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SiekamCebuleSiekamCebule
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Do a BIG work on trainings. Training Analyzer, engine, UI...
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‎lib/bloc/training_analyzer/training_analyzer_cubit.dart

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import 'dart:convert';
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import 'dart:io';
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import 'package:equatable/equatable.dart';
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import 'package:flutter/material.dart';
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import 'package:flutter_bloc/flutter_bloc.dart';
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import 'package:path/path.dart' as path;
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import 'package:sj_manager/bloc/training_analyzer/training_test_runner.dart';
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import 'package:sj_manager/json/json_types.dart';
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import 'package:sj_manager/models/simulation/flow/dynamic_params/jumper_dynamic_params.dart';
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import 'package:sj_manager/models/simulation/flow/training/jumper_training_config.dart';
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import 'package:sj_manager/models/training_analyzer/actions.dart';
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import 'package:sj_manager/models/training_analyzer/chart_data_category.dart';
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import 'package:sj_manager/models/training_analyzer/training_analyzer_result.dart';
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import 'package:sj_manager/models/training_analyzer/training_segment.dart';
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import 'package:sj_manager/models/user_db/jumper/jumper_skills.dart';
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import 'package:sj_manager/models/user_db/psyche/level_of_consciousness.dart';
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import 'package:sj_manager/training_engine/jumper_training_engine_settings.dart';
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import 'package:sj_manager/utils/file_system.dart';
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import 'package:sj_manager/utils/training_analyzer_utils.dart';
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class TrainingAnalyzerCubit extends Cubit<TrainingAnalyzerNotSimulated> {
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TrainingAnalyzerCubit()
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: super(TrainingAnalyzerNotSimulated(
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dataCategories: TrainingAnalyzerDataCategory.values.toSet()));
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void simulate(
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{required PlarformSpecificPathsCache pathsCache,
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Set<TrainingAnalyzerActions> additionalActions = const {}}) {
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late final Json configJson;
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try {
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final configFile =
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userDataFile(pathsCache, path.join('training_analyzer', 'config.json'));
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configJson = jsonDecode(configFile.readAsStringSync()) as Json;
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} catch (e) {
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debugPrint(
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'Błąd podczas wczytywania konfiguracji (config.json). Oryginalna treść błędu: $e');
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rethrow;
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}
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final engineSettingsJson = configJson['engineSettings'] as Json;
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final dynamicParamsJson = configJson['dynamicParams'] as Json;
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final skillsJson = configJson['jumperSkills'] as Json;
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final segmentsJson = (configJson['trainingSegments'] as List).cast<Json>();
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final engineSettings = JumperTrainingEngineSettings.fromJson(engineSettingsJson);
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final jumperSkills = JumperSkills.fromJson(skillsJson);
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final dynamicParams = JumperDynamicParams(
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trainingConfig: null,
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form: (dynamicParamsJson['form'] as num).toDouble(),
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trainingFeeling: (dynamicParamsJson['trainingFeeling'] as Map).map(
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(categoryName, value) => MapEntry(
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JumperTrainingCategory.values.singleWhere((cat) => cat.name == categoryName),
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(value as num).toDouble(),
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),
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),
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jumpsConsistency: (dynamicParamsJson['jumpsConsistency'] as num).toDouble(),
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morale: (dynamicParamsJson['morale'] as num).toDouble(),
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fatigue: (dynamicParamsJson['fatigue'] as num).toDouble(),
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levelOfConsciousness: LevelOfConsciousness.fromMapOfConsciousness(
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LevelOfConsciousnessLabels.values.singleWhere(
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(lbl) => lbl.name == dynamicParamsJson['levelOfConsciousness'],
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),
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),
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);
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final trainingSegments = segmentsJson.map((segmentJson) => TrainingSegment(
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start: segmentJson['start'],
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end: segmentJson['end'],
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scale: (segmentJson['scale'] as num).toDouble(),
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trainingConfig: JumperTrainingConfig(
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balance: (segmentJson['trainingConfig']['balance'] as Map).map(
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(categoryName, value) => MapEntry(
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JumperTrainingCategory.values
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.singleWhere((value) => value.name == categoryName),
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(value as num).toDouble(),
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),
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),
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),
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));
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final runner = TrainingTestRunner(
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segments: trainingSegments.toList(),
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dynamicParams: dynamicParams,
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jumperSkills: jumperSkills,
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engineSettings: engineSettings,
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daysToSimulate: configJson['daysToSimulate'],
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);
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final result = runner.run();
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if (additionalActions.contains(TrainingAnalyzerActions.saveFormattedTxt)) {
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final buffer = StringBuffer();
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for (var day = 1; day <= result.dayResults.length; day++) {
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final dayResult = result.dayResults[day - 1];
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buffer.writeln(
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'Dzień $day: ${formatJumperTrainingResultResultForAnalyzer(dayResult.trainingResult)}');
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}
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final resultsFile = userDataFile(
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pathsCache, path.join('training_analyzer', 'formatted_results.txt'));
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resultsFile.writeAsStringSync(buffer.toString());
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}
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if (additionalActions.contains(TrainingAnalyzerActions.saveCsv)) {
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final resultsFile =
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userDataFile(pathsCache, path.join('training_analyzer', 'results.csv'));
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final csv = TrainingAnalyzerDaySimulationResult.listToCsv(result.dayResults,
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delimiter: ';');
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resultsFile.writeAsStringSync(csv);
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}
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if (additionalActions.contains(TrainingAnalyzerActions.saveAttributeStats)) {
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_writeAttributeStats(
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result.dayResults
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.map((result) => result.trainingResult.skills.takeoffQuality)
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.toList(),
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userDataFile(pathsCache, path.join('training_analyzer', 'takeoff_stats.txt')),
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);
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_writeAttributeStats(
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result.dayResults
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.map((result) => result.trainingResult.skills.flightQuality)
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.toList(),
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userDataFile(pathsCache, path.join('training_analyzer', 'flight_stats.txt')),
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);
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_writeAttributeStats(
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result.dayResults
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.map((result) => result.trainingResult.skills.landingQuality)
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.toList(),
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userDataFile(pathsCache, path.join('training_analyzer', 'landing_stats.txt')),
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);
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_writeAttributeStats(
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result.dayResults.map((result) => result.trainingResult.form).toList(),
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userDataFile(pathsCache, path.join('training_analyzer', 'form_stats.txt')),
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);
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}
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emit(
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TrainingAnalyzerSimulated(
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result: result,
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dataCategories: state.dataCategories,
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),
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);
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}
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void _writeAttributeStats(List<double> values, File file) {
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final buffer = StringBuffer();
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var deltas = <double>[];
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for (int i = 0; i < values.length - 1; i++) {
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deltas.add(values[i + 1] - values[i]);
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}
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deltas.sort((a, b) => b.abs().compareTo(a.abs()));
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final deltasMaxRange = 25.clamp(0, values.length);
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buffer.writeln('TOP $deltasMaxRange zmian wartości:');
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for (var i = 0; i < deltasMaxRange; i++) {
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final delta = deltas[i];
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var furtherText = delta != 0 ? delta.abs().toStringAsFixed(2) : '';
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buffer.writeln('${i + 1}. $furtherText');
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}
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final sortedForm = List.of(values)..sort((first, second) => second.compareTo(first));
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final highFormMaxRange = 25.clamp(0, values.length);
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buffer.writeln('\n\nTOP $highFormMaxRange najwyższych wartości');
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for (var i = 0; i < highFormMaxRange; i++) {
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final delta = sortedForm[i];
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var furtherText = delta != 0 ? delta.abs().toStringAsFixed(2) : '';
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buffer.writeln('${i + 1}. $furtherText');
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}
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sortedForm.sort((first, second) => first.compareTo(second));
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final lowFormMaxRange = 25.clamp(0, values.length);
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buffer.writeln('\n\nTOP $lowFormMaxRange najniższych wartości');
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for (var i = 0; i < lowFormMaxRange; i++) {
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final delta = sortedForm[i];
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var furtherText = delta != 0 ? delta.abs().toStringAsFixed(2) : '';
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buffer.writeln('${i + 1}. $furtherText');
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}
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file.writeAsStringSync(buffer.toString());
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}
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void setCategories(Set<TrainingAnalyzerDataCategory> categories) {
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if (state.runtimeType == TrainingAnalyzerNotSimulated) {
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emit(state.copyWith(dataCategories: categories));
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} else if (state.runtimeType == TrainingAnalyzerSimulated) {
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emit((state as TrainingAnalyzerSimulated).copyWith(
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dataCategories: categories,
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));
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}
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}
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}
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class TrainingAnalyzerNotSimulated with EquatableMixin {
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const TrainingAnalyzerNotSimulated({
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required this.dataCategories,
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});
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final Set<TrainingAnalyzerDataCategory> dataCategories;
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@override
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List<Object?> get props => [
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dataCategories,
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];
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TrainingAnalyzerNotSimulated copyWith({
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Set<TrainingAnalyzerDataCategory>? dataCategories,
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}) {
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return TrainingAnalyzerNotSimulated(
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dataCategories: dataCategories ?? this.dataCategories,
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);
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}
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}
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class TrainingAnalyzerSimulated extends TrainingAnalyzerNotSimulated {
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const TrainingAnalyzerSimulated({
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required this.result,
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required super.dataCategories,
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});
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final TrainingAnalyzerResult result;
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@override
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List<Object?> get props => [
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result,
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super.props,
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];
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@override
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TrainingAnalyzerSimulated copyWith({
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TrainingAnalyzerResult? result,
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Set<TrainingAnalyzerDataCategory>? dataCategories,
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}) {
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return TrainingAnalyzerSimulated(
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result: result ?? this.result,
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dataCategories: dataCategories ?? this.dataCategories,
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);
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}
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}

‎lib/bloc/training_analyzer/training_test_runner.dart

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import 'package:sj_manager/models/training_analyzer/training_analyzer_result.dart';
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import 'package:sj_manager/models/training_analyzer/training_segment.dart';
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import 'package:sj_manager/models/simulation/flow/dynamic_params/jumper_dynamic_params.dart';
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import 'package:sj_manager/models/user_db/jumper/jumper_skills.dart';
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import 'package:sj_manager/training_engine/jumper_training_engine.dart';
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import 'package:sj_manager/training_engine/jumper_training_engine_settings.dart';
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class TrainingTestRunner {
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const TrainingTestRunner({
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required this.segments,
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required this.dynamicParams,
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required this.jumperSkills,
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required this.engineSettings,
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required this.daysToSimulate,
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});
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final List<TrainingSegment> segments;
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final JumperDynamicParams dynamicParams;
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final JumperSkills jumperSkills;
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final JumperTrainingEngineSettings engineSettings;
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final int daysToSimulate;
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TrainingAnalyzerResult run() {
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final dayResults = <TrainingAnalyzerDaySimulationResult>[];
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var currentDynamicParams = dynamicParams;
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var currentJumperSkills = jumperSkills;
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TrainingAnalyzerDaySimulationResult? lastResult;
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for (var day = 1; day < daysToSimulate + 1; day++) {
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final applicableSegment = segments.cast<TrainingSegment?>().firstWhere(
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(segment) => segment!.start <= day && segment.end >= day,
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orElse: () => null,
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);
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if (applicableSegment == null) {
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if (lastResult != null) {
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dayResults.add(lastResult);
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continue;
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} else {
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throw StateError('There is no training segment in passed list. Hmm...');
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}
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}
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final engine = JumperTrainingEngine(
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settings: engineSettings,
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jumperSkills: currentJumperSkills,
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dynamicParams: currentDynamicParams.copyWith(
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trainingConfig: applicableSegment.trainingConfig,
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),
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scaleFactor: applicableSegment.scale,
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);
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final trainingResult = engine.doTraining();
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lastResult = TrainingAnalyzerDaySimulationResult(
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day: day,
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trainingResult: trainingResult,
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);
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currentDynamicParams = currentDynamicParams.copyWith(
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form: trainingResult.form,
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trainingFeeling: trainingResult.trainingFeeling,
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jumpsConsistency: trainingResult.jumpsConsistency,
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fatigue: trainingResult.fatigue,
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);
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currentJumperSkills = trainingResult.skills;
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dayResults.add(lastResult);
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}
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return TrainingAnalyzerResult(
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segments: segments,
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dayResults: dayResults,
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);
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}
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}

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