luomein

class ComplicationController: NSObject, CLKComplicationDataSource {
    func getComplicationDescriptors(handler: @escaping ([CLKComplicationDescriptor]) -> Void) {
        // for graphicCircular
        let descriptors = [
           CLKComplicationDescriptor(identifier: "graphicCircular", displayName: "graphicCircular", supportedFamilies:[ CLKComplicationFamily.graphicCircular]),
        ]
        
        // Call the handler with the currently supported complication descriptors
        handler(descriptors)
    }

luomein

class ComplicationController: NSObject, CLKComplicationDataSource {
    func getComplicationDescriptors(handler: @escaping ([CLKComplicationDescriptor]) -> Void) {
        // for graphicCircular
        let descriptors = [
           CLKComplicationDescriptor(identifier: "graphicCircular1", displayName: "graphicCircular1", supportedFamilies:[ CLKComplicationFamily.graphicCircular]),
           CLKComplicationDescriptor(identifier: "graphicCircular2", displayName: "graphicCircular2", supportedFamilies:[ CLKComplicationFamily.graphicCircular]),
        ]
        
        // Call the handler with the currently supported complication descriptors
        handler(descriptors)

luomein

luomein

import pandas as pd
import numpy as np

def get_rank_df(df , station_column , time_column , sort_time_ascending = True ):
    df.sort_values(by=[station_column , time_column] , ignore_index = True , inplace = True)
    df = df.drop_duplicates(subset=[station_column , time_column]).reset_index(drop = True)

    df["rank"] = df.groupby(station_column)[time_column].rank("dense", ascending= sort_time_ascending )
    df = df.astype({'rank': 'int32'} ) 
    

luomein

import pandas as pd
import numpy as np


def filter_time_windows(result_df , station_column , time_window_size ):
    result_df['counts'] = result_df.groupby([station_column, 'rank_group'])['rank'].transform('size')
    result_df = result_df[result_df.counts == time_window_size  ]
    
    return result_df
  

luomein

import pandas as pd
import numpy as np

def get_feature_array(df , feature_column_list , time_window_size ) :
    feature_count = len(feature_column_list )
    x = df.loc[: , feature_column_list  ].to_numpy().reshape( -1  , time_window_size , feature_count )
    return x 

luomein

import pandas as pd
import numpy as np

def get_grouped_df(df ,station_column , rank_group_column ):
    gdb = df.groupby([station_column, rank_group_column ], sort = False).size()
    y = pd.DataFrame(data = { station_column : gdb.index.get_level_values(0).tolist() , rank_group_column : gdb.index.get_level_values(1).tolist() })
    return y

luomein

    func testFetchResultIsFault()throws{
        for i in 0..<configurations.count{
            //given
            let viewContext = try initViewContext()
            let configuration = configurations[i]
            let fetchRequest = getFetchRequest(configuration:configuration)
             
            //when
            let object = try viewContext.fetch(fetchRequest).first! as! NSManagedObject
            

luomein

import SwiftUI

class AppDelegate: NSObject, UIApplicationDelegate {
    func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey : Any]? = nil) -> Bool {
        application.registerForRemoteNotifications()
        let center = UNUserNotificationCenter.current()
        center.requestAuthorization(options: [.alert, .sound, .badge]) { granted, error in
            if let error = error {
                // Handle the error here.
            }

luomein

import XCTest
import CoreData
@testable import labSingleStore

final class labSingleStoreTests: XCTestCase {

    func testAutoConfiguredStore(){
        let container = PersistenceController.shared.container
        
        assert(container.persistentStoreDescriptions.count == 1)