[feature] 服务测试
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3
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3
.idea/misc.xml
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@ -1,4 +1,7 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.13" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13" project-jdk-type="Python SDK" />
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</project>
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1
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1
.idea/vcs.xml
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@ -2,5 +2,6 @@
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="" vcs="Git" />
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<mapping directory="$PROJECT_DIR$/param_struct_test" vcs="Git" />
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</component>
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</project>
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57
app.py
57
app.py
@ -1,3 +1,5 @@
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import time
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from component.widget_main.widget_main import Widget_Main
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from component.widget_channel.widget_channel import Widget_Channel
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from component.widget_card.widget_card import Widget_Card
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@ -13,49 +15,49 @@ from PySide6.QtWidgets import QMainWindow, QPushButton, QVBoxLayout
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from PySide6.QtWidgets import QWidget
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from PySide6.QtCore import QObject
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class MainWindow(QWidget):
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def __init__(self):
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super().__init__()
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# 初始化服务
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ServiceManager.instance().init_services("127.0.0.1", 1234)
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ServiceManager.instance().init_services("192.168.5.4", 12345)
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# 初始化应用控制器
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self.app_controller = ApplicationController.instance()
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self.widget_main = Widget_Main()
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self.widget_channel = Widget_Channel()
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self.widget_card = Widget_Card()
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# self.widget_main.ui.ListWidget_vLayout.addWidget(self.widget_card)
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# 添加测试按钮
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self.test_button = QPushButton("Get_All")
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self.test_button.clicked.connect(self.Get_All)
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self.widget_main.ui.ListWidget_vLayout.addWidget(self.test_button)
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self.widget_main.ui.ListWidget_vLayout.addWidget(self.widget_card)
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self.widget_main.ui.Channel_hLayout.addWidget(self.widget_channel)
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self.widget_filter_list = []
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self.filter_controllers = [] # 存储控制器实例
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# 添加测试按钮
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self.test_button = QPushButton("Test Communication")
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self.test_button.clicked.connect(self.test_communication)
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self.widget_main.ui.ListWidget_vLayout.addWidget(self.test_button)
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self.create_filter_widget()
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self.setup_connections()
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def create_filter_widget(self):
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for i in range(24):
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for i in range(1, 7):
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# 创建widget
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filter_widget = AudioFilterWidget()
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filter_widget.set_channel_id(i)
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filter_widget.set_channel_name(f"Channel {i+1}")
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filter_widget.set_channel_name(f"Channel {i}")
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# 创建model和controller
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model = AudioFilterModel(channel_id=i, channel_name=f"Channel {i+1}")
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model = AudioFilterModel(channel_id=i, channel_name=f"Channel {i}")
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controller = AudioFilterController(model)
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controller.set_widget(filter_widget)
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# 连接控制器信号
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controller.error_occurred.connect(lambda msg: print(f"Error: {msg}"))
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controller.state_changed.connect(lambda state: print(f"State changed: {state}"))
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controller.params_synced.connect(lambda: print("Params synced"))
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# 存储实例
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self.widget_filter_list.append(filter_widget)
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self.filter_controllers.append(controller)
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@ -71,18 +73,29 @@ class MainWindow(QWidget):
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def test_communication(self):
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"""测试通信功能"""
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print("Testing communication...")
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# 测试第一个控制器的通信
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if self.filter_controllers:
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controller = self.filter_controllers[0]
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# 测试从服务器加载数据
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print("Testing load from server...")
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controller.load_from_server()
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# 测试同步数据到服务器
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print("Testing sync to server...")
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# controller.sync_to_server()
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controller.sync_to_server()
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def Get_All(self):
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try:
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for controller in self.filter_controllers:
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controller.load_from_server()
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break
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time.sleep(1)
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print("Successfully loaded all filter data")
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except Exception as e:
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print(f"Error loading filter data: {e}")
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if __name__ == '__main__':
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import sys
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@ -90,7 +103,7 @@ if __name__ == '__main__':
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app = QApplication(sys.argv)
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main_window = MainWindow()
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# # 添加测试卡片
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# for i in range(1, 11):
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# data = CardData(
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@ -99,6 +112,6 @@ if __name__ == '__main__':
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# description=f"这是项目 {i} 的详细描述信息，可以包含多行文本内容。这是一个较长的描述，用于测试换行效果。"
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# )
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# main_window.widget_card.add_card_item(data)
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main_window.widget_main.show()
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sys.exit(app.exec())
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@ -17,7 +17,7 @@ class MainWindow(QWidget):
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def __init__(self):
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super().__init__()
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# 初始化服务
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ServiceManager.instance().init_services("127.0.0.1", 1234)
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ServiceManager.instance().init_services("192.168.5.4", 12345)
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# 初始化应用控制器
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self.app_controller = ApplicationController.instance()
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@ -33,7 +33,7 @@ class MainWindow(QWidget):
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# 添加测试按钮
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self.test_button = QPushButton("Test Communication")
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self.test_button.clicked.connect(self.test_communication)
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self.test_button.clicked.connect(self.Get_All)
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self.widget_main.ui.ListWidget_vLayout.addWidget(self.test_button)
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self.create_filter_widget()
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@ -84,6 +84,14 @@ class MainWindow(QWidget):
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print("Testing sync to server...")
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controller.sync_to_server()
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def Get_All(self):
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try:
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for controller in self.filter_controllers:
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controller.load_from_server()
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print("Successfully loaded all filter data")
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except Exception as e:
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print(f"Error loading filter data: {e}")
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if __name__ == '__main__':
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import sys
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from PySide6.QtWidgets import QApplication
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@ -52,18 +52,22 @@ class ApplicationController(QObject):
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def _on_service_request_complete(self, signal_proxy: SignalProxy):
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"""服务请求完成的槽函数"""
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# 找到对应的controller_id
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print("CCCCCC:signal_proxy.data:", signal_proxy.data)
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controller_id = self._get_controller_id_for_widget(signal_proxy.widget)
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# to do:这个槽函数要实现对信号的分发，但是不执行具体的业务，
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# 每个具体的控制器只用注册对应的信号，然后执行具体的业务
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# print("CCCCCC:signal_proxy.data:", signal_proxy.data)
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# controller_id = self._get_controller_id_for_widget(signal_proxy.widget)
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# 测试用 返回值类似这种格式 用来标机控件
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controller_id = "audio_filter"
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if controller_id:
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# 转发信号
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self.signal_params_updated.emit(ControllerSignalData(
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controller_id=controller_id,
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widget=signal_proxy.widget,
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data=signal_proxy.data
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))
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# # 测试用 返回值类似这种格式 用来标记控件
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# controller_id = "audio_filter"
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# if controller_id:
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# # 转发信号
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# self.signal_params_updated.emit(ControllerSignalData(
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# controller_id=controller_id,
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# widget=signal_proxy.widget,
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# data=signal_proxy.data
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# ))
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pass
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def _get_controller_id_for_widget(self, widget: QObject) -> Optional[str]:
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"""根据widget查找对应的controller_id"""
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@ -16,9 +16,8 @@ from typing import List, Dict, Optional, Any
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from component.widget_filter.Ui_widget import Ui_Widget
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from component.widget_filter.checkbox_header import SCheckBoxHeaderView
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from component.widget_filter.audio_filter_model import AudioFilterModel
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from component.widget_filter.audio_filter_model import AudioFilterModel, FilterParams, FilterType
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from component.widget_filter.Ui_widget import Ui_Widget
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import component.widget_filter.resources
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@ -463,17 +462,17 @@ class AudioFilterWidget(QWidget):
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"""处理参数值变化"""
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if hasattr(self, 'model'):
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try:
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float_value = float(value)
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# float_value = float(value)
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# 更新model中的通道参数
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channel_params = self.model.channel_params
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if param == 'delay':
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channel_params.delay = float_value
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channel_params.delay = value
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elif param == 'volume':
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channel_params.volume = float_value
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channel_params.volume = value
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elif param == 'mix_right':
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channel_params.mix_right = float_value
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channel_params.mix_right = value
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elif param == 'mix_left':
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channel_params.mix_left = float_value
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channel_params.mix_left = value
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self.model.set_channel_params(channel_params)
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except ValueError:
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@ -529,7 +528,7 @@ class AudioFilterWidget(QWidget):
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self.ui.tableWidget.setCellWidget(row, 2, combo)
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# 创建新的滤波器项，使用唯一的默认名称
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self._update_table_row(row, {
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filter_data = {
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'filter_name': f"Filter_{row}", # 使用行号创建唯一名称
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'filter_type': self.filter_types[0],
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'enabled': True,
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@ -537,7 +536,22 @@ class AudioFilterWidget(QWidget):
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'q': 0,
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'gain': 0,
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'slope': 0
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})
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}
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# 更新表格
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self._update_table_row(row, filter_data)
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# 更新model数据
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if hasattr(self, 'model'):
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filter_params = FilterParams(
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filter_type=FilterType[self.filter_types[0]],
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frequency=float(filter_data['freq']),
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q_value=float(filter_data['q']),
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gain=float(filter_data['gain']),
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slope=float(filter_data['slope']),
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enabled=filter_data['enabled']
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)
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self.model.add_filter(filter_params)
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# 发送信号
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self.filter_added.emit(self.filter_types[0])
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@ -713,10 +727,10 @@ class AudioFilterWidget(QWidget):
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# 连接模型信号到视图更新方法
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self.model.dataChanged.connect(self._updateFromModel)
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self.model.filterAdded.connect(self._handleFilterAdded)
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self.model.filterRemoved.connect(self._handleFilterRemoved)
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self.model.filterUpdated.connect(self._handleFilterUpdated)
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self.model.channelParamsChanged.connect(self._handleChannelParamsChanged)
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# self.model.filterAdded.connect(self._handleFilterAdded)
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# self.model.filterRemoved.connect(self._handleFilterRemoved)
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# self.model.filterUpdated.connect(self._handleFilterUpdated)
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# self.model.channelParamsChanged.connect(self._handleChannelParamsChanged)
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# 设置初始数据
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self.set_channel_id(model.channel_id)
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@ -8,6 +8,7 @@ from param_struct_test.params_service import ParamsService
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from component.widget_filter.audio_filter_model import AudioFilterModel
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from component.widget_filter.audio_filter_componet import AudioFilterWidget
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from component.widget_filter.audio_filter_model import FilterParams, FilterType, ChannelParams
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from param_struct_test.params_service import Response
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class AudioControllerState(Enum):
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"""音频控制器状态"""
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@ -78,6 +79,44 @@ class AudioFilterController(QObject):
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# self.widget.filter_added.connect(self._on_widget_filter_added)
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# self.widget.filter_deleted.connect(self._on_widget_filter_deleted)
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# self.widget.filter_enabled_changed.connect(self._on_widget_filter_enabled_changed)
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self.widget.send_params_clicked.connect(self.sync_to_server)
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def _convert_struct_data_to_model(self, struct_data: Dict[str, Any]) -> Dict[str, Any]:
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"""将结构体格式数据转换为模型数据"""
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channel_params = ChannelParams(
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delay=struct_data.get(f'tuning_parameters.delay_parameters[{self.model.channel_id-1}].delay_data', 0.0),
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volume=struct_data.get(f'tuning_parameters.volume_parameters[{self.model.channel_id-1}].vol_data', 0.0),
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mix_right=struct_data.get(f'tuning_parameters.mix_parameters[{self.model.channel_id-1}].mix_right_data', 0.0),
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mix_left=struct_data.get(f'tuning_parameters.mix_parameters[{self.model.channel_id-1}].mix_left_data', 0.0)
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)
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filters = []
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base_idx = (self.model.channel_id - 1) * 20 # 当前通道的均衡器起始索引
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print("channel_id:", self.model.channel_id)
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# 遍历当前通道的20个均衡器单元
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for i in range(20):
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idx = base_idx + i
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fc_key = f'tuning_parameters.eq_parameters[{idx}].fc'
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# 如果找不到该索引的频率参数，说明没有更多的均衡器数据了
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if fc_key not in struct_data:
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break
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filter_params = FilterParams(
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filter_type=FilterType(struct_data.get(f'tuning_parameters.eq_parameters[{idx}].filterType', 0)),
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frequency=struct_data.get(f'tuning_parameters.eq_parameters[{idx}].fc', 0.0),
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q_value=struct_data.get(f'tuning_parameters.eq_parameters[{idx}].q', 0.0),
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gain=struct_data.get(f'tuning_parameters.eq_parameters[{idx}].gain', 0.0),
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slope=struct_data.get(f'tuning_parameters.eq_parameters[{idx}].slope', 0.0)
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)
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filters.append(filter_params)
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return {
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'channel_id': self.model.channel_id,
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'channel_name': self.model.channel_name,
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'channel_params': channel_params,
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'filters': filters
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}
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@Slot(ControllerSignalData)
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def _on_params_updated(self, signal_data: ControllerSignalData):
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@ -87,66 +126,143 @@ class AudioFilterController(QObject):
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try:
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self.state = AudioControllerState.UPDATING
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data = signal_data.data
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# 使用ChannelParams类创建实例
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channel_params = ChannelParams(
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delay=data.get('delay_data1', 0.0),
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volume=data.get('vol_data1', 0.0),
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mix_right=data.get('mix_right_data1', 0.0),
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mix_left=data.get('mix_left_data1', 0.0)
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)
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struct_data = signal_data.data
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# 将结构体数据转换为模型数据
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model_data = self._convert_struct_data_to_model(struct_data)
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# 更新通道参数
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self.model.set_channel_params(channel_params)
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# 清除现有滤波器
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self.model.filters.clear() # 使用filters列表的clear方法替代clear_filters
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# 解析并添加滤波器参数
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# filter_index = 1
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# while True:
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# filter_type_value = data.get(f'filterType1_{filter_index}')
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# if filter_type_value is None:
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# break
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# # 使用FilterParams类创建实例
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# filter_params = FilterParams(
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# filter_type=FilterType(filter_type_value),
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# frequency=data.get(f'fc1_{filter_index}', 0.0),
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# q_value=data.get(f'q1_{filter_index}', 0.0),
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# gain=data.get(f'gain1_{filter_index}', 0.0),
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# slope=data.get(f'slope1_{filter_index}', 0.0)
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# )
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# self.model.add_filter(filter_params)
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# filter_index += 1
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self.model.set_channel_params(model_data['channel_params'])
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print("model_filters:", self.model.filters)
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# 清除现有滤波器并添加新的滤波器
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self.model.filters.clear()
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if model_data['channel_id'] == self.model.channel_id:
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for filter_param in model_data['filters']:
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# self.model.add_filter(filter_param)
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self.model.filters.append(filter_param)
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self.params_synced.emit()
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self.state = AudioControllerState.IDLE
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except Exception as e:
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self.state = AudioControllerState.ERROR
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self.error_occurred.emit(f"Error updating params: {str(e)}")
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self.error_occurred.emit(f"更新参数时发生错误: {str(e)}")
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def _on_params_updated_new(self, res: Response):
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"""处理来自ApplicationController的参数更新信号"""
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try:
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self.state = AudioControllerState.UPDATING
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struct_data = res.data
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# 将结构体数据转换为模型数据
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model_data = self._convert_struct_data_to_model(struct_data)
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# 更新通道参数
|
||||
self.model.set_channel_params(model_data['channel_params'])
|
||||
# 清除现有滤波器
|
||||
self.model.filters.clear()
|
||||
|
||||
if model_data['channel_id'] == self.model.channel_id:
|
||||
for filter_param in model_data['filters']:
|
||||
# 检查滤波器的关键参数是否都为0
|
||||
if not (filter_param.frequency == 0 and
|
||||
filter_param.q_value == 0 and
|
||||
filter_param.gain == 0 and
|
||||
filter_param.slope == 0):
|
||||
self.model.filters.append(filter_param)
|
||||
|
||||
self.widget.setModel(self.model)
|
||||
print("有效的滤波器数量:", len(self.model.filters))
|
||||
|
||||
except Exception as e:
|
||||
self.state = AudioControllerState.ERROR
|
||||
self.error_occurred.emit(f"更新参数时发生错误: {str(e)}")
|
||||
|
||||
def _convert_model_data_to_struct(self, model_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""将模型数据转换为结构体格式"""
|
||||
result = {}
|
||||
channel_id = model_data['channel_id']
|
||||
|
||||
# 添加通道参数
|
||||
channel_params = model_data['channel_params']
|
||||
|
||||
# 混音参数
|
||||
result[f'tuning_parameters.mix_parameters[{channel_id}].ch_n'] = channel_id
|
||||
result[f'tuning_parameters.mix_parameters[{channel_id}].mix_left_data'] = channel_params['mix_left']
|
||||
result[f'tuning_parameters.mix_parameters[{channel_id}].mix_right_data'] = channel_params['mix_right']
|
||||
|
||||
# 延迟参数
|
||||
result[f'tuning_parameters.delay_parameters[{channel_id}].ch_n'] = channel_id
|
||||
result[f'tuning_parameters.delay_parameters[{channel_id}].delay_data'] = channel_params['delay']
|
||||
|
||||
# 音量参数
|
||||
result[f'tuning_parameters.volume_parameters[{channel_id}].ch_n'] = channel_id
|
||||
result[f'tuning_parameters.volume_parameters[{channel_id}].vol_data'] = channel_params['volume']
|
||||
|
||||
# 均衡器参数
|
||||
for i, filter_param in enumerate(model_data['filters']):
|
||||
base_idx = (channel_id) * 20 + i # 每个通道20个均衡器单元
|
||||
result[f'tuning_parameters.eq_parameters[{base_idx}].fc'] = filter_param['frequency']
|
||||
result[f'tuning_parameters.eq_parameters[{base_idx}].q'] = filter_param['q_value']
|
||||
result[f'tuning_parameters.eq_parameters[{base_idx}].gain'] = filter_param['gain']
|
||||
result[f'tuning_parameters.eq_parameters[{base_idx}].slope'] = filter_param['slope']
|
||||
result[f'tuning_parameters.eq_parameters[{base_idx}].filterType'] = filter_param['filter_type'].value
|
||||
|
||||
return result
|
||||
|
||||
def sync_to_server(self):
|
||||
"""同步数据到服务器"""
|
||||
try:
|
||||
self.state = AudioControllerState.UPDATING
|
||||
params = self.model.get_all_data()
|
||||
ServiceManager.instance().params_service.set_params(params)
|
||||
# 转换数据格式
|
||||
struct_params = self._convert_model_data_to_struct(params)
|
||||
print("sync_to_server:", struct_params)
|
||||
ServiceManager.instance().params_service.set_params(self.widget, struct_params)
|
||||
except Exception as e:
|
||||
self.state = AudioControllerState.ERROR
|
||||
self.error_occurred.emit(f"Error syncing to server: {str(e)}")
|
||||
|
||||
|
||||
def load_from_server(self):
|
||||
"""从服务器加载数据"""
|
||||
try:
|
||||
print("BBBBBB:load_from_server")
|
||||
self.state = AudioControllerState.UPDATING
|
||||
ServiceManager.instance().params_service.get_params(self.widget)
|
||||
channel_id = self.model.channel_id
|
||||
|
||||
# 构建需要获取的参数键列表
|
||||
param_keys = []
|
||||
|
||||
# 添加通道基本参数的键
|
||||
param_keys.extend([
|
||||
f'tuning_parameters.mix_parameters[{channel_id}].ch_n',
|
||||
f'tuning_parameters.mix_parameters[{channel_id}].mix_left_data',
|
||||
f'tuning_parameters.mix_parameters[{channel_id}].mix_right_data',
|
||||
f'tuning_parameters.delay_parameters[{channel_id}].ch_n',
|
||||
f'tuning_parameters.delay_parameters[{channel_id}].delay_data',
|
||||
f'tuning_parameters.volume_parameters[{channel_id}].ch_n',
|
||||
f'tuning_parameters.volume_parameters[{channel_id}].vol_data'
|
||||
])
|
||||
|
||||
# 添加当前通道的20个滤波器的所有参数键
|
||||
base_idx = (channel_id) * 20
|
||||
for i in range(20): # 固定生成20个滤波器的参数键
|
||||
idx = base_idx + i
|
||||
param_keys.extend([
|
||||
f'tuning_parameters.eq_parameters[{idx}].fc',
|
||||
f'tuning_parameters.eq_parameters[{idx}].q',
|
||||
f'tuning_parameters.eq_parameters[{idx}].gain',
|
||||
f'tuning_parameters.eq_parameters[{idx}].slope',
|
||||
f'tuning_parameters.eq_parameters[{idx}].filterType'
|
||||
])
|
||||
print(f"aa param_keys:",param_keys)
|
||||
ServiceManager.instance().params_service.get_params(
|
||||
self.widget,
|
||||
param_keys,
|
||||
callback=self._on_params_updated_new
|
||||
)
|
||||
except Exception as e:
|
||||
self.state = AudioControllerState.ERROR
|
||||
self.error_occurred.emit(f"Error loading from server: {str(e)}")
|
||||
self.error_occurred.emit(f"从服务器加载数据时发生错误: {str(e)}")
|
||||
|
||||
# 模型信号处理器
|
||||
def _on_model_data_changed(self):
|
||||
|
||||
@ -88,7 +88,7 @@ class AudioFilterModel(QObject):
|
||||
def add_filter(self, filter_params: FilterParams):
|
||||
"""添加新的滤波器"""
|
||||
self.filters.append(filter_params)
|
||||
self.filterAdded.emit(len(self.filters) - 1)
|
||||
# self.filterAdded.emit(len(self.filters) - 1)
|
||||
self.updateData()
|
||||
|
||||
def remove_filter(self, index: int):
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
644
doc/struct_members.txt
Normal file
644
doc/struct_members.txt
Normal file
@ -0,0 +1,644 @@
|
||||
dataset.audio_mode: offset 0 (int32_t)
|
||||
dataset.send_action: offset 4 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[0].ch_n: offset 8 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[1].ch_n: offset 20 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[2].ch_n: offset 32 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[3].ch_n: offset 44 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[4].ch_n: offset 56 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[5].ch_n: offset 68 (int32_t)
|
||||
dataset.tuning_parameters.mix_parameters[0].mix_left_data: offset 12 (float)
|
||||
dataset.tuning_parameters.mix_parameters[1].mix_left_data: offset 24 (float)
|
||||
dataset.tuning_parameters.mix_parameters[2].mix_left_data: offset 36 (float)
|
||||
dataset.tuning_parameters.mix_parameters[3].mix_left_data: offset 48 (float)
|
||||
dataset.tuning_parameters.mix_parameters[4].mix_left_data: offset 60 (float)
|
||||
dataset.tuning_parameters.mix_parameters[5].mix_left_data: offset 72 (float)
|
||||
dataset.tuning_parameters.mix_parameters[0].mix_right_data: offset 16 (float)
|
||||
dataset.tuning_parameters.mix_parameters[1].mix_right_data: offset 28 (float)
|
||||
dataset.tuning_parameters.mix_parameters[2].mix_right_data: offset 40 (float)
|
||||
dataset.tuning_parameters.mix_parameters[3].mix_right_data: offset 52 (float)
|
||||
dataset.tuning_parameters.mix_parameters[4].mix_right_data: offset 64 (float)
|
||||
dataset.tuning_parameters.mix_parameters[5].mix_right_data: offset 76 (float)
|
||||
dataset.tuning_parameters.eq_parameters[0].fc: offset 80 (float)
|
||||
dataset.tuning_parameters.eq_parameters[1].fc: offset 100 (float)
|
||||
dataset.tuning_parameters.eq_parameters[2].fc: offset 120 (float)
|
||||
dataset.tuning_parameters.eq_parameters[3].fc: offset 140 (float)
|
||||
dataset.tuning_parameters.eq_parameters[4].fc: offset 160 (float)
|
||||
dataset.tuning_parameters.eq_parameters[5].fc: offset 180 (float)
|
||||
dataset.tuning_parameters.eq_parameters[6].fc: offset 200 (float)
|
||||
dataset.tuning_parameters.eq_parameters[7].fc: offset 220 (float)
|
||||
dataset.tuning_parameters.eq_parameters[8].fc: offset 240 (float)
|
||||
dataset.tuning_parameters.eq_parameters[9].fc: offset 260 (float)
|
||||
dataset.tuning_parameters.eq_parameters[10].fc: offset 280 (float)
|
||||
dataset.tuning_parameters.eq_parameters[11].fc: offset 300 (float)
|
||||
dataset.tuning_parameters.eq_parameters[12].fc: offset 320 (float)
|
||||
dataset.tuning_parameters.eq_parameters[13].fc: offset 340 (float)
|
||||
dataset.tuning_parameters.eq_parameters[14].fc: offset 360 (float)
|
||||
dataset.tuning_parameters.eq_parameters[15].fc: offset 380 (float)
|
||||
dataset.tuning_parameters.eq_parameters[16].fc: offset 400 (float)
|
||||
dataset.tuning_parameters.eq_parameters[17].fc: offset 420 (float)
|
||||
dataset.tuning_parameters.eq_parameters[18].fc: offset 440 (float)
|
||||
dataset.tuning_parameters.eq_parameters[19].fc: offset 460 (float)
|
||||
dataset.tuning_parameters.eq_parameters[20].fc: offset 480 (float)
|
||||
dataset.tuning_parameters.eq_parameters[21].fc: offset 500 (float)
|
||||
dataset.tuning_parameters.eq_parameters[22].fc: offset 520 (float)
|
||||
dataset.tuning_parameters.eq_parameters[23].fc: offset 540 (float)
|
||||
dataset.tuning_parameters.eq_parameters[24].fc: offset 560 (float)
|
||||
dataset.tuning_parameters.eq_parameters[25].fc: offset 580 (float)
|
||||
dataset.tuning_parameters.eq_parameters[26].fc: offset 600 (float)
|
||||
dataset.tuning_parameters.eq_parameters[27].fc: offset 620 (float)
|
||||
dataset.tuning_parameters.eq_parameters[28].fc: offset 640 (float)
|
||||
dataset.tuning_parameters.eq_parameters[29].fc: offset 660 (float)
|
||||
dataset.tuning_parameters.eq_parameters[30].fc: offset 680 (float)
|
||||
dataset.tuning_parameters.eq_parameters[31].fc: offset 700 (float)
|
||||
dataset.tuning_parameters.eq_parameters[32].fc: offset 720 (float)
|
||||
dataset.tuning_parameters.eq_parameters[33].fc: offset 740 (float)
|
||||
dataset.tuning_parameters.eq_parameters[34].fc: offset 760 (float)
|
||||
dataset.tuning_parameters.eq_parameters[35].fc: offset 780 (float)
|
||||
dataset.tuning_parameters.eq_parameters[36].fc: offset 800 (float)
|
||||
dataset.tuning_parameters.eq_parameters[37].fc: offset 820 (float)
|
||||
dataset.tuning_parameters.eq_parameters[38].fc: offset 840 (float)
|
||||
dataset.tuning_parameters.eq_parameters[39].fc: offset 860 (float)
|
||||
dataset.tuning_parameters.eq_parameters[40].fc: offset 880 (float)
|
||||
dataset.tuning_parameters.eq_parameters[41].fc: offset 900 (float)
|
||||
dataset.tuning_parameters.eq_parameters[42].fc: offset 920 (float)
|
||||
dataset.tuning_parameters.eq_parameters[43].fc: offset 940 (float)
|
||||
dataset.tuning_parameters.eq_parameters[44].fc: offset 960 (float)
|
||||
dataset.tuning_parameters.eq_parameters[45].fc: offset 980 (float)
|
||||
dataset.tuning_parameters.eq_parameters[46].fc: offset 1000 (float)
|
||||
dataset.tuning_parameters.eq_parameters[47].fc: offset 1020 (float)
|
||||
dataset.tuning_parameters.eq_parameters[48].fc: offset 1040 (float)
|
||||
dataset.tuning_parameters.eq_parameters[49].fc: offset 1060 (float)
|
||||
dataset.tuning_parameters.eq_parameters[50].fc: offset 1080 (float)
|
||||
dataset.tuning_parameters.eq_parameters[51].fc: offset 1100 (float)
|
||||
dataset.tuning_parameters.eq_parameters[52].fc: offset 1120 (float)
|
||||
dataset.tuning_parameters.eq_parameters[53].fc: offset 1140 (float)
|
||||
dataset.tuning_parameters.eq_parameters[54].fc: offset 1160 (float)
|
||||
dataset.tuning_parameters.eq_parameters[55].fc: offset 1180 (float)
|
||||
dataset.tuning_parameters.eq_parameters[56].fc: offset 1200 (float)
|
||||
dataset.tuning_parameters.eq_parameters[57].fc: offset 1220 (float)
|
||||
dataset.tuning_parameters.eq_parameters[58].fc: offset 1240 (float)
|
||||
dataset.tuning_parameters.eq_parameters[59].fc: offset 1260 (float)
|
||||
dataset.tuning_parameters.eq_parameters[60].fc: offset 1280 (float)
|
||||
dataset.tuning_parameters.eq_parameters[61].fc: offset 1300 (float)
|
||||
dataset.tuning_parameters.eq_parameters[62].fc: offset 1320 (float)
|
||||
dataset.tuning_parameters.eq_parameters[63].fc: offset 1340 (float)
|
||||
dataset.tuning_parameters.eq_parameters[64].fc: offset 1360 (float)
|
||||
dataset.tuning_parameters.eq_parameters[65].fc: offset 1380 (float)
|
||||
dataset.tuning_parameters.eq_parameters[66].fc: offset 1400 (float)
|
||||
dataset.tuning_parameters.eq_parameters[67].fc: offset 1420 (float)
|
||||
dataset.tuning_parameters.eq_parameters[68].fc: offset 1440 (float)
|
||||
dataset.tuning_parameters.eq_parameters[69].fc: offset 1460 (float)
|
||||
dataset.tuning_parameters.eq_parameters[70].fc: offset 1480 (float)
|
||||
dataset.tuning_parameters.eq_parameters[71].fc: offset 1500 (float)
|
||||
dataset.tuning_parameters.eq_parameters[72].fc: offset 1520 (float)
|
||||
dataset.tuning_parameters.eq_parameters[73].fc: offset 1540 (float)
|
||||
dataset.tuning_parameters.eq_parameters[74].fc: offset 1560 (float)
|
||||
dataset.tuning_parameters.eq_parameters[75].fc: offset 1580 (float)
|
||||
dataset.tuning_parameters.eq_parameters[76].fc: offset 1600 (float)
|
||||
dataset.tuning_parameters.eq_parameters[77].fc: offset 1620 (float)
|
||||
dataset.tuning_parameters.eq_parameters[78].fc: offset 1640 (float)
|
||||
dataset.tuning_parameters.eq_parameters[79].fc: offset 1660 (float)
|
||||
dataset.tuning_parameters.eq_parameters[80].fc: offset 1680 (float)
|
||||
dataset.tuning_parameters.eq_parameters[81].fc: offset 1700 (float)
|
||||
dataset.tuning_parameters.eq_parameters[82].fc: offset 1720 (float)
|
||||
dataset.tuning_parameters.eq_parameters[83].fc: offset 1740 (float)
|
||||
dataset.tuning_parameters.eq_parameters[84].fc: offset 1760 (float)
|
||||
dataset.tuning_parameters.eq_parameters[85].fc: offset 1780 (float)
|
||||
dataset.tuning_parameters.eq_parameters[86].fc: offset 1800 (float)
|
||||
dataset.tuning_parameters.eq_parameters[87].fc: offset 1820 (float)
|
||||
dataset.tuning_parameters.eq_parameters[88].fc: offset 1840 (float)
|
||||
dataset.tuning_parameters.eq_parameters[89].fc: offset 1860 (float)
|
||||
dataset.tuning_parameters.eq_parameters[90].fc: offset 1880 (float)
|
||||
dataset.tuning_parameters.eq_parameters[91].fc: offset 1900 (float)
|
||||
dataset.tuning_parameters.eq_parameters[92].fc: offset 1920 (float)
|
||||
dataset.tuning_parameters.eq_parameters[93].fc: offset 1940 (float)
|
||||
dataset.tuning_parameters.eq_parameters[94].fc: offset 1960 (float)
|
||||
dataset.tuning_parameters.eq_parameters[95].fc: offset 1980 (float)
|
||||
dataset.tuning_parameters.eq_parameters[96].fc: offset 2000 (float)
|
||||
dataset.tuning_parameters.eq_parameters[97].fc: offset 2020 (float)
|
||||
dataset.tuning_parameters.eq_parameters[98].fc: offset 2040 (float)
|
||||
dataset.tuning_parameters.eq_parameters[99].fc: offset 2060 (float)
|
||||
dataset.tuning_parameters.eq_parameters[100].fc: offset 2080 (float)
|
||||
dataset.tuning_parameters.eq_parameters[101].fc: offset 2100 (float)
|
||||
dataset.tuning_parameters.eq_parameters[102].fc: offset 2120 (float)
|
||||
dataset.tuning_parameters.eq_parameters[103].fc: offset 2140 (float)
|
||||
dataset.tuning_parameters.eq_parameters[104].fc: offset 2160 (float)
|
||||
dataset.tuning_parameters.eq_parameters[105].fc: offset 2180 (float)
|
||||
dataset.tuning_parameters.eq_parameters[106].fc: offset 2200 (float)
|
||||
dataset.tuning_parameters.eq_parameters[107].fc: offset 2220 (float)
|
||||
dataset.tuning_parameters.eq_parameters[108].fc: offset 2240 (float)
|
||||
dataset.tuning_parameters.eq_parameters[109].fc: offset 2260 (float)
|
||||
dataset.tuning_parameters.eq_parameters[110].fc: offset 2280 (float)
|
||||
dataset.tuning_parameters.eq_parameters[111].fc: offset 2300 (float)
|
||||
dataset.tuning_parameters.eq_parameters[112].fc: offset 2320 (float)
|
||||
dataset.tuning_parameters.eq_parameters[113].fc: offset 2340 (float)
|
||||
dataset.tuning_parameters.eq_parameters[114].fc: offset 2360 (float)
|
||||
dataset.tuning_parameters.eq_parameters[115].fc: offset 2380 (float)
|
||||
dataset.tuning_parameters.eq_parameters[116].fc: offset 2400 (float)
|
||||
dataset.tuning_parameters.eq_parameters[117].fc: offset 2420 (float)
|
||||
dataset.tuning_parameters.eq_parameters[118].fc: offset 2440 (float)
|
||||
dataset.tuning_parameters.eq_parameters[119].fc: offset 2460 (float)
|
||||
dataset.tuning_parameters.eq_parameters[0].q: offset 84 (float)
|
||||
dataset.tuning_parameters.eq_parameters[1].q: offset 104 (float)
|
||||
dataset.tuning_parameters.eq_parameters[2].q: offset 124 (float)
|
||||
dataset.tuning_parameters.eq_parameters[3].q: offset 144 (float)
|
||||
dataset.tuning_parameters.eq_parameters[4].q: offset 164 (float)
|
||||
dataset.tuning_parameters.eq_parameters[5].q: offset 184 (float)
|
||||
dataset.tuning_parameters.eq_parameters[6].q: offset 204 (float)
|
||||
dataset.tuning_parameters.eq_parameters[7].q: offset 224 (float)
|
||||
dataset.tuning_parameters.eq_parameters[8].q: offset 244 (float)
|
||||
dataset.tuning_parameters.eq_parameters[9].q: offset 264 (float)
|
||||
dataset.tuning_parameters.eq_parameters[10].q: offset 284 (float)
|
||||
dataset.tuning_parameters.eq_parameters[11].q: offset 304 (float)
|
||||
dataset.tuning_parameters.eq_parameters[12].q: offset 324 (float)
|
||||
dataset.tuning_parameters.eq_parameters[13].q: offset 344 (float)
|
||||
dataset.tuning_parameters.eq_parameters[14].q: offset 364 (float)
|
||||
dataset.tuning_parameters.eq_parameters[15].q: offset 384 (float)
|
||||
dataset.tuning_parameters.eq_parameters[16].q: offset 404 (float)
|
||||
dataset.tuning_parameters.eq_parameters[17].q: offset 424 (float)
|
||||
dataset.tuning_parameters.eq_parameters[18].q: offset 444 (float)
|
||||
dataset.tuning_parameters.eq_parameters[19].q: offset 464 (float)
|
||||
dataset.tuning_parameters.eq_parameters[20].q: offset 484 (float)
|
||||
dataset.tuning_parameters.eq_parameters[21].q: offset 504 (float)
|
||||
dataset.tuning_parameters.eq_parameters[22].q: offset 524 (float)
|
||||
dataset.tuning_parameters.eq_parameters[23].q: offset 544 (float)
|
||||
dataset.tuning_parameters.eq_parameters[24].q: offset 564 (float)
|
||||
dataset.tuning_parameters.eq_parameters[25].q: offset 584 (float)
|
||||
dataset.tuning_parameters.eq_parameters[26].q: offset 604 (float)
|
||||
dataset.tuning_parameters.eq_parameters[27].q: offset 624 (float)
|
||||
dataset.tuning_parameters.eq_parameters[28].q: offset 644 (float)
|
||||
dataset.tuning_parameters.eq_parameters[29].q: offset 664 (float)
|
||||
dataset.tuning_parameters.eq_parameters[30].q: offset 684 (float)
|
||||
dataset.tuning_parameters.eq_parameters[31].q: offset 704 (float)
|
||||
dataset.tuning_parameters.eq_parameters[32].q: offset 724 (float)
|
||||
dataset.tuning_parameters.eq_parameters[33].q: offset 744 (float)
|
||||
dataset.tuning_parameters.eq_parameters[34].q: offset 764 (float)
|
||||
dataset.tuning_parameters.eq_parameters[35].q: offset 784 (float)
|
||||
dataset.tuning_parameters.eq_parameters[36].q: offset 804 (float)
|
||||
dataset.tuning_parameters.eq_parameters[37].q: offset 824 (float)
|
||||
dataset.tuning_parameters.eq_parameters[38].q: offset 844 (float)
|
||||
dataset.tuning_parameters.eq_parameters[39].q: offset 864 (float)
|
||||
dataset.tuning_parameters.eq_parameters[40].q: offset 884 (float)
|
||||
dataset.tuning_parameters.eq_parameters[41].q: offset 904 (float)
|
||||
dataset.tuning_parameters.eq_parameters[42].q: offset 924 (float)
|
||||
dataset.tuning_parameters.eq_parameters[43].q: offset 944 (float)
|
||||
dataset.tuning_parameters.eq_parameters[44].q: offset 964 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[61].q: offset 1304 (float)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[63].q: offset 1344 (float)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[65].q: offset 1384 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[24].gain: offset 568 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[29].gain: offset 668 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[34].gain: offset 768 (float)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[36].gain: offset 808 (float)
|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[39].gain: offset 868 (float)
|
||||
dataset.tuning_parameters.eq_parameters[40].gain: offset 888 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[54].gain: offset 1168 (float)
|
||||
dataset.tuning_parameters.eq_parameters[55].gain: offset 1188 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[63].gain: offset 1348 (float)
|
||||
dataset.tuning_parameters.eq_parameters[64].gain: offset 1368 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[72].gain: offset 1528 (float)
|
||||
dataset.tuning_parameters.eq_parameters[73].gain: offset 1548 (float)
|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[76].gain: offset 1608 (float)
|
||||
dataset.tuning_parameters.eq_parameters[77].gain: offset 1628 (float)
|
||||
dataset.tuning_parameters.eq_parameters[78].gain: offset 1648 (float)
|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[81].gain: offset 1708 (float)
|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[84].gain: offset 1768 (float)
|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[88].gain: offset 1848 (float)
|
||||
dataset.tuning_parameters.eq_parameters[89].gain: offset 1868 (float)
|
||||
dataset.tuning_parameters.eq_parameters[90].gain: offset 1888 (float)
|
||||
dataset.tuning_parameters.eq_parameters[91].gain: offset 1908 (float)
|
||||
dataset.tuning_parameters.eq_parameters[92].gain: offset 1928 (float)
|
||||
dataset.tuning_parameters.eq_parameters[93].gain: offset 1948 (float)
|
||||
dataset.tuning_parameters.eq_parameters[94].gain: offset 1968 (float)
|
||||
dataset.tuning_parameters.eq_parameters[95].gain: offset 1988 (float)
|
||||
dataset.tuning_parameters.eq_parameters[96].gain: offset 2008 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[115].gain: offset 2388 (float)
|
||||
dataset.tuning_parameters.eq_parameters[116].gain: offset 2408 (float)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[118].gain: offset 2448 (float)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[6].slope: offset 212 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[7].slope: offset 232 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[8].slope: offset 252 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[9].slope: offset 272 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[10].slope: offset 292 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[11].slope: offset 312 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[12].slope: offset 332 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[13].slope: offset 352 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[14].slope: offset 372 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[15].slope: offset 392 (int32_t)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[17].slope: offset 432 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[18].slope: offset 452 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[19].slope: offset 472 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[20].slope: offset 492 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[21].slope: offset 512 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[22].slope: offset 532 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[23].slope: offset 552 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[24].slope: offset 572 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[25].slope: offset 592 (int32_t)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[27].slope: offset 632 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[28].slope: offset 652 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[29].slope: offset 672 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[30].slope: offset 692 (int32_t)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[32].slope: offset 732 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[33].slope: offset 752 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[34].slope: offset 772 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[35].slope: offset 792 (int32_t)
|
||||
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|
||||
dataset.tuning_parameters.eq_parameters[37].slope: offset 832 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[38].slope: offset 852 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[39].slope: offset 872 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[40].slope: offset 892 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[41].slope: offset 912 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[42].slope: offset 932 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[43].slope: offset 952 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[44].slope: offset 972 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[45].slope: offset 992 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[46].slope: offset 1012 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[47].slope: offset 1032 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[48].slope: offset 1052 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[49].slope: offset 1072 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[50].slope: offset 1092 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[51].slope: offset 1112 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[52].slope: offset 1132 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[53].slope: offset 1152 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[54].slope: offset 1172 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[55].slope: offset 1192 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[56].slope: offset 1212 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[57].slope: offset 1232 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[58].slope: offset 1252 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[59].slope: offset 1272 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[60].slope: offset 1292 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[61].slope: offset 1312 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[62].slope: offset 1332 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[63].slope: offset 1352 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[64].slope: offset 1372 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[65].slope: offset 1392 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[66].slope: offset 1412 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[67].slope: offset 1432 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[68].slope: offset 1452 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[69].slope: offset 1472 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[70].slope: offset 1492 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[71].slope: offset 1512 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[72].slope: offset 1532 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[73].slope: offset 1552 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[74].slope: offset 1572 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[75].slope: offset 1592 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[76].slope: offset 1612 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[77].slope: offset 1632 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[78].slope: offset 1652 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[79].slope: offset 1672 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[80].slope: offset 1692 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[81].slope: offset 1712 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[82].slope: offset 1732 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[83].slope: offset 1752 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[84].slope: offset 1772 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[85].slope: offset 1792 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[86].slope: offset 1812 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[87].slope: offset 1832 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[88].slope: offset 1852 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[89].slope: offset 1872 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[90].slope: offset 1892 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[91].slope: offset 1912 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[92].slope: offset 1932 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[93].slope: offset 1952 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[94].slope: offset 1972 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[95].slope: offset 1992 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[96].slope: offset 2012 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[97].slope: offset 2032 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[98].slope: offset 2052 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[99].slope: offset 2072 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[100].slope: offset 2092 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[101].slope: offset 2112 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[102].slope: offset 2132 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[103].slope: offset 2152 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[104].slope: offset 2172 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[105].slope: offset 2192 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[106].slope: offset 2212 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[107].slope: offset 2232 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[108].slope: offset 2252 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[109].slope: offset 2272 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[110].slope: offset 2292 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[111].slope: offset 2312 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[112].slope: offset 2332 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[113].slope: offset 2352 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[114].slope: offset 2372 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[115].slope: offset 2392 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[116].slope: offset 2412 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[117].slope: offset 2432 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[118].slope: offset 2452 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[119].slope: offset 2472 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[0].filterType: offset 96 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[1].filterType: offset 116 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[2].filterType: offset 136 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[3].filterType: offset 156 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[4].filterType: offset 176 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[5].filterType: offset 196 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[6].filterType: offset 216 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[7].filterType: offset 236 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[8].filterType: offset 256 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[9].filterType: offset 276 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[10].filterType: offset 296 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[11].filterType: offset 316 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[12].filterType: offset 336 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[13].filterType: offset 356 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[14].filterType: offset 376 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[15].filterType: offset 396 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[16].filterType: offset 416 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[17].filterType: offset 436 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[18].filterType: offset 456 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[19].filterType: offset 476 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[20].filterType: offset 496 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[21].filterType: offset 516 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[22].filterType: offset 536 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[23].filterType: offset 556 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[24].filterType: offset 576 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[25].filterType: offset 596 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[26].filterType: offset 616 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[27].filterType: offset 636 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[28].filterType: offset 656 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[29].filterType: offset 676 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[30].filterType: offset 696 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[31].filterType: offset 716 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[32].filterType: offset 736 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[33].filterType: offset 756 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[34].filterType: offset 776 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[35].filterType: offset 796 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[36].filterType: offset 816 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[37].filterType: offset 836 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[38].filterType: offset 856 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[39].filterType: offset 876 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[40].filterType: offset 896 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[41].filterType: offset 916 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[42].filterType: offset 936 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[43].filterType: offset 956 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[44].filterType: offset 976 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[45].filterType: offset 996 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[46].filterType: offset 1016 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[47].filterType: offset 1036 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[48].filterType: offset 1056 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[49].filterType: offset 1076 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[50].filterType: offset 1096 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[51].filterType: offset 1116 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[52].filterType: offset 1136 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[53].filterType: offset 1156 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[54].filterType: offset 1176 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[55].filterType: offset 1196 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[56].filterType: offset 1216 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[57].filterType: offset 1236 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[58].filterType: offset 1256 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[59].filterType: offset 1276 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[60].filterType: offset 1296 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[61].filterType: offset 1316 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[62].filterType: offset 1336 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[63].filterType: offset 1356 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[64].filterType: offset 1376 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[65].filterType: offset 1396 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[66].filterType: offset 1416 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[67].filterType: offset 1436 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[68].filterType: offset 1456 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[69].filterType: offset 1476 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[70].filterType: offset 1496 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[71].filterType: offset 1516 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[72].filterType: offset 1536 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[73].filterType: offset 1556 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[74].filterType: offset 1576 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[75].filterType: offset 1596 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[76].filterType: offset 1616 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[77].filterType: offset 1636 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[78].filterType: offset 1656 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[79].filterType: offset 1676 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[80].filterType: offset 1696 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[81].filterType: offset 1716 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[82].filterType: offset 1736 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[83].filterType: offset 1756 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[84].filterType: offset 1776 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[85].filterType: offset 1796 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[86].filterType: offset 1816 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[87].filterType: offset 1836 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[88].filterType: offset 1856 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[89].filterType: offset 1876 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[90].filterType: offset 1896 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[91].filterType: offset 1916 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[92].filterType: offset 1936 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[93].filterType: offset 1956 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[94].filterType: offset 1976 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[95].filterType: offset 1996 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[96].filterType: offset 2016 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[97].filterType: offset 2036 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[98].filterType: offset 2056 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[99].filterType: offset 2076 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[100].filterType: offset 2096 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[101].filterType: offset 2116 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[102].filterType: offset 2136 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[103].filterType: offset 2156 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[104].filterType: offset 2176 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[105].filterType: offset 2196 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[106].filterType: offset 2216 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[107].filterType: offset 2236 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[108].filterType: offset 2256 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[109].filterType: offset 2276 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[110].filterType: offset 2296 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[111].filterType: offset 2316 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[112].filterType: offset 2336 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[113].filterType: offset 2356 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[114].filterType: offset 2376 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[115].filterType: offset 2396 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[116].filterType: offset 2416 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[117].filterType: offset 2436 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[118].filterType: offset 2456 (int32_t)
|
||||
dataset.tuning_parameters.eq_parameters[119].filterType: offset 2476 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[0].ch_n: offset 2500 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[1].ch_n: offset 2508 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[2].ch_n: offset 2516 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[3].ch_n: offset 2524 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[4].ch_n: offset 2532 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[5].ch_n: offset 2540 (int32_t)
|
||||
dataset.tuning_parameters.delay_parameters[0].delay_data: offset 2504 (float)
|
||||
dataset.tuning_parameters.delay_parameters[1].delay_data: offset 2512 (float)
|
||||
dataset.tuning_parameters.delay_parameters[2].delay_data: offset 2520 (float)
|
||||
dataset.tuning_parameters.delay_parameters[3].delay_data: offset 2528 (float)
|
||||
dataset.tuning_parameters.delay_parameters[4].delay_data: offset 2536 (float)
|
||||
dataset.tuning_parameters.delay_parameters[5].delay_data: offset 2544 (float)
|
||||
dataset.tuning_parameters.volume_parameters[0].ch_n: offset 2548 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[1].ch_n: offset 2556 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[2].ch_n: offset 2564 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[3].ch_n: offset 2572 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[4].ch_n: offset 2580 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[5].ch_n: offset 2588 (int32_t)
|
||||
dataset.tuning_parameters.volume_parameters[0].vol_data: offset 2552 (float)
|
||||
dataset.tuning_parameters.volume_parameters[1].vol_data: offset 2560 (float)
|
||||
dataset.tuning_parameters.volume_parameters[2].vol_data: offset 2568 (float)
|
||||
dataset.tuning_parameters.volume_parameters[3].vol_data: offset 2576 (float)
|
||||
dataset.tuning_parameters.volume_parameters[4].vol_data: offset 2584 (float)
|
||||
dataset.tuning_parameters.volume_parameters[5].vol_data: offset 2592 (float)
|
||||
@ -1 +1 @@
|
||||
Subproject commit 032f99bc1eea81ba0f2c6608114940f31510a3fc
|
||||
Subproject commit 5d3fc10c5ddbadfb5b8cd4825355fbabc9a42438
|
||||
BIN
音效控件UI/plugins/FilterButton.exe
Normal file
BIN
音效控件UI/plugins/FilterButton.exe
Normal file
Binary file not shown.
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Reference in New Issue
Block a user