HI I've updated my fortran program and I'm trying to get through the notarizing process. I created a pkg with >pkgbuild ... I signed the package with >productsign ... I created an application password for notarytool I created store credentials with >xcrun notarytool store-credentials ... Received: Success. Credentials validated. Credentials saved to Keychain. Requested notarization with >xcrun notarytool submit ... Submission ID received Successfully uploaded file Current status: Waiting.... after a minute or two this changed to Current status: Invalid... Could this because the notarizing system doesn't understand Fortran executables or is there something else I've done incorrectly? I have a submission id, is there a way to get more information. Thanks, David

I'm importing a csv file of 299 rows and creating a subset by filtering on one column value (24 rows). I want to Chart just the filtered values. However, when I print one column I get values from the original dataFrame. Any suggestions? Thanks, David The code: import SwiftUI import Charts import TabularData struct DataPoint: Identifiable { var id = UUID() // This makes it conform to Identifiable var date: Date var value: Double } struct ContentView: View { @State private var dataPoints: [DataPoint] = [] var body: some View { Text("Hello") Chart { ForEach(dataPoints) { dataPoint in PointMark( x: .value("Date", dataPoint.date), y: .value("Value", dataPoint.value) ) } } .frame(height: 300) .padding() .onAppear(perform: loadData) } func loadData() { print("In Loading Data") // Load the CSV file if let url = Bundle.main.url(forResource: "observations", withExtension: "csv") { do { let options = CSVReadingOptions(hasHeaderRow: true, delimiter: ",") var data0 = try DataFrame(contentsOfCSVFile: url, options: options) let formattingOptions = FormattingOptions( maximumLineWidth: 200, maximumCellWidth: 15, maximumRowCount: 30 ) // print(data0.description(options: formattingOptions)) print("Number of Columns: \(data0.columns.count)") let columnsSet = ["plant_id", "date", "plot", "plantNumber", "plantCount"] data0 = try DataFrame(contentsOfCSVFile:url, columns: columnsSet, options: options) print("Number of Columns (after columnsSet): \(data0.columns.count)") print("Printing data0") print(data0.description(options: formattingOptions)) let data = data0.filter { $0["plant_id"] as? Int == 15 } print("Printing data") print(data.description(options: formattingOptions)) print(" Number of Rows \(data.rows.count)") for i in 0 ... data.rows.count { // print("\(i): \(data["plantCount"][i]!)") if let plantCount = data["plantCount"][i] as? Int { print("\(i): \(plantCount)") } else { print("\(i): Value not found or invalid type") } } // // var newDataPoints: [DataPoint] = [] // Here I plan to add the filtered data to DataPoint // DispatchQueue.main.async { dataPoints = newDataPoints } } catch { print("Error reading CSV file: \(error)") } } else { print("Didn't load csv file") } } } struct ContentView_Previews: PreviewProvider { static var previews: some View { ContentView() } } Here is the new dataFrame and print output Printing data ┏━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ ┃ plant_id ┃ date ┃ plot ┃ plantNumber ┃ plantCount ┃ ┃ ┃ <Int> ┃ <String> ┃ <Int> ┃ <Int> ┃ <Int> ┃ ┡━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ 0 │ 15 │ 2023-09-07 │ 1 │ 5 │ 5 │ │ 32 │ 15 │ 2023-09-07 │ 2 │ 10 │ 10 │ │ 38 │ 15 │ 2023-09-07 │ 2 │ 20 │ 20 │ │ 66 │ 15 │ 2023-09-07 │ 4 │ 25 │ 25 │ │ 77 │ 15 │ 2023-09-07 │ 5 │ 5 │ 5 │ │ 99 │ 15 │ 2023-09-14 │ 7 │ 45 │ 45 │ │ 142 │ 15 │ 2024-05-30 │ 1 │ 20 │ 20 │ │ 162 │ 15 │ 2024-05-30 │ 4 │ 5 │ 5 │ │ 169 │ 15 │ 2024-05-30 │ 5 │ 10 │ 10 │ │ 175 │ 15 │ 2024-05-30 │ 7 │ 10 │ 10 │ │ 188 │ 15 │ 2024-07-11 │ 1 │ 20 │ 40 │ │ 199 │ 15 │ 2024-07-11 │ 2 │ 5 │ 5 │ │ 215 │ 15 │ 2024-07-11 │ 5 │ 20 │ 30 │ │ 220 │ 15 │ 2024-07-11 │ 7 │ 30 │ 40 │ │ 236 │ 15 │ 2024-09-06 │ 1 │ 20 │ 60 │ │ 238 │ 15 │ 2024-09-06 │ 2 │ 30 │ 35 │ │ 248 │ 15 │ 2024-09-06 │ 5 │ 5 │ 35 │ │ 254 │ 15 │ 2024-09-06 │ 7 │ 50 │ 90 │ │ 267 │ 15 │ 2025-05-04 │ 1 │ 10 │ 70 │ │ 273 │ 15 │ 2025-05-04 │ 2 │ 10 │ 45 │ │ 282 │ 15 │ 2025-05-04 │ 5 │ 10 │ 45 │ │ 287 │ 15 │ 2025-05-04 │ 7 │ 30 │ 120 │ │ 292 │ 15 │ 2025-05-04 │ 8 │ 10 │ 0 │ │ 297 │ 15 │ 2925-05-04 │ 3 │ 10 │ 0 │ └─────┴──────────┴────────────┴───────┴─────────────┴────────────┘ 24 rows, 5 columns Number of Rows 24 0: 5 1: 80 2: 1 3: 1 4: 1 5: 3 6: 3 7: 1 8: 6 9: 1 10: 1 11: 1 12: 1 13: 10 14: 50 15: 1 16: 2 17: 1 18: 3 19: 8 20: 5 21: 3 22: 7 23: 2 24: 1