src/ria_toolkit_oss/viz/recording.py

@@ -189,155 +189,3 @@ def constellation(rec: Recording) -> Figure:
     )
 
     return fig
-
-
-def power_spectral_density(rec: Recording) -> Figure:
-    """Create a Power Spectral Density (PSD) plot from the recording.
-
-    :param rec: Input signal to plot.
-    :type rec: ria_toolkit_oss.datatypes.Recording
-
-    :return: PSD plot, as a Plotly Figure.
-    """
-    complex_signal = rec.data[0]
-    center_frequency = int(rec.metadata.get("center_frequency", 0))
-    sample_rate = int(rec.metadata.get("sample_rate", 1))
-
-    # Calculate PSD using Welch's method
-    frequencies, psd = signal.welch(
-        complex_signal,
-        fs=sample_rate,
-        nperseg=min(1024, len(complex_signal)),
-        return_onesided=False,
-        scaling="density",
-    )
-
-    # Shift frequencies and PSD for proper visualization
-    frequencies_shifted = fftshift(frequencies) + center_frequency
-    psd_shifted = fftshift(psd)
-
-    # Convert to dB scale
-    psd_db = 10 * np.log10(psd_shifted + 1e-10)
-
-    fig = go.Figure()
-    fig.add_trace(
-        go.Scatter(x=frequencies_shifted, y=psd_db, mode="lines", name="PSD", line=dict(width=0.8, color="#00D9FF"))
-    )
-
-    fig.update_layout(
-        title="Power Spectral Density",
-        xaxis_title="Frequency [Hz]",
-        yaxis_title="Power/Frequency [dB/Hz]",
-        template="plotly_dark",
-        height=300,
-        width=800,
-        showlegend=False,
-    )
-
-    return fig
-
-
-def fft_plot(rec: Recording) -> Figure:
-    """Create an FFT magnitude plot from the recording.
-
-    :param rec: Input signal to plot.
-    :type rec: ria_toolkit_oss.datatypes.Recording
-
-    :return: FFT plot, as a Plotly Figure.
-    """
-    complex_signal = rec.data[0]
-    center_frequency = int(rec.metadata.get("center_frequency", 0))
-    sample_rate = int(rec.metadata.get("sample_rate", 1))
-
-    # Compute FFT
-    fft_result = fftshift(fft(complex_signal))
-    freqs = fftshift(np.fft.fftfreq(len(complex_signal), 1 / sample_rate)) + center_frequency
-
-    # Convert to magnitude in dB
-    magnitude = np.abs(fft_result)
-    magnitude_db = 20 * np.log10(magnitude + 1e-10)
-
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=freqs, y=magnitude_db, mode="lines", name="FFT", line=dict(width=0.6, color="#FF6B9D")))
-
-    fig.update_layout(
-        title="FFT Magnitude",
-        xaxis_title="Frequency [Hz]",
-        yaxis_title="Magnitude [dB]",
-        template="plotly_dark",
-        height=300,
-        width=800,
-        showlegend=False,
-    )
-
-    return fig
-
-
-def spectrogram_3d(rec: Recording) -> Figure:
-    """Create a 3D spectrogram plot from the recording.
-
-    :param rec: Input signal to plot.
-    :type rec: ria_toolkit_oss.datatypes.Recording
-
-    :return: 3D Spectrogram, as a Plotly Figure.
-    """
-    complex_signal = rec.data[0]
-    sample_rate = int(rec.metadata.get("sample_rate", 1))
-    plot_length = len(complex_signal)
-
-    # Determine FFT size
-    if plot_length < 2000:
-        fft_size = 64
-    elif plot_length < 10000:
-        fft_size = 256
-    elif plot_length < 1000000:
-        fft_size = 1024
-    else:
-        fft_size = 2048
-
-    frequencies, times, Sxx = signal.spectrogram(
-        complex_signal,
-        fs=sample_rate,
-        nfft=fft_size,
-        nperseg=fft_size,
-        noverlap=fft_size // 8,
-        scaling="density",
-        mode="complex",
-        return_onesided=False,
-    )
-
-    # Convert complex values to amplitude and then to log scale
-    Sxx_magnitude = np.abs(Sxx)
-    Sxx_log = 10 * np.log10(Sxx_magnitude + 1e-10)
-
-    # Shift frequency bins for proper visualization
-    frequencies_shifted = np.fft.fftshift(frequencies)
-    Sxx_shifted = np.fft.fftshift(Sxx_log, axes=0)
-
-    fig = go.Figure(
-        data=[
-            go.Surface(
-                z=Sxx_shifted,
-                x=times,
-                y=frequencies_shifted,
-                colorscale="Viridis",
-                showscale=True,
-                colorbar=dict(title="Power [dB]"),
-            )
-        ]
-    )
-
-    fig.update_layout(
-        title="3D Spectrogram",
-        scene=dict(
-            xaxis_title="Time [s]",
-            yaxis_title="Frequency [Hz]",
-            zaxis_title="Power [dB]",
-            camera=dict(eye=dict(x=1.5, y=1.5, z=1.3)),
-        ),
-        template="plotly_dark",
-        height=600,
-        width=900,
-    )
-
-    return fig