IQ Plot - Python

import sys

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.widgets import Slider

from scipy.signal import butter, filtfilt, convolve, ellip, cheby1

import struct

from tqdm import tqdm

import matplotlib

matplotlib.use('Qt5Agg')

import matplotlib.pyplot as plt

import mplcursors

import argparse

def read_iq_data(filename, start_pos, length=None):

    with open(filename, 'rb') as f:

        raw_data = f.read()

    num_samples = len(raw_data) // 8

    if length is not None:

        end_pos = min(start_pos + length, num_samples)

    else:

        end_pos = num_samples

    data = np.frombuffer(raw_data, dtype=np.float32).view(np.complex64)[start_pos:end_pos]

    #data = np.frombuffer(raw_data, dtype=np.float32).view(np.complex64)

    return data

updating_xlims = False

updating_ylims = False

def plot_data(data, downsample_rate=1, vbw=1, filter_type='butter', filter_order=5, filter_cutoff=None):

    def butter_lowpass(cutoff, fs, order=5):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = butter(order, normal_cutoff, btype='low', analog=False)

        return b, a

    def ellip_lowpass(cutoff, fs, order=5, rp=0.5, rs=40):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = ellip(order, rp, rs, normal_cutoff, btype='low', analog=False)

        return b, a

    def cheby_lowpass(cutoff, fs, order=5, rp=0.5):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = cheby1(order, rp, normal_cutoff, btype='low', analog=False)

        return b, a

    def lowpass_filter(data, cutoff, fs, order=5, filter_type='butter'):

        if filter_type == 'butter':

            b, a = butter_lowpass(cutoff, fs, order=order)

        elif filter_type == 'elliptic':

            b, a = ellip_lowpass(cutoff, fs, order=order)

        elif filter_type == 'cheby':

            b, a = cheby_lowpass(cutoff, fs, order=order)

        else:

            raise ValueError("Invalid filter type. Choose 'butter', 'elliptic', or 'cheby'.")

        y = filtfilt(b, a, data)

        return y

    def moving_average(data, window_size):

        window = np.ones(window_size) / window_size

        return convolve(data, window, mode='same')

    if downsample_rate > 1:

        cutoff_frequency = filter_cutoff #0.45 * (0.5 / downsample_rate)

        filtered_data = lowpass_filter(data, cutoff_frequency, 1, filter_order, filter_type)

        downsampled_data = filtered_data[::downsample_rate]

    else:

        downsampled_data = data

    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)

    ax1.plot(downsampled_data.real)

    ax1.set_title('Real Part', fontsize=10)

    ax1.set_xticklabels([])

    ax1.set_yticklabels([])

    ax2.plot(downsampled_data.imag)

    ax2.set_title('Imaginary Part', fontsize=10)

    ax2.set_xticklabels([])

    ax2.set_yticklabels([])

    # Compute the magnitude of the downsampled data

    magnitude = np.absolute(downsampled_data)

    ax3.plot(magnitude)

    ax3.set_title('Magnitude', fontsize=10)

    ax3.set_xticklabels([])

    ax3.set_yticklabels([])

    spectrum = np.fft.fft(downsampled_data)

    centered_spectrum = np.fft.fftshift(spectrum)

    power_mw = np.abs(centered_spectrum)**2

    centered_spectrum_dbm = 10 * np.log10(power_mw)

    if vbw > 1:

        vbw_filtered_spectrum = moving_average(centered_spectrum_dbm, vbw)

    else:

        vbw_filtered_spectrum = centered_spectrum_dbm

    vbw_filtered_spectrum_normalized = vbw_filtered_spectrum - np.max(vbw_filtered_spectrum)

    ax4.plot(vbw_filtered_spectrum_normalized)

    ax4.set_title('Spectrum (FFT) with VBW', fontsize=10)

    ax4.set_xticklabels([])

    ax4.set_yticklabels([])

    plt.tight_layout()

    plt.subplots_adjust(top=0.95)

    plt.subplots_adjust(hspace = 0.3)

    def on_xlims_change(event_axes):

        global updating_xlims

        if not updating_xlims:

            updating_xlims = True

            if event_axes == ax1:

                ax2.set_xlim(ax1.get_xlim())

                ax3.set_xlim(ax1.get_xlim())

            elif event_axes == ax2:

                ax1.set_xlim(ax2.get_xlim())

                ax3.set_xlim(ax2.get_xlim())

            elif event_axes == ax3:

                ax1.set_xlim(ax3.get_xlim())

                ax2.set_xlim(ax3.get_xlim())

            updating_xlims = False

            update_spectrum_plot()

    def on_ylims_change(event_axes):

        global updating_ylims

        if not updating_ylims:

            updating_ylims = True

            if event_axes == ax1:

                ax2.set_ylim(ax1.get_ylim())

                ax3.set_ylim(ax1.get_ylim())

            elif event_axes == ax2:

                ax1.set_ylim(ax2.get_ylim())

                ax3.set_ylim(ax2.get_ylim())

            elif event_axes == ax3:

                ax1.set_ylim(ax3.get_ylim())

                ax2.set_ylim(ax3.get_ylim())

            updating_ylims = False

            update_spectrum_plot()

    def update_spectrum_plot():

        x1_min, x1_max = map(int, ax1.get_xlim())

        y1_min, y1_max = map(int, ax1.get_ylim())

        x2_min, x2_max = map(int, ax2.get_xlim())

        y2_min, y2_max = map(int, ax2.get_ylim())

        x3_min, x3_max = map(int, ax3.get_xlim())  # add this line for ax3 x limits

        y3_min, y3_max = map(int, ax3.get_ylim())  # add this line for ax3 y limits

        x1_min = max(x1_min, 0)

        x2_min = max(x2_min, 0)

        x3_min = max(x3_min, 0)  # make sure x3_min is non-negative

        if x1_max - x1_min < 1:

            x1_max = x1_min + 1

        if x2_max - x2_min < 1:

            x2_max = x2_min + 1

        if x3_max - x3_min < 1:  # ensure x3 range is at least 1

            x3_max = x3_min + 1

        visible_data_real = downsampled_data.real[x1_min:x1_max]

        visible_data_imag = downsampled_data.imag[x2_min:x2_max]

        visible_data_magnitude = np.abs(downsampled_data)[x3_min:x3_max]  # visible magnitude data from ax3

        visible_data = visible_data_real + 1j * visible_data_imag

        spectrum_data = np.fft.fft(visible_data)

        centered_spectrum_data = np.fft.fftshift(spectrum_data)

        power_mw = np.abs(centered_spectrum_data)**2

        centered_spectrum_dbm = 10 * np.log10(power_mw)

        if vbw > 1:

            vbw_filtered_spectrum = moving_average(centered_spectrum_dbm, vbw)

        else:

            vbw_filtered_spectrum = centered_spectrum_dbm

        vbw_filtered_spectrum_normalized = vbw_filtered_spectrum - np.max(vbw_filtered_spectrum)

        ax4.clear()

        ax4.plot(vbw_filtered_spectrum_normalized)

        ax4.set_title("Spectrum Data", fontsize=10)

        ax4.set_xlabel("Frequency", fontsize=10)

        ax4.set_ylabel("Amplitude", fontsize=10)

        ax4.set_xticklabels([])

        ax4.set_yticklabels([])

        fig.canvas.draw_idle()

    ax1.callbacks.connect('xlim_changed', on_xlims_change)

    #ax1.callbacks.connect('ylim_changed', on_ylims_change)

    ax2.callbacks.connect('xlim_changed', on_xlims_change)

    #ax2.callbacks.connect('ylim_changed', on_ylims_change)

    ax3.callbacks.connect('xlim_changed', on_xlims_change)

    #ax3.callbacks.connect('ylim_changed', on_ylims_change)

    plt.show()

def parse_arguments():

    parser = argparse.ArgumentParser(description='Plot IQ data from a file.')

    parser.add_argument('datafile', type=str, help='Path to the IQ data file')

    parser.add_argument('--data-pos-unit', type=str, choices=['sample', 'percent'], default='sample', help='Unit for start-pos and length: sample or percent')

    parser.add_argument('--start-pos', type=float, default=0, help='Start position of the I/Q data pair to read')

    parser.add_argument('--length', type=float, default=None, help='Length of the I/Q data pair to read')

    parser.add_argument('--downsample-rate', type=int, default=1, help='Factor to downsample the data by')

    parser.add_argument('--vbw', type=int, default=1000, help='Video Bandwidth (VBW)')

    parser.add_argument('--filter-type', type=str, default='cheby', choices=['butter', 'elliptic', 'cheby'], help='Type of low-pass filter to use')

    parser.add_argument('--filter-order', type=int, default=5, help='Order of the low-pass filter')

    parser.add_argument('--cutoff-frequency', type=float, default=None, help='Cutoff frequency of the low-pass filter')

    # Add a double dash to allow optional arguments to be in any order

    parser.add_argument('--', dest='double_dash', action='store_true', help=argparse.SUPPRESS)

    args = parser.parse_args()

    # Handle double dash by re-parsing the command line and re-assigning the arguments

    if args.double_dash:

        parser = argparse.ArgumentParser(description='Plot IQ data from a file.')

        parser.add_argument('datafile', type=str, help='Path to the IQ data file')

        parser.add_argument('--data-pos-unit', type=str, choices=['sample', 'percent'], default='sample', help='Unit for start-pos and length: sample or percent')

        parser.add_argument('--start-pos', type=float, default=0, help='Start position of the I/Q data pair to read')

        parser.add_argument('--length', type=float, default=None, help='Length of the I/Q data pair to read')

        parser.add_argument('--downsample-rate', type=int, default=1, help='Factor to downsample the data by')

        parser.add_argument('--vbw', type=int, default=1000, help='Video Bandwidth (VBW)')

        parser.add_argument('--filter-type', type=str, default='cheby', choices=['butter', 'elliptic', 'cheby'], help='Type of low-pass filter to use')

        parser.add_argument('--filter-order', type=int, default=5, help='Order of the low-pass filter')

        parser.add_argument('--cutoff-frequency', type=float, default=None, help='Cutoff frequency of the low-pass filter')

        args = parser.parse_args()

    return args

def main():

    args = parse_arguments()

    if args.data_pos_unit == 'percent':

        with open(args.datafile, 'rb') as f:

            total_samples = len(f.read()) // 8

        start_pos = int(args.start_pos / 100 * total_samples)

        if args.length is not None:

            length = int(args.length / 100 * total_samples)

        else:

            length = None

    else:

        start_pos = int(args.start_pos)

        length = int(args.length) if args.length is not None else None

    data = read_iq_data(args.datafile, start_pos, length)

    if args.cutoff_frequency is not None:

        filter_cutoff = args.cutoff_frequency

    else:

        filter_cutoff = 0.45 * (0.5 / args.downsample_rate)

    plot_data(data, downsample_rate=args.downsample_rate, vbw=args.vbw, filter_type=args.filter_type, filter_order=args.filter_order, filter_cutoff=filter_cutoff)

if __name__ == '__main__':

    main()

import sys

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.widgets import Slider

from scipy.signal import butter, filtfilt, convolve, ellip, cheby1

import struct

from tqdm import tqdm

import matplotlib

matplotlib.use('Qt5Agg')

import matplotlib.pyplot as plt

import mplcursors

import argparse

# This function reads IQ data from a binary file and returns it as a numpy array of complex numbers.

#

# Parameters:

#    filename (str): The name of the file to read the data from.

#    start_pos (int): The starting position in the data array from where to read the data.

#    length (int, optional): The number of samples to read. If not specified, the function reads data till the end of the file.

#

# Returns:

#    numpy.ndarray: A numpy array of complex numbers representing the IQ data read from the file.

#

# The function first reads the entire file into a byte array. It then calculates the total number of samples in the file  (each sample is a complex number consisting of 8 bytes - 4 for the real part and 4

# for the imaginary part).

#

# If a length is specified, it calculates the end position as the minimum of start_pos + length and num_samples.  If length is not specified, it sets the end position to num_samples, effectively reading

# till the end of the file.

#

# It then converts the byte array into a numpy array of float32, views it as a numpy array of complex64, and slices it  from start_pos to end_pos. The resulting sliced array is returned.

#

def read_iq_data(filename, start_pos, length=None):

    with open(filename, 'rb') as f:

        raw_data = f.read()

    num_samples = len(raw_data) // 8

    if length is not None:

        end_pos = min(start_pos + length, num_samples)

    else:

        end_pos = num_samples

    data = np.frombuffer(raw_data, dtype=np.float32).view(np.complex64)[start_pos:end_pos]

    #data = np.frombuffer(raw_data, dtype=np.float32).view(np.complex64)

    return data

# This function plots the real part, imaginary part, magnitude, and spectrum of the given data.

#

# The data is first low-pass filtered and downsampled if a downsample rate greater than 1 is specified.  The type of low-pass filter (Butterworth, elliptic, or Chebyshev) can be specified.

#

# The function then plots the real part, imaginary part, and magnitude of the downsampled data in separate subplots. It also computes the spectrum of the data, applies a video bandwidth (VBW)

# filter if specified, and plots the spectrum in a fourth subplot.

#

# The function also sets up callbacks to update the x-limits and y-limits of the plots when they are changed in any of the subplots. This allows the user to zoom in on a particular part of the data

# in onesubplot and see the corresponding part of the data in the other subplots.

#

# Parameters:

#    data (numpy.ndarray): The data to plot. This should be a 1D numpy array of complex numbers.

#    downsample_rate (int, optional): The rate at which to downsample the data. If not specified, the data is not downsampled.

#    vbw (int, optional): The video bandwidth (VBW) to apply when computing the spectrum. If not specified, no VBW is applied.

#    filter_type (str, optional): The type of low-pass filter to use when downsampling. Can be 'butter' (Butterworth), 'elliptic', or 'cheby' (Chebyshev). If not specified, a Butterworth filter is used.

#    filter_order (int, optional): The order of the low-pass filter. If not specified, a 5th order filter is used.

#    filter_cutoff (float, optional): The cutoff frequency of the low-pass filter. If not specified, the cutoff frequency is set to 0.45 times the Nyquist frequency.

#

# Returns:

#    None

#

updating_xlims = False

updating_ylims = False

def plot_data(data, downsample_rate=1, vbw=1, filter_type='butter', filter_order=5, filter_cutoff=None):

    def butter_lowpass(cutoff, fs, order=5):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = butter(order, normal_cutoff, btype='low', analog=False)

        return b, a

    def ellip_lowpass(cutoff, fs, order=5, rp=0.5, rs=40):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = ellip(order, rp, rs, normal_cutoff, btype='low', analog=False)

        return b, a

    def cheby_lowpass(cutoff, fs, order=5, rp=0.5):

        nyq = 0.5 * fs

        normal_cutoff = cutoff / nyq

        b, a = cheby1(order, rp, normal_cutoff, btype='low', analog=False)

        return b, a

    def lowpass_filter(data, cutoff, fs, order=5, filter_type='butter'):

        if filter_type == 'butter':

            b, a = butter_lowpass(cutoff, fs, order=order)

        elif filter_type == 'elliptic':

            b, a = ellip_lowpass(cutoff, fs, order=order)

        elif filter_type == 'cheby':

            b, a = cheby_lowpass(cutoff, fs, order=order)

        else:

            raise ValueError("Invalid filter type. Choose 'butter', 'elliptic', or 'cheby'.")

        y = filtfilt(b, a, data)

        return y

    def moving_average(data, window_size):

        window = np.ones(window_size) / window_size

        return convolve(data, window, mode='same')

    if downsample_rate > 1:

        cutoff_frequency = filter_cutoff #0.45 * (0.5 / downsample_rate)

        filtered_data = lowpass_filter(data, cutoff_frequency, 1, filter_order, filter_type)

        downsampled_data = filtered_data[::downsample_rate]

    else:

        downsampled_data = data

    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)

    ax1.plot(downsampled_data.real)

    ax1.set_title('Real Part', fontsize=10)

    ax1.set_xticklabels([])

    ax1.set_yticklabels([])

    ax2.plot(downsampled_data.imag)

    ax2.set_title('Imaginary Part', fontsize=10)

    ax2.set_xticklabels([])

    ax2.set_yticklabels([])

    # Compute the magnitude of the downsampled data

    magnitude = np.absolute(downsampled_data)

    ax3.plot(magnitude)

    ax3.set_title('Magnitude', fontsize=10)

    ax3.set_xticklabels([])

    ax3.set_yticklabels([])

    spectrum = np.fft.fft(downsampled_data)

    centered_spectrum = np.fft.fftshift(spectrum)

    power_mw = np.abs(centered_spectrum)**2

    centered_spectrum_dbm = 10 * np.log10(power_mw)

    if vbw > 1:

        vbw_filtered_spectrum = moving_average(centered_spectrum_dbm, vbw)

    else:

        vbw_filtered_spectrum = centered_spectrum_dbm

    vbw_filtered_spectrum_normalized = vbw_filtered_spectrum - np.max(vbw_filtered_spectrum)

    ax4.plot(vbw_filtered_spectrum_normalized)

    ax4.set_title('Spectrum (FFT) with VBW', fontsize=10)

    ax4.set_xticklabels([])

    ax4.set_yticklabels([])

    plt.tight_layout()

    plt.subplots_adjust(top=0.95)

    plt.subplots_adjust(hspace = 0.3)

    def on_xlims_change(event_axes):

        global updating_xlims

        if not updating_xlims:

            updating_xlims = True

            if event_axes == ax1:

                ax2.set_xlim(ax1.get_xlim())

                ax3.set_xlim(ax1.get_xlim())

            elif event_axes == ax2:

                ax1.set_xlim(ax2.get_xlim())

                ax3.set_xlim(ax2.get_xlim())

            elif event_axes == ax3:

                ax1.set_xlim(ax3.get_xlim())

                ax2.set_xlim(ax3.get_xlim())

            updating_xlims = False

            update_spectrum_plot()

    def on_ylims_change(event_axes):

        global updating_ylims

        if not updating_ylims:

            updating_ylims = True

            if event_axes == ax1:

                ax2.set_ylim(ax1.get_ylim())

                ax3.set_ylim(ax1.get_ylim())

            elif event_axes == ax2:

                ax1.set_ylim(ax2.get_ylim())

                ax3.set_ylim(ax2.get_ylim())

            elif event_axes == ax3:

                ax1.set_ylim(ax3.get_ylim())

                ax2.set_ylim(ax3.get_ylim())

            updating_ylims = False

            update_spectrum_plot()

    def update_spectrum_plot():

        x1_min, x1_max = map(int, ax1.get_xlim())

        y1_min, y1_max = map(int, ax1.get_ylim())

        x2_min, x2_max = map(int, ax2.get_xlim())

        y2_min, y2_max = map(int, ax2.get_ylim())

        x3_min, x3_max = map(int, ax3.get_xlim())  # add this line for ax3 x limits

        y3_min, y3_max = map(int, ax3.get_ylim())  # add this line for ax3 y limits

        x1_min = max(x1_min, 0)

        x2_min = max(x2_min, 0)

        x3_min = max(x3_min, 0)  # make sure x3_min is non-negative

        if x1_max - x1_min < 1:

            x1_max = x1_min + 1

        if x2_max - x2_min < 1:

            x2_max = x2_min + 1

        if x3_max - x3_min < 1:  # ensure x3 range is at least 1

            x3_max = x3_min + 1

        visible_data_real = downsampled_data.real[x1_min:x1_max]

        visible_data_imag = downsampled_data.imag[x2_min:x2_max]

        visible_data_magnitude = np.abs(downsampled_data)[x3_min:x3_max]  # visible magnitude data from ax3

        visible_data = visible_data_real + 1j * visible_data_imag

        spectrum_data = np.fft.fft(visible_data)

        centered_spectrum_data = np.fft.fftshift(spectrum_data)

        power_mw = np.abs(centered_spectrum_data)**2

        centered_spectrum_dbm = 10 * np.log10(power_mw)

        if vbw > 1:

            vbw_filtered_spectrum = moving_average(centered_spectrum_dbm, vbw)

        else:

            vbw_filtered_spectrum = centered_spectrum_dbm

        vbw_filtered_spectrum_normalized = vbw_filtered_spectrum - np.max(vbw_filtered_spectrum)

        ax4.clear()

        ax4.plot(vbw_filtered_spectrum_normalized)

        ax4.set_title("Spectrum Data", fontsize=10)

        ax4.set_xlabel("Frequency", fontsize=10)

        ax4.set_ylabel("Amplitude", fontsize=10)

        ax4.set_xticklabels([])

        ax4.set_yticklabels([])

        fig.canvas.draw_idle()

    ax1.callbacks.connect('xlim_changed', on_xlims_change)

    #ax1.callbacks.connect('ylim_changed', on_ylims_change)

    ax2.callbacks.connect('xlim_changed', on_xlims_change)

    #ax2.callbacks.connect('ylim_changed', on_ylims_change)

    ax3.callbacks.connect('xlim_changed', on_xlims_change)

    #ax3.callbacks.connect('ylim_changed', on_ylims_change)

    plt.show()

# This function parses command-line arguments for plotting IQ data from a file.

#

# It uses the argparse module to define and parse the command-line arguments. It supports the following arguments:

#    datafile (str): The path to the IQ data file.

#    data-pos-unit (str, optional): The unit for start-pos and length. Can be 'sample' or 'percent'. Default is 'sample'.

#    start-pos (float, optional): The start position of the I/Q data pair to read. Default is 0.

#    length (float, optional): The length of the I/Q data pair to read. Default is None, which means read to the end of the file.

#    downsample-rate (int, optional): The factor to downsample the data by. Default is 1, which means no downsampling.

#    vbw (int, optional): The Video Bandwidth (VBW). Default is 1000.

#    filter-type (str, optional): The type of low-pass filter to use. Can be 'butter' (Butterworth), 'elliptic', or 'cheby' (Chebyshev). Default is 'cheby'.

#    filter-order (int, optional): The order of the low-pass filter. Default is 5.

#    cutoff-frequency (float, optional): The cutoff frequency of the low-pass filter. Default is None, which means the cutoff frequency is set to 0.45 times the Nyquist frequency.

#   

# The function also supports a special '--' argument that allows optional arguments to be in any order. If '--' is specified, the function re-parses the command line and re-assigns the arguments.

#   

# Returns:

#    argparse.Namespace: An object that contains the parsed arguments as attributes.

#

def parse_arguments():

    parser = argparse.ArgumentParser(description='Plot IQ data from a file.')

    parser.add_argument('datafile', type=str, help='Path to the IQ data file')

    parser.add_argument('--data-pos-unit', type=str, choices=['sample', 'percent'], default='sample', help='Unit for start-pos and length: sample or percent')

    parser.add_argument('--start-pos', type=float, default=0, help='Start position of the I/Q data pair to read')

    parser.add_argument('--length', type=float, default=None, help='Length of the I/Q data pair to read')

    parser.add_argument('--downsample-rate', type=int, default=1, help='Factor to downsample the data by')

    parser.add_argument('--vbw', type=int, default=1000, help='Video Bandwidth (VBW)')

    parser.add_argument('--filter-type', type=str, default='cheby', choices=['butter', 'elliptic', 'cheby'], help='Type of low-pass filter to use')

    parser.add_argument('--filter-order', type=int, default=5, help='Order of the low-pass filter')

    parser.add_argument('--cutoff-frequency', type=float, default=None, help='Cutoff frequency of the low-pass filter')

    # Add a double dash to allow optional arguments to be in any order

    parser.add_argument('--', dest='double_dash', action='store_true', help=argparse.SUPPRESS)

    args = parser.parse_args()

    # Handle double dash by re-parsing the command line and re-assigning the arguments

    if args.double_dash:

        parser = argparse.ArgumentParser(description='Plot IQ data from a file.')

        parser.add_argument('datafile', type=str, help='Path to the IQ data file')

        parser.add_argument('--data-pos-unit', type=str, choices=['sample', 'percent'], default='sample', help='Unit for start-pos and length: sample or percent')

        parser.add_argument('--start-pos', type=float, default=0, help='Start position of the I/Q data pair to read')

        parser.add_argument('--length', type=float, default=None, help='Length of the I/Q data pair to read')

        parser.add_argument('--downsample-rate', type=int, default=1, help='Factor to downsample the data by')

        parser.add_argument('--vbw', type=int, default=1000, help='Video Bandwidth (VBW)')

        parser.add_argument('--filter-type', type=str, default='cheby', choices=['butter', 'elliptic', 'cheby'], help='Type of low-pass filter to use')

        parser.add_argument('--filter-order', type=int, default=5, help='Order of the low-pass filter')

        parser.add_argument('--cutoff-frequency', type=float, default=None, help='Cutoff frequency of the low-pass filter')

        args = parser.parse_args()

    return args

# This function is the main entry point(main routine) of the program.

#

# It first parses the command-line arguments using the parse_arguments function.

# If the data-pos-unit argument is 'percent', it calculates the start position and length in samples based on the total number of samples in the file.

# If the data-pos-unit argument is not 'percent', it uses the start-pos and length arguments directly as the start position and length in samples.

# It then reads the specified portion of the IQ data from the file using the read_iq_data function.

# If a cutoff frequency is specified, it uses that as the cutoff frequency for the low-pass filter.

# If no cutoff frequency is specified, it calculates the cutoff frequency based on the downsample rate.

# Finally, it plots the data using the plot_data function, passing the appropriate arguments for downsampling, VBW, filter type, filter order, and filter cutoff.

#

def main():

    args = parse_arguments()

    if args.data_pos_unit == 'percent':

        with open(args.datafile, 'rb') as f:

            total_samples = len(f.read()) // 8

        start_pos = int(args.start_pos / 100 * total_samples)

        if args.length is not None:

            length = int(args.length / 100 * total_samples)

        else:

            length = None

    else:

        start_pos = int(args.start_pos)

        length = int(args.length) if args.length is not None else None

    data = read_iq_data(args.datafile, start_pos, length)

    if args.cutoff_frequency is not None:

        filter_cutoff = args.cutoff_frequency

    else:

        filter_cutoff = 0.45 * (0.5 / args.downsample_rate)

    plot_data(data, downsample_rate=args.downsample_rate, vbw=args.vbw, filter_type=args.filter_type, filter_order=args.filter_order, filter_cutoff=filter_cutoff)

if __name__ == '__main__':

    main()