GNU Radio使用Python Block实现模块运行时间间隔获取

文章目录

  • 前言
  • [一、timestamp_sender 模块](#一、timestamp_sender 模块)
  • [二、timestamp_receiver 模块](#二、timestamp_receiver 模块)
  • 三、测试

前言

GNU Radio 中没有实现测量两个模块之间的时间测量模块,本文记录一下通过 python block 制作一个很简单的测时 block。


一、timestamp_sender 模块

使用 python block 做一个发送端时间戳记录模块,并添加下面的代码:

python 复制代码
"""
Embedded Python Blocks:

Each time this file is saved, GRC will instantiate the first class it finds
to get ports and parameters of your block. The arguments to __init__  will
be the parameters. All of them are required to have default values!
"""

import numpy as np
from gnuradio import gr
import time
import numpy as np

class timestamp_sender(gr.sync_block):  # other base classes are basic_block, decim_block, interp_block
    """Embedded Python Block example - a simple multiply const"""

    def __init__(self):  # only default arguments here
        """arguments to this function show up as parameters in GRC"""
        gr.sync_block.__init__(
            self,
            name="timestamp_sender",   # will show up in GRC
            in_sig=None,
            out_sig=[np.float32]
        )
        self.kk = 1;
        
        # if an attribute with the same name as a parameter is found,
        # a callback is registered (properties work, too).

    def work(self, input_items, output_items):
        # Record the current time
        start = np.float32(time.perf_counter())
        
        # Output data and the current timestamp (here using a simple value for demonstration)
        output_items[0][:] = [start for _ in output_items[0]]
        
        if self.kk == 1:
            self.kk = 2;
            print(f"output_items[0][:] = {output_items[0][:]}");
        
        return len(output_items[0])

二、timestamp_receiver 模块

使用 python block 做一个接收端时间戳记录模块,并添加下面的代码:

python 复制代码
"""
Embedded Python Blocks:

Each time this file is saved, GRC will instantiate the first class it finds
to get ports and parameters of your block. The arguments to __init__  will
be the parameters. All of them are required to have default values!
"""

import numpy as np
from gnuradio import gr
import numpy as np  # 导入NumPy库
import time

class timestamp_receiver(gr.sync_block):  # other base classes are basic_block, decim_block, interp_block
    """Embedded Python Block example - a simple multiply const"""

    def __init__(self):  # only default arguments here
        """arguments to this function show up as parameters in GRC"""
        gr.sync_block.__init__(
            self,
            name="timestamp_receiver",   # will show up in GRC
            in_sig=[np.float32],
            out_sig=None
        )
        # if an attribute with the same name as a parameter is found,
        # a callback is registered (properties work, too).
        self.kk = 1

    def work(self, input_items, output_items):
        for item in input_items[0]:
            if item == 0:
                continue
            if self.kk == 1:
                self.kk = 2
                end = np.float32(time.perf_counter())
                print(f"input_items[0] = {input_items[0]}")
                print(f"Received at {end}, interval since sent: {(end - item) * 1000000} Microsecond")

        return len(input_items[0])

三、测试

按照下图将 block 进行连接:

采样率 32KHz,延时 320 * 5 = 160000 个采样点,大约 5s 的时间

打印信息:

bash 复制代码
Generating: '/home/gnep/ofdm_usrp/test.py'

Executing: /usr/bin/python3 -u /home/gnep/ofdm_usrp/test.py

Press Enter to quit: output_items[0][:] = [7809.57 7809.57 7809.57 ... 7809.57 7809.57 7809.57]
item = 7809.56982421875
input_items[0] = [   0.      0.      0.   ... 7809.57 7809.57 7809.57]
Received at 7814.44189453125, interval since sent: 4872070.3125 Microsecond

可以看到打印信息为 4872070.3125 ,大约为 5s 时间


我的qq:2442391036,欢迎交流!


相关推荐
2401_897930061 小时前
tensorflow常用使用场景
人工智能·python·tensorflow
酷飞飞3 小时前
错误是ModuleNotFoundError: No module named ‘pip‘解决“找不到 pip”
人工智能·python·pip
点云SLAM4 小时前
PyTorch 中.backward() 详解使用
人工智能·pytorch·python·深度学习·算法·机器学习·机器人
B1118521Y464 小时前
flask的使用
后端·python·flask
Learn Beyond Limits5 小时前
Transfer Learning|迁移学习
人工智能·python·深度学习·神经网络·机器学习·ai·吴恩达
love530love6 小时前
【保姆级教程】阿里 Wan2.1-T2V-14B 模型本地部署全流程:从环境配置到视频生成(附避坑指南)
人工智能·windows·python·开源·大模型·github·音视频
He1955017 小时前
Go初级之十:错误处理与程序健壮性
开发语言·python·golang
和鲸社区8 小时前
《斯坦福CS336》作业1开源,从0手搓大模型|代码复现+免环境配置
人工智能·python·深度学习·计算机视觉·语言模型·自然语言处理·nlp
豌豆花下猫8 小时前
Python 潮流周刊#118:Python 异步为何不够流行?(摘要)
后端·python·ai
THMAIL8 小时前
深度学习从入门到精通 - LSTM与GRU深度剖析:破解长序列记忆遗忘困境
人工智能·python·深度学习·算法·机器学习·逻辑回归·lstm