).
arguments = ['install', '--ignore-installed', 'virtualenv==1.8.4']
# First we do a s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runTest(self):\n # We will test the downloading, conversion to binary distribution and\n # installation of the virtualenv package (we simply need a package we\n # know is available from PyPI).\n arguments = ['install', '--ignore-installed', 'virtualenv==1.8.4']\n # First we d... | [
"0.75798845",
"0.69819945",
"0.6818097",
"0.6713533",
"0.6589174",
"0.6525606",
"0.6503367",
"0.63632447",
"0.63099754",
"0.622459",
"0.6204702",
"0.6194493",
"0.6139652",
"0.61071426",
"0.6083854",
"0.5994085",
"0.5992309",
"0.59918314",
"0.5952341",
"0.59369195",
"0.586638"... | 0.74043363 | 1 |
Cleanup the temporary working directory that was used during the test. | def tearDown(self):
shutil.rmtree(self.working_directory) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup(self):\r\n if self.tempDirectory != None:\r\n shutil.rmtree(self.tempDirectory, True)\r\n self.tempDirectory = None",
"def cleanup_temp_dir(context):\n\n try:\n os.chdir(context.cwd)\n except:\n print(\"Current working file record does not exist\")\n\n... | [
"0.8413275",
"0.8402331",
"0.8346875",
"0.8175108",
"0.81133807",
"0.80950266",
"0.8081311",
"0.80100954",
"0.79845655",
"0.7974465",
"0.79468673",
"0.7941451",
"0.7917673",
"0.7913126",
"0.7907241",
"0.7902104",
"0.7886492",
"0.7886492",
"0.7882106",
"0.78649205",
"0.7860815... | 0.78299254 | 22 |
Create a temporary txt file from HDF5. | def hdf2txt_tmp(fname):
print 'converting HDF5 -> temporary ASCII ...',
data, fin = readh5(fname)
f = tf.NamedTemporaryFile(suffix='') # create temp file
np.savetxt(f, data, fmt='%f')
f.seek(0)
closeh5(fin)
print 'done'
return f | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_as_hdf5(self, filename):",
"def temporary(cls):\n fh, path = tempfile.mkstemp(suffix='.hdf5')\n os.close(fh)\n self = cls(path, 'w')\n self.path = path\n return self",
"def test_create():\n\n with tempfile.TemporaryDirectory() as td:\n fp = os.path.join(td,... | [
"0.6588873",
"0.65184367",
"0.6387805",
"0.6336854",
"0.6114972",
"0.59868824",
"0.592905",
"0.5881204",
"0.5869554",
"0.5852435",
"0.58333004",
"0.5813109",
"0.58126175",
"0.5812096",
"0.5809681",
"0.57816786",
"0.57685274",
"0.57606965",
"0.57593733",
"0.57477915",
"0.57401... | 0.7862914 | 0 |
Takes a dataframe of processed eviction data and predictions from an ARIMAX model at the month level. Returns a dataframe grouped by ZIP with a column containing the average percentage of total evictions each ZIP represented | def top_down_forecast_data_processing(original_df,predictions_by_month):
transformed_df=original_df.groupby('Month_Year').sum().reset_index()\
[['Month_Year','Eviction_Notice']]
percentage_of_month_df = pd.merge(original_df[['Month_Year','Address_Zipcode',\
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_down_forecast(original_df,predictions_by_month,months_ahead):\n\n group_by_zip_df = top_down_forecast_data_processing(original_df,predictions_by_month)\n\n zip_perc_df= pd.DataFrame(np.random.randn(1, 4), columns=['zip_predicted','zip_code','perc_of_month','month_year'])\n\n for month in range(mon... | [
"0.6544387",
"0.58413523",
"0.57256114",
"0.56348085",
"0.555309",
"0.54977405",
"0.5485508",
"0.54725814",
"0.5401345",
"0.5389973",
"0.536069",
"0.5329506",
"0.5328788",
"0.5302536",
"0.52628785",
"0.5251212",
"0.5242129",
"0.522812",
"0.52199835",
"0.5200968",
"0.5185783",... | 0.739867 | 0 |
Takes a dataframe of processed eviction data and predictions from an ARIMAX model at the month level, as well as the number of months into the future to predict. | def top_down_forecast(original_df,predictions_by_month,months_ahead):
group_by_zip_df = top_down_forecast_data_processing(original_df,predictions_by_month)
zip_perc_df= pd.DataFrame(np.random.randn(1, 4), columns=['zip_predicted','zip_code','perc_of_month','month_year'])
for month in range(months_ahead):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\n columns=['month_year', 'predicted_evictions'])\n\n now = datetime.datetime.now()\n\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',a... | [
"0.79838103",
"0.7393525",
"0.725368",
"0.69809437",
"0.6334739",
"0.63147026",
"0.6194552",
"0.6134306",
"0.6121274",
"0.6016633",
"0.601663",
"0.5980682",
"0.5806225",
"0.57996064",
"0.5768583",
"0.57633406",
"0.5728102",
"0.5712658",
"0.5658473",
"0.5626327",
"0.56168824",... | 0.6652185 | 4 |
Takes a dataframe of processed eviction data as well as a set amount of lagged variables about future datapoints and how many months ahead to include in the prediction. | def arimax_by_month_train_test(model_data,future_data,months_ahead):
transformed_df = arima_by_zip_data_transform(model_data)
transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()
y_train = transformed_df
y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']]
now =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_predictions(year, month):\n \n start_date = str(year)+\"-\"+str(month)+\"-01\"\n end_date = str(year)+\"-\"+str(month)+\"-\"+str(monthrange(year, month)[1])\n\n date_range = pd.date_range(start_date,end_date, freq='D').strftime(\"%Y-%m-%d\").tolist()\n\n # predictfunction \n # do predict... | [
"0.6364175",
"0.63334864",
"0.6267178",
"0.59978694",
"0.5987615",
"0.5957174",
"0.59301317",
"0.57890916",
"0.5736458",
"0.5725275",
"0.5689226",
"0.56639534",
"0.5641206",
"0.56295705",
"0.5626032",
"0.5600229",
"0.5582438",
"0.55348295",
"0.5518111",
"0.55100405",
"0.55089... | 0.5992216 | 4 |
Takes in training and test data formatted as time series with an exogenous variable, CASANF0URN, which which represents unemployment rate in San Francisco from the previous year. | def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):
predictions_df= pd.DataFrame(np.random.randn(1, 2),\
columns=['month_year', 'predicted_evictions'])
now = datetime.datetime.now()
model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arimax_by_month_train_test(model_data,future_data,months_ahead):\n transformed_df = arima_by_zip_data_transform(model_data)\n transformed_df=transformed_df.groupby('Month_Year').sum().reset_index()\n\n y_train = transformed_df\n\n\n y_test = future_data[['Month_Year','Eviction_Notice','CASANF0URN']... | [
"0.6474548",
"0.5763003",
"0.56484705",
"0.56113726",
"0.55643433",
"0.555857",
"0.5496503",
"0.5457722",
"0.54512095",
"0.54455984",
"0.543785",
"0.5425412",
"0.5386848",
"0.5385404",
"0.53398514",
"0.5329046",
"0.5323245",
"0.5297824",
"0.5291544",
"0.5286219",
"0.527358",
... | 0.5213099 | 28 |
Activate the DHCP agent. | def run(self):
self.sync_state()
self.periodic_resync()
self.lease_relay.start()
self.notifications.run_dispatch(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activate(self):\n super().activate()\n self._change_lease_time(self.runner.config.get(\"dhcp_lease_time\"))\n self._scan_finalize()",
"def server_activate(self):\n\t\tpass",
"def activate_controller(self):\n if self.controller_address:\n #print \"Activating controller... | [
"0.7367523",
"0.6174387",
"0.6084445",
"0.6073693",
"0.6065638",
"0.6057743",
"0.59695095",
"0.5860989",
"0.5859584",
"0.5761732",
"0.5749872",
"0.5738167",
"0.57222456",
"0.5659488",
"0.56560457",
"0.56503",
"0.5624306",
"0.56200856",
"0.56175464",
"0.5599799",
"0.5599799",
... | 0.0 | -1 |
Invoke an action on a DHCP driver instance. | def call_driver(self, action, network):
if self.conf.use_namespaces:
namespace = NS_PREFIX + network.id
else:
namespace = None
try:
# the Driver expects something that is duck typed similar to
# the base models.
driver = self.dhcp_drive... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def device_action(host, details, action):\n if details:\n pprint(cs.get_device_details(host))\n if action:\n pprint(cs.device_action(host, action))",
"def device_action(self, client, action):\r\n client.deviceAction(action)",
"def _RunDHCPCD(self, **kwargs):\n del kwargs\n clea... | [
"0.61030775",
"0.5921867",
"0.58629996",
"0.5762756",
"0.57204163",
"0.56761485",
"0.56761485",
"0.55990696",
"0.55990696",
"0.5535578",
"0.55226314",
"0.54966766",
"0.54925567",
"0.5439608",
"0.541157",
"0.54060113",
"0.5376972",
"0.537439",
"0.53660226",
"0.5343927",
"0.529... | 0.7318727 | 0 |
Sync the local DHCP state with Quantum. | def sync_state(self):
LOG.info(_('Synchronizing state'))
known_networks = set(self.cache.get_network_ids())
try:
active_networks = set(self.plugin_rpc.get_active_networks())
for deleted_id in known_networks - active_networks:
self.disable_dhcp_helper(dele... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sync_experiment_state_with_ddb(self):\n if self.local_mode:\n self.sync_thread.sync_experiment_state_with_ddb()",
"def update(self):\n self._state = get_local_ip()",
"def sync():\n sync_ssda()",
"def update(self):\n try:\n if not self._sysinfo:\n ... | [
"0.6180898",
"0.5961581",
"0.56596506",
"0.5563325",
"0.55320233",
"0.5489496",
"0.54843223",
"0.5393621",
"0.5317044",
"0.5298483",
"0.52749354",
"0.5256482",
"0.5226604",
"0.5218777",
"0.51281685",
"0.5090927",
"0.5090464",
"0.50808954",
"0.5073649",
"0.5064125",
"0.5057595... | 0.6583717 | 0 |
Resync the dhcp state at the configured interval. | def _periodic_resync_helper(self):
while True:
eventlet.sleep(self.conf.resync_interval)
if self.needs_resync:
self.needs_resync = False
self.sync_state() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n active_networks = set(self.plugin_rpc.get_active_networks())\n for deleted_id in known_networks - active_networks:\n self.disable_dhcp... | [
"0.62378156",
"0.60955244",
"0.58129066",
"0.5434197",
"0.53986806",
"0.5379062",
"0.53275496",
"0.52896744",
"0.52710843",
"0.51772535",
"0.51282424",
"0.50909096",
"0.5082574",
"0.5062422",
"0.50369143",
"0.49950752",
"0.49852315",
"0.49442717",
"0.49381378",
"0.49372518",
... | 0.55289453 | 3 |
Spawn a thread to periodically resync the dhcp state. | def periodic_resync(self):
eventlet.spawn(self._periodic_resync_helper) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.sync_state()\n self.periodic_resync()\n self.lease_relay.start()\n self.notifications.run_dispatch(self)",
"def sync_state(self):\n LOG.info(_('Synchronizing state'))\n known_networks = set(self.cache.get_network_ids())\n\n try:\n ... | [
"0.6033633",
"0.559053",
"0.5557032",
"0.55510986",
"0.5474557",
"0.5411881",
"0.5409579",
"0.5318271",
"0.53177303",
"0.5296048",
"0.5247797",
"0.5238221",
"0.5237003",
"0.5179221",
"0.5164641",
"0.5161137",
"0.515453",
"0.5152708",
"0.51398647",
"0.5128912",
"0.51281554",
... | 0.50748193 | 23 |
Enable DHCP for a network that meets enabling criteria. | def enable_dhcp_helper(self, network_id):
try:
network = self.plugin_rpc.get_network_info(network_id)
except:
self.needs_resync = True
LOG.exception(_('Network %s RPC info call failed.') % network_id)
return
if not network.admin_state_up:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dhcp_agent_network_add(self, dhcp_net_info):\n self.turn_on_dhcp_check()",
"def enable_network_management(self):\n self._request({\"enable-network-management\": True})",
"def elAddNetworkConfigurationWithDhcp(self, device):\n commandSection = self.sectionByName(\"command\")\n # ... | [
"0.6613713",
"0.64811337",
"0.6424179",
"0.6270945",
"0.6147416",
"0.61341375",
"0.603933",
"0.5958222",
"0.59445256",
"0.5920509",
"0.5902045",
"0.5895537",
"0.58745736",
"0.57734",
"0.5736621",
"0.57331413",
"0.5694706",
"0.5690121",
"0.5680276",
"0.5655933",
"0.56160223",
... | 0.75427896 | 0 |
Disable DHCP for a network known to the agent. | def disable_dhcp_helper(self, network_id):
network = self.cache.get_network_by_id(network_id)
if network:
if self.call_driver('disable', network):
self.cache.remove(network) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dhcp_agent_network_remove(self, dhcp_net_info):\n self.turn_on_dhcp_check()",
"def network_delete_end(self, payload):\n self.disable_dhcp_helper(payload['network_id'])",
"def _disable_wifi_ap(self):\n call(['systemctl', 'disable', 'hostapd', ])\n call(['systemctl', 'disable', 'd... | [
"0.7135386",
"0.6868477",
"0.64515764",
"0.62737286",
"0.62714326",
"0.6229779",
"0.6223586",
"0.6125738",
"0.61127055",
"0.6056309",
"0.59756565",
"0.5961324",
"0.59271187",
"0.5900384",
"0.5843378",
"0.57802856",
"0.57735544",
"0.5729633",
"0.5697703",
"0.56655335",
"0.5631... | 0.7986895 | 0 |
Refresh or disable DHCP for a network depending on the current state of the network. | def refresh_dhcp_helper(self, network_id):
old_network = self.cache.get_network_by_id(network_id)
if not old_network:
# DHCP current not running for network.
return self.enable_dhcp_helper(network_id)
try:
network = self.plugin_rpc.get_network_info(network_id... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def network_update_end(self, payload):\n network_id = payload['network']['id']\n if payload['network']['admin_state_up']:\n self.enable_dhcp_helper(network_id)\n else:\n self.disable_dhcp_helper(network_id)",
"def disable_dhcp_helper(self, network_id):\n network ... | [
"0.70645887",
"0.7003579",
"0.6516991",
"0.6359894",
"0.6336317",
"0.61639994",
"0.60851556",
"0.607324",
"0.59917974",
"0.5893061",
"0.57851523",
"0.5669228",
"0.56041586",
"0.5593157",
"0.5579951",
"0.54653764",
"0.54648304",
"0.5416313",
"0.5412252",
"0.53830546",
"0.53794... | 0.733629 | 0 |
Handle the network.create.end notification event. | def network_create_end(self, payload):
network_id = payload['network']['id']
self.enable_dhcp_helper(network_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visit_graph_end(self, network: Network):\n pass",
"def onMessageEnd(self):",
"def network_create_event(self, network_info):\n net = network_info['network']\n net_id = net['id']\n net_name = net.get('name')\n network_db_elem = self.get_network(net_id)\n # Check if t... | [
"0.6248566",
"0.6031988",
"0.57522935",
"0.57338864",
"0.5701586",
"0.5675204",
"0.5653594",
"0.5636173",
"0.5629332",
"0.5538093",
"0.54424596",
"0.5430942",
"0.5419787",
"0.53785735",
"0.53761613",
"0.53417826",
"0.53303516",
"0.5320978",
"0.531378",
"0.530669",
"0.52717966... | 0.6849954 | 0 |
Handle the network.update.end notification event. | def network_update_end(self, payload):
network_id = payload['network']['id']
if payload['network']['admin_state_up']:
self.enable_dhcp_helper(network_id)
else:
self.disable_dhcp_helper(network_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notify_end(self, status, objective):\n pass # pragma: no cover",
"def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()",
"def end(update: Update, context: CallbackContext) -> int:\n update.callback_query.answer()\n\n text = \"See you around!\"\n updat... | [
"0.64979863",
"0.63618815",
"0.6354132",
"0.6308048",
"0.6289232",
"0.61400086",
"0.6041612",
"0.60274327",
"0.59674954",
"0.5949109",
"0.5926635",
"0.58869374",
"0.58779126",
"0.5860421",
"0.5796766",
"0.5758815",
"0.57569045",
"0.57188445",
"0.5713383",
"0.5688551",
"0.5639... | 0.6571846 | 0 |
Handle the network.delete.end notification event. | def network_delete_end(self, payload):
self.disable_dhcp_helper(payload['network_id']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_IN_DELETE(self, event):",
"def _notify_delete(self, cuds_object):",
"def on_deleted(self, event):\n\n # the absolute path of the event file/folder\n abs_path = event.src_path\n # replace the root path with a '.' to build a relative path to be sent to server\n relative_ev... | [
"0.6687941",
"0.65360117",
"0.6504362",
"0.6332553",
"0.6319839",
"0.6128922",
"0.6042391",
"0.6031273",
"0.6013475",
"0.6010431",
"0.596033",
"0.59602207",
"0.59489894",
"0.58846825",
"0.58623254",
"0.58432305",
"0.5838188",
"0.58338237",
"0.58286065",
"0.5809571",
"0.579758... | 0.68294984 | 0 |
Handle the subnet.update.end notification event. | def subnet_update_end(self, payload):
network_id = payload['subnet']['network_id']
self.refresh_dhcp_helper(network_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n subnet = self._get_subnet_info(context._subnet)\n if subnet is not None:\n try:\n self.rpc_handler.update_subnet(subnet)\n except:\n pass",
"d... | [
"0.66729087",
"0.6600842",
"0.62174314",
"0.6109604",
"0.6024752",
"0.5942195",
"0.5750984",
"0.57237357",
"0.5659294",
"0.5652078",
"0.56508094",
"0.55938303",
"0.55554795",
"0.55497646",
"0.55028933",
"0.5502632",
"0.5420597",
"0.5417562",
"0.5396377",
"0.53416765",
"0.5335... | 0.7799576 | 0 |
Handle the subnet.delete.end notification event. | def subnet_delete_end(self, payload):
subnet_id = payload['subnet_id']
network = self.cache.get_network_by_subnet_id(subnet_id)
if network:
self.refresh_dhcp_helper(network.id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_subnet_postcommit(self, context):\n if self.rpc_handler is None:\n return\n try:\n self.rpc_handler.delete_subnet({str(context._subnet.get('id', '')): {}})\n except:\n pass",
"def delete_subnet_postcommit(self, mech_context):\n LOG.debug(\"d... | [
"0.6993552",
"0.6756371",
"0.67003113",
"0.6559159",
"0.647308",
"0.6463808",
"0.6403783",
"0.63142115",
"0.6309298",
"0.62003213",
"0.57914406",
"0.5777481",
"0.57660466",
"0.57313883",
"0.57229215",
"0.565757",
"0.5645164",
"0.55317897",
"0.55291295",
"0.5499232",
"0.548507... | 0.7865546 | 0 |
Handle the port.update.end notification event. | def port_update_end(self, payload):
port = DictModel(payload['port'])
network = self.cache.get_network_by_id(port.network_id)
if network:
self.cache.put_port(port)
self.call_driver('reload_allocations', network) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_finish_event(self):\n self.status['type'] = '__end__'\n self._send()",
"def onMessageEnd(self):",
"def end(update: Update, context: CallbackContext) -> int:\n update.callback_query.answer()\n\n text = \"See you around!\"\n update.callback_query.edit_message_text(text=text)\n\n ... | [
"0.6465052",
"0.6250917",
"0.6206949",
"0.6200631",
"0.6087534",
"0.60749346",
"0.60135967",
"0.59975713",
"0.5883709",
"0.58819026",
"0.58666104",
"0.5856382",
"0.58052033",
"0.57543814",
"0.5744977",
"0.5744686",
"0.57377493",
"0.57021797",
"0.56638414",
"0.5663316",
"0.566... | 0.65497047 | 0 |
Handle the port.delete.end notification event. | def port_delete_end(self, payload):
port = self.cache.get_port_by_id(payload['port_id'])
if port:
network = self.cache.get_network_by_id(port.network_id)
self.cache.remove_port(port)
self.call_driver('reload_allocations', network) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_IN_DELETE(self, event):",
"def delete_port_postcommit(self, context):\n if self.rpc_handler is None:\n return\n port = self._get_port_info(context)\n if port is not None:\n try:\n self.rpc_handler.delete_port(port)\n except:\n ... | [
"0.6525613",
"0.65228605",
"0.646454",
"0.6217715",
"0.62165207",
"0.6130076",
"0.6126507",
"0.6012117",
"0.5925986",
"0.58993256",
"0.5834277",
"0.5791649",
"0.57853687",
"0.57846904",
"0.5774742",
"0.5691563",
"0.5691563",
"0.56881493",
"0.56836855",
"0.5662047",
"0.5659632... | 0.6916865 | 0 |
Make a remote process call to retrieve the active networks. | def get_active_networks(self):
return self.call(self.context,
self.make_msg('get_active_networks', host=self.host),
topic=self.topic) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_networks():\n return get_networks()",
"def list_net(self):\n _url = \"http://\" + self.host_ip + \":9696/v2.0/networks\"\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.project_info[\"token_project\"]}\n _body = None\n\n response... | [
"0.657749",
"0.65077436",
"0.638325",
"0.6311575",
"0.61095214",
"0.5842249",
"0.57740545",
"0.5771512",
"0.5732876",
"0.5730552",
"0.57152635",
"0.571397",
"0.57076454",
"0.57016504",
"0.56785256",
"0.56415784",
"0.5635792",
"0.5630904",
"0.5617051",
"0.55758715",
"0.5521408... | 0.6694506 | 0 |
Make a remote process call to retrieve network info. | def get_network_info(self, network_id):
return DictModel(self.call(self.context,
self.make_msg('get_network_info',
network_id=network_id,
host=self.host),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remote_info():\n run('uname -a')",
"def do_remote(self, *args):\n return self.do_scpi(':communicate:remote 1')",
"def do_local(self, *args):\n return self.do_scpi(':communicate:remote 0')",
"def subcmd_getnic_main(args, parameter_info):\n \n from get_nic_inventory import get_nic_in... | [
"0.6053576",
"0.60060537",
"0.5726204",
"0.56940055",
"0.5621628",
"0.5613466",
"0.54797566",
"0.544631",
"0.5397861",
"0.5345061",
"0.5319306",
"0.5302995",
"0.5250394",
"0.5229157",
"0.52221423",
"0.51966923",
"0.51758206",
"0.513977",
"0.5130418",
"0.51230186",
"0.51097083... | 0.0 | -1 |
Make a remote process call to create the dhcp port. | def get_dhcp_port(self, network_id, device_id):
return DictModel(self.call(self.context,
self.make_msg('get_dhcp_port',
network_id=network_id,
device_id=device_id,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dhcp_port(self, port):\n LOG.debug(\"create_dhcp_port: %s\", port)\n port['port']['id'] = port['port']['network_id']\n\n # The following MAC address will be assigned to the Linux dummy\n # interface that\n # networking_calico.agent.linux.interface.RoutedInterfaceDriver... | [
"0.6433571",
"0.62138724",
"0.60005367",
"0.59957063",
"0.59321785",
"0.59223795",
"0.5902261",
"0.58771735",
"0.58316875",
"0.5814186",
"0.5716689",
"0.5710547",
"0.5704256",
"0.56838495",
"0.5680676",
"0.5598628",
"0.5588033",
"0.5549641",
"0.553411",
"0.55322653",
"0.55301... | 0.48129672 | 99 |
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