Added view folder and view methods in Recording

src/ria_toolkit_oss/datatypes/recording.py

@@ -448,6 +448,60 @@ class Recording:
         else:
             raise ValueError(f"Key {key} is protected and cannot be modified or removed.")
 
+    def view(self, output_path: Optional[str] = "images/signal.png", **kwargs) -> None:
+        """Create a plot of various signal visualizations as a PNG image.
+
+        :param output_path: The output image path. Defaults to "images/signal.png".
+        :type output_path: str, optional
+        :param kwargs: Keyword arguments passed on to utils.view.view_sig.
+        :type: dict of keyword arguments
+
+        **Examples:**
+
+        Create a recording and view it as a plot in a .png image:
+
+        >>> import numpy
+        >>> from utils.data import Recording
+
+        >>> samples = numpy.ones(10000, dtype=numpy.complex64)
+        >>> metadata = {
+        >>>     "sample_rate": 1e6,
+        >>>     "center_frequency": 2.44e9,
+        >>> }
+
+        >>> recording = Recording(data=samples, metadata=metadata)
+        >>> recording.view()
+        """
+        from ria_toolkit_oss.view.view_signal import view_sig
+
+        view_sig(recording=self, output_path=output_path, **kwargs)
+
+    def simple_view(self, **kwargs) -> None:
+        """Create a plot of various signal visualizations as a PNG or SVG image.
+
+        :param kwargs: Keyword arguments passed on to utils.view.view_signal_simple.create_plots.
+        :type: dict of keyword arguments
+
+        **Examples:**
+
+        Create a recording and view it as a plot in a .png image:
+
+        >>> import numpy
+        >>> from utils.data import Recording
+
+        >>> samples = numpy.ones(10000, dtype=numpy.complex64)
+        >>> metadata = {
+        >>>     "sample_rate": 1e6,
+        >>>     "center_frequency": 2.44e9,
+        >>> }
+
+        >>> recording = Recording(data=samples, metadata=metadata)
+        >>> recording.simple_view()
+        """
+        from ria_toolkit_oss.view.view_signal_simple import view_simple_sig
+
+        view_simple_sig(recording=self, **kwargs)
+
     def to_sigmf(
         self, filename: Optional[str] = None, path: Optional[os.PathLike | str] = None, overwrite: bool = False
     ) -> None:

src/ria_toolkit_oss/view/tools.py

@@ -0,0 +1,57 @@
+import pathlib
+
+MAX_PLOT_POINTS = 100_000
+COLORS = {
+    "primary": "#6366f1",
+    "secondary": "#8b5cf6",
+    "accent": "#06b6d4",
+    "dark": "#1e293b",
+    "light": "#f8fafc",
+    "text": "#334155",
+    "muted": "#64748b",
+    "success": "#10b981",
+    "warning": "#f59e0b",
+    "error": "#ef4444",
+    "purple": "#8b5cf6",
+    "magenta": "#d946ef",
+}
+
+
+def decimate(x, max_points=MAX_PLOT_POINTS):
+    if len(x) <= max_points:
+        return x
+    step = len(x) // max_points
+    return x[::step]
+
+
+def extract_metadata_fields(metadata):
+    sample_rate = next((v for k, v in metadata.items() if "sample_rate" in k), 1)
+    center_freq = next((v for k, v in metadata.items() if "center_freq" in k), 0)
+    sdr = next((v for k, v in metadata.items() if "sdr" in k), "Unknown")
+    return sample_rate, center_freq, sdr
+
+
+def set_path(output_path):
+    split_path = output_path.split("/")
+
+    if len(split_path) == 1:
+        folder = "images"
+        file = split_path[0]
+    elif len(split_path) > 2:
+        file = split_path[-1]
+        folder = "/".join(split_path[:-1])
+    else:
+        folder, file = split_path
+
+    split_file = file.split(".")
+    if len(split_file) == 2:
+        extension = split_file[1]
+    else:
+        extension = "no extension"
+    if extension != "png" and extension != "svg":
+        print(f"{extension} not supported, saving as .png.")
+        extension = "png"
+        file = file + ".png"
+
+    pathlib.Path(folder).mkdir(parents=True, exist_ok=True)
+    return "/".join([folder, file]), extension

src/ria_toolkit_oss/view/view_signal.py

@@ -0,0 +1,257 @@
+import os
+import textwrap
+from typing import Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import gridspec
+from PIL import Image
+from scipy.fft import fft, fftshift
+from scipy.signal import spectrogram
+
+from ria_toolkit_oss.datatypes.recording import Recording
+from ria_toolkit_oss.view.tools import (
+    COLORS,
+    decimate,
+    extract_metadata_fields,
+    set_path,
+)
+
+
+def get_fft_size(plot_length):
+    if plot_length < 2000:
+        return int(64)
+    elif plot_length < 10000:
+        return int(256)
+    elif plot_length < 1000000:
+        return int(1024)
+    else:
+        return int(2048)
+
+
+def set_spines(ax, spines):
+    if not spines:
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+        ax.spines["bottom"].set_visible(False)
+        ax.spines["left"].set_visible(False)
+
+
+def view_sig(
+    recording: Recording,
+    output_path: Optional[str] = "images/signal.png",
+    title: Optional[str] = "Signal Plot",
+    dpi: Optional[int] = 250,
+    plot_length: Optional[int] = None,
+    plot_spectrogram: Optional[bool] = True,
+    iq: Optional[bool] = True,
+    frequency: Optional[bool] = True,
+    constellation: Optional[bool] = True,
+    metadata: Optional[bool] = True,
+    logo: Optional[bool] = True,
+    dark: Optional[bool] = True,
+    spines: Optional[bool] = False,
+    title_fontsize: Optional[int] = 40,
+    subtitle_fontsize: Optional[int] = 20,
+) -> None:
+    """
+    Create a plot of various signal visualizations as a png image.
+
+    :param recording: The recording object to plot.
+    :type recording: Recording
+    :param output_path: The output image path. Defaults to "images/signal.png"
+    :type output_path: str, optional
+    :param title: The display title. Defaults to "Signal Plot"
+    :type title: str, optional
+    :param dpi: The dots per inch resolution. Defaults to 250
+    :type dpi: int, optional
+    :param plot_length: The number of samples to plot, default is the whole recording. Defaults to None
+    :type plot_length: int, optional
+    :param plot_spectrogram: Display the spectrogram. Defaults to True
+    :type plot_spectrogram: bool, optional
+    :param iq: Display the iq sample plot. Defaults to True
+    :type iq: bool, optional
+    :param frequency: Display the fft of the recording. Defaults to True
+    :type frequency: bool, optional
+    :param constellation: Display the constellation plot. Defaults to True
+    :type constellation: bool, optional
+    :param metadata: Display the metadata text. Defaults to True
+    :type metadata: bool, optional
+    :param logo: Display the Qoherent logo. Defaults to True
+    :type logo: bool, optional
+    :param dark: Use dark mode. Defaults to True
+    :type dark: bool, optional
+    :param spines: Display spines (bounding lines) around plots. Defaults to False
+    :type spines: bool, optional
+    :param title_fontsize: The font size of the main title text. Defaults to 40
+    :type title_fontsize: int, optional
+    :param subtitle_fontsize: The fontsize of the subplot titles. Defaults to 20
+    :type subtitle_fontsize: int, optional
+
+     **Examples:**
+
+    .. todo:: Usage examples coming soon.
+    """
+
+    complex_signal = recording.data[0]
+    sample_rate, center_frequency, _ = extract_metadata_fields(recording.metadata)
+
+    subplot_height = 2 * (plot_spectrogram + iq + frequency) + 3 * (constellation or metadata or logo)
+    subplot_width = max((constellation + metadata or 1), logo * 3)
+
+    if dark:
+        plt.style.use("dark_background")
+        logo_path = os.path.dirname(__file__) + "/graphics/Qoherent-logo-white-transparent.png"
+    else:
+        plt.style.use("default")
+        logo_path = os.path.dirname(__file__) + "/graphics/Qoherent-logo-black-transparent.png"
+
+    if plot_length is None:
+        plot_length = len(recording.data[0])
+
+    # Plot preparation
+    fig = plt.figure(figsize=(14, 12))
+    fig.suptitle(title, fontsize=title_fontsize)
+    gs = gridspec.GridSpec(subplot_height, subplot_width)
+
+    plot_y_indx = 0
+    plot_x_indx = 0
+
+    if plot_spectrogram:
+        spec_ax = plt.subplot(gs[plot_y_indx : plot_y_indx + 2, :])
+        plot_y_indx = plot_y_indx + 2
+        fft_size = get_fft_size(plot_length=plot_length)
+
+        f, t_spec, Sxx = spectrogram(
+            complex_signal[:plot_length],
+            fs=sample_rate,
+            nperseg=fft_size,
+            noverlap=fft_size // 8,
+            mode="magnitude",
+            return_onesided=False,
+        )
+
+        # shift frequencies so zero is centered
+        Sxx = np.fft.fftshift(Sxx, axes=0)
+        f = np.fft.fftshift(f) - sample_rate / 2 + center_frequency
+
+        spec_ax.imshow(
+            10 * np.log10(Sxx + 1e-12),
+            aspect="auto",
+            origin="lower",
+            extent=[t_spec[0], t_spec[-1], f[0], f[-1]],
+            cmap="twilight",
+        )
+
+        set_spines(spec_ax, spines)
+        spec_ax.set_title("Spectrogram", loc="center", fontsize=subtitle_fontsize)
+        spec_ax.set_ylabel("Frequency (Hz)")
+        spec_ax.set_xlabel("Time (s)")
+
+    if iq:
+        iq_ax = plt.subplot(gs[plot_y_indx : plot_y_indx + 2, :])
+        plot_y_indx = plot_y_indx + 2
+
+        plot_iq = decimate(complex_signal[:plot_length])
+        t = np.arange(len(plot_iq)) / sample_rate * (len(complex_signal[:plot_length]) / len(plot_iq))
+
+        iq_ax.plot(t, plot_iq.real, color=COLORS["purple"], linewidth=0.6, alpha=0.8, label="I")
+        iq_ax.plot(t, plot_iq.imag, color=COLORS["magenta"], linewidth=0.6, alpha=0.8, label="Q")
+        iq_ax.grid(False)
+
+        iq_ax.set_ylabel("Amplitude")
+        iq_ax.set_xlim([min(t), max(t)])
+        iq_ax.set_xlabel("Time (s)")
+        iq_ax.set_title("IQ Sample Plot", fontsize=subtitle_fontsize)
+        set_spines(iq_ax, spines)
+
+    if frequency:
+        freq_ax = plt.subplot(gs[plot_y_indx : plot_y_indx + 2, :])
+        plot_y_indx = plot_y_indx + 2
+
+        epsilon = 1e-10
+        spectrum = np.abs(fftshift(fft(complex_signal[0:plot_length])))
+        freqs = (
+            np.linspace(-1 * (sample_rate / 2), (sample_rate / 2), len(complex_signal[0:plot_length]))
+            + center_frequency
+        )
+
+        # Use semi-log for the y-axis
+        freq_ax.semilogy(freqs, spectrum + epsilon, color=COLORS["accent"], linewidth=0.8)
+        freq_ax.set_xlabel("Frequency")
+        freq_ax.set_ylabel("Magnitude")
+        freq_ax.set_title("Frequency Spectrum", fontsize=subtitle_fontsize)
+        set_spines(freq_ax, spines)
+
+    if constellation:
+        const_ax = plt.subplot(gs[plot_y_indx:, plot_x_indx])
+        plot_x_indx = plot_x_indx + 1
+        plot_const = decimate(complex_signal[:plot_length], 50_000)
+        const_ax.scatter(plot_const.real, plot_const.imag, c=COLORS["purple"], s=1, linewidths=0.1)
+        dimension = max(abs(complex_signal)) * 1.1
+        const_ax.set_xlim([-1 * dimension, dimension])
+        const_ax.set_ylim([-1 * dimension, dimension])
+        const_ax.set_xlabel("In-phase (I)")
+        const_ax.set_ylabel("Quadrature (Q)")
+        const_ax.set_title("Constellation", fontsize=subtitle_fontsize)
+        const_ax.set_aspect("equal")
+
+        if not spines:
+            const_ax.spines["top"].set_visible(False)
+            const_ax.spines["right"].set_visible(False)
+            const_ax.spines["bottom"].set_visible(False)
+            const_ax.spines["left"].set_visible(False)
+
+    # metadata text box
+    if metadata:
+        meta_ax = plt.subplot(gs[plot_y_indx:, plot_x_indx])
+        plot_x_indx = plot_x_indx + 1
+        metadata_text = "\n".join(
+            [
+                f"{key}: {textwrap.shorten(str(value), width=80, placeholder='...')}"
+                for key, value in recording.metadata.items()
+            ]
+        )
+
+        meta_ax.text(
+            0.05,
+            0.95,
+            metadata_text,
+            fontsize=10,
+            va="top",
+            ha="left",
+            bbox=dict(facecolor="none", alpha=0.5, edgecolor="none"),
+        )
+        meta_ax.set_title("Metadata", fontsize=subtitle_fontsize)
+        # Remove the tick labels
+        meta_ax.xaxis.set_ticklabels([])  # Remove x-axis tick labels
+        meta_ax.yaxis.set_ticklabels([])  # Remove y-axis tick labels
+        meta_ax.set_xticks([])
+        meta_ax.set_yticks([])
+        set_spines(meta_ax, spines)
+
+    if logo and os.path.isfile(logo_path):
+        logo_ax = plt.subplot(gs[plot_y_indx:, 2])
+        plot_x_indx = plot_x_indx + 1
+        logo_ax.axis("off")
+
+        try:
+            image = Image.open(logo_path)  # Open the PNG image using PIL
+            logo_ax.imshow(image)
+
+        except FileNotFoundError:
+            print(f"Warning, {logo_path} not found.")
+
+    fig.subplots_adjust(
+        left=0.1,  # Left margin
+        right=0.9,  # Right margin
+        top=0.9,  # Top margin
+        bottom=0.1,  # Bottom margin
+        wspace=0.4,  # Horizontal space between subplots
+        hspace=2.5,  # Vertical space between subplots
+    )
+
+    # save path handling
+    output_path, _ = set_path(output_path=output_path)
+    plt.savefig(output_path, dpi=dpi)
+    print(f"Saved signal plot to {output_path}")

src/ria_toolkit_oss/view/view_signal_simple.py

@@ -0,0 +1,304 @@
+"""Shared plotting primitives for signal visualization."""
+
+from __future__ import annotations
+
+from typing import Optional
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.fft import fft, fftshift
+from scipy.signal.windows import hann
+
+from ria_toolkit_oss.datatypes.recording import Recording
+from ria_toolkit_oss.view.tools import (
+    COLORS,
+    decimate,
+    extract_metadata_fields,
+    set_path,
+)
+
+
+def _get_nfft_size(signal, fast_mode):
+    if len(signal) < 1000:
+        nfft = 128
+    elif len(signal) < 10_000:
+        nfft = 256
+    elif len(signal) < 100_000:
+        nfft = 512
+    elif len(signal) < 1_000_000:
+        nfft = 1024
+    else:
+        nfft = 2048
+
+    if fast_mode:
+        nfft = min(nfft, 512)
+    overlap = nfft // 8 if fast_mode else nfft // 4
+    return nfft, overlap
+
+
+def _get_plot_samples(signal, fast_mode, slow_max, fast_max):
+    max_samples = fast_max if fast_mode else slow_max
+    if len(signal) > max_samples:
+        start_idx = len(signal) // 2 - max_samples // 2
+        return signal[start_idx : start_idx + max_samples]
+    else:
+        return signal
+
+
+def _set_dpi(fast_mode, labels_mode, extension):
+    if fast_mode:
+        dpi = 75
+    elif labels_mode:
+        dpi = 200
+    else:
+        dpi = 150
+    return dpi if extension == "png" else None
+
+
+def setup_style(*, labels_mode: bool = False, compact_mode: bool = False) -> None:
+    """Configure matplotlib with the signal-testbed styling."""
+
+    plt.style.use("dark_background")
+
+    if compact_mode:
+        base_font = 8
+        title_font = 10
+        label_font = 8
+    elif labels_mode:
+        base_font = 12
+        title_font = 16
+        label_font = 14
+    else:
+        base_font = 10
+        title_font = 12
+        label_font = 10
+
+    matplotlib.rcParams.update(
+        {
+            "figure.facecolor": "#0f172a",
+            "axes.facecolor": "#1e293b",
+            "axes.edgecolor": COLORS["muted"],
+            "axes.labelcolor": COLORS["light"],
+            "text.color": COLORS["light"],
+            "xtick.color": COLORS["muted"],
+            "ytick.color": COLORS["muted"],
+            "grid.color": COLORS["muted"],
+            "grid.alpha": 0.3,
+            "font.size": base_font,
+            "axes.titlesize": title_font,
+            "axes.labelsize": label_font,
+            "figure.titlesize": title_font + 2,
+            "legend.frameon": False,
+            "legend.facecolor": "none",
+            "xtick.labelsize": base_font,
+            "ytick.labelsize": base_font,
+        }
+    )
+
+
+def detect_constellation_symbols(signal: np.ndarray, method: str = "differential") -> np.ndarray:
+    """Heuristic symbol detector used for constellation highlighting."""
+
+    if len(signal) < 100:
+        return np.ones(len(signal), dtype=bool)
+
+    if method == "differential":
+        di = np.diff(signal.imag)
+        dq = np.diff(signal.real)
+        derivative_magnitude = np.sqrt(di**2 + dq**2)
+        derivative_magnitude = np.append(derivative_magnitude, 0)
+        threshold = np.percentile(derivative_magnitude, 15)
+        return derivative_magnitude < threshold
+
+    if method == "amplitude":
+        amplitude = np.abs(signal)
+        amplitude_change = np.abs(np.diff(amplitude))
+        amplitude_change = np.append(amplitude_change, 0)
+        threshold = np.percentile(amplitude_change, 20)
+        return amplitude_change < threshold
+
+    if method == "phase":
+        phase = np.angle(signal)
+        phase_diff = np.diff(np.unwrap(phase))
+        phase_diff = np.append(phase_diff, 0)
+        threshold = np.percentile(np.abs(phase_diff), 20)
+        return np.abs(phase_diff) < threshold
+
+    if method == "combined":
+        diff_stable = detect_constellation_symbols(signal, "differential")
+        amp_stable = detect_constellation_symbols(signal, "amplitude")
+        phase_stable = detect_constellation_symbols(signal, "phase")
+        stability_count = diff_stable.astype(int) + amp_stable.astype(int) + phase_stable.astype(int)
+        return stability_count >= 2
+
+    raise ValueError(f"Unknown method: {method}")
+
+
+def view_simple_sig(
+    recording: Recording,
+    output_path: Optional[str] = "images/signal.png",
+    saveplot: Optional[bool] = True,
+    fast_mode: Optional[bool] = False,
+    compact_mode: Optional[bool] = False,
+    horizontal_mode: Optional[bool] = False,
+    constellation_mode: Optional[bool] = False,
+    labels_mode: Optional[bool] = False,
+    slice: Optional[tuple] = None,
+    title: Optional[str] = "Signal",
+):
+    """Render the multi-panel viewer used by the CLI."""
+
+    signal = recording.data[0]
+    sample_rate_hz, center_freq_hz, sdr = extract_metadata_fields(recording.metadata)
+
+    setup_style(labels_mode=labels_mode, compact_mode=compact_mode)
+
+    if slice:
+        start_idx, end_idx = slice
+        signal = signal[start_idx:end_idx]
+        print(f"Using slice: samples {start_idx} to {end_idx} ({len(signal):,} samples)")
+
+    max_display_pixels = 100_000 if fast_mode else 250_000
+    display_signal = decimate(signal, max_display_pixels) if len(signal) > max_display_pixels else signal
+    spec_signal = signal
+
+    if compact_mode:
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), gridspec_kw={"height_ratios": [1, 5]})
+        show_title = False
+        show_labels = False
+        ax_constellation = ax_psd = None
+    elif horizontal_mode:
+        if constellation_mode:
+            fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))
+            ax_constellation = ax3
+        else:
+            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
+            ax_constellation = None
+        show_title = True
+        show_labels = labels_mode
+        ax_psd = None
+    else:
+        if constellation_mode:
+            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+            ax_constellation, ax_psd = ax3, ax4
+        else:
+            fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10))
+            ax_constellation = ax_psd = None
+        show_title = True
+        show_labels = labels_mode
+
+    if show_title:
+        fig.suptitle(title, fontsize=16, color=COLORS["light"], y=0.96)
+    fig.patch.set_facecolor("#0f172a")
+
+    total_duration_s = len(signal) / sample_rate_hz if sample_rate_hz else 0.0
+    t_s = np.linspace(0, total_duration_s, len(display_signal)) if len(display_signal) else np.array([])
+
+    ax1.plot(t_s, display_signal.real, color=COLORS["purple"], linewidth=0.8, alpha=0.8, label="I")
+    ax1.plot(t_s, display_signal.imag, color=COLORS["magenta"], linewidth=0.8, alpha=0.8, label="Q")
+    ax1.set_xlim(0, total_duration_s)
+    ax1.grid(True, alpha=0.3)
+
+    nfft, overlap = _get_nfft_size(signal=signal, fast_mode=fast_mode)
+
+    _, freqs, _, _ = ax2.specgram(
+        spec_signal,
+        NFFT=nfft,
+        Fc=center_freq_hz,
+        Fs=sample_rate_hz,
+        noverlap=overlap,
+        cmap="twilight",
+    )
+
+    ax2.set_ylim(center_freq_hz - sample_rate_hz / 2, center_freq_hz + sample_rate_hz / 2)
+    ax2.set_xlim(0, total_duration_s)
+
+    if show_labels:
+        if horizontal_mode:
+            ax1.set_xlabel("Time (s)")
+        else:
+            ax2.set_xlabel("Time (s)")
+
+        ax1.set_ylabel("Amplitude")
+        ax1.set_title(f"Time Series - {sdr} SDR")
+        ax1.legend(loc="upper right")
+
+        ax2.set_ylabel("Frequency (Hz)")
+        ax2.set_title(f"Spectrogram - {center_freq_hz / 1e6:.1f} MHz ± {sample_rate_hz / 2e6:.1f} MHz")
+        yticks = ax2.get_yticks()
+        ax2.set_yticklabels([f"{y / 1e6:.1f}" for y in yticks])
+    elif not compact_mode:
+        ax1.set_title("Time Series")
+        ax1.legend(loc="upper right", fontsize=8)
+
+        ax2.set_title("Spectrogram")
+
+    if ax_constellation is not None:
+        constellation_samples = _get_plot_samples(signal=signal, fast_mode=fast_mode, slow_max=50_000, fast_max=20_000)
+        method = "differential" if fast_mode else "combined"
+        stable_points = detect_constellation_symbols(constellation_samples, method=method)
+
+        ax_constellation.scatter(
+            constellation_samples.real[~stable_points],
+            constellation_samples.imag[~stable_points],
+            c=COLORS["muted"],
+            s=0.5,
+            alpha=0.2,
+        )
+        ax_constellation.scatter(
+            constellation_samples.real[stable_points],
+            constellation_samples.imag[stable_points],
+            c=COLORS["purple"],
+            s=3,
+            alpha=0.8,
+        )
+        ax_constellation.set_xlabel("In-phase (I)")
+        ax_constellation.set_ylabel("Quadrature (Q)")
+        ax_constellation.set_title("Constellation")
+        ax_constellation.grid(True, alpha=0.3)
+        ax_constellation.set_aspect("equal")
+
+    if ax_psd is not None:
+        psd_samples = _get_plot_samples(signal=signal, fast_mode=fast_mode, slow_max=65_536, fast_max=16_384)
+        window = hann(len(psd_samples))
+        spectrum = np.abs(fftshift(fft(psd_samples * window))) ** 2
+        freqs = np.linspace(-sample_rate_hz / 2, sample_rate_hz / 2, len(psd_samples))
+        freqs = freqs + center_freq_hz
+        spectrum_db = 10 * np.log10(spectrum + 1e-12)
+
+        ax_psd.plot(freqs / 1e6, spectrum_db, color=COLORS["accent"], linewidth=1.0)
+        ax_psd.set_xlabel("Frequency (MHz)")
+        ax_psd.set_ylabel("Power (dB)")
+        ax_psd.set_title("Power Spectral Density")
+        ax_psd.grid(True, alpha=0.3)
+
+    if compact_mode:
+        ax1.set_xticks([])
+        ax1.set_yticks([])
+        ax2.set_xticks([])
+        ax2.set_yticks([])
+
+        plt.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0)
+    else:
+        plt.tight_layout()
+        if show_title:
+            plt.subplots_adjust(top=0.92)
+
+    if saveplot:
+        output_path, extension = set_path(output_path=output_path)
+        dpi_value = _set_dpi(fast_mode=fast_mode, labels_mode=labels_mode, extension=extension)
+
+        plt.savefig(output_path, dpi=dpi_value, bbox_inches="tight", facecolor="#0f172a", edgecolor="none")
+        print(f"Saved signal plot to {output_path}")
+        return output_path
+
+    plt.show()
+    return None
+
+
+__all__ = [
+    "setup_style",
+    "detect_constellation_symbols",
+    "create_plots",
+]