src/ria_toolkit_oss/viz/recording.py

@@ -54,18 +54,6 @@ def spectrogram(rec: Recording, thumbnail: bool = False) -> Figure:
     frequencies_shifted = np.fft.fftshift(frequencies)
     Sxx_shifted = np.fft.fftshift(Sxx_log_norm, axes=0)
 
-    # Downsample heatmap for performance (max 500x500 = 250,000 points)
-    max_freq_bins = 500
-    max_time_bins = 500
-
-    freq_step = max(1, len(frequencies_shifted) // max_freq_bins)
-    time_step = max(1, len(times) // max_time_bins)
-
-    if freq_step > 1 or time_step > 1:
-        Sxx_shifted = Sxx_shifted[::freq_step, ::time_step]
-        frequencies_shifted = frequencies_shifted[::freq_step]
-        times = times[::time_step]
-
     fig = go.Figure(
         data=go.Heatmap(
             z=Sxx_shifted,
@@ -114,19 +102,11 @@ def iq_time_series(rec: Recording) -> Figure:
     complex_signal = rec.data[0]
     sample_rate = int(rec.metadata.get("sample_rate", 1))
     plot_length = len(complex_signal)
-    # Downsample for performance (max 10,000 points)
+    t = np.arange(0, plot_length, 1) / sample_rate
-    max_points = 10000
-    step = max(1, plot_length // max_points)
-    if step > 1:
-        complex_signal = complex_signal[::step]
-        plot_length = len(complex_signal)
-
-    t = np.arange(0, plot_length, 1) * step / sample_rate
 
     fig = go.Figure()
-    # Use Scattergl for WebGL-accelerated rendering
+    fig.add_trace(go.Scatter(x=t, y=complex_signal.real, mode="lines", name="I (In-phase)", line=dict(width=0.6)))
-    fig.add_trace(go.Scattergl(x=t, y=complex_signal.real, mode="lines", name="I (In-phase)", line=dict(width=0.6)))
+    fig.add_trace(go.Scatter(x=t, y=complex_signal.imag, mode="lines", name="Q (Quadrature)", line=dict(width=0.6)))
-    fig.add_trace(go.Scattergl(x=t, y=complex_signal.imag, mode="lines", name="Q (Quadrature)", line=dict(width=0.6)))
 
     fig.update_layout(
         title="IQ Time Series",
@@ -159,15 +139,8 @@ def frequency_spectrum(rec: Recording) -> Figure:
     log_spectrum = np.log10(spectrum + epsilon)
     scaled_log_spectrum = (log_spectrum - log_spectrum.min()) / (log_spectrum.max() - log_spectrum.min())
 
-    # Downsample for performance (max 10,000 points)
-    max_points = 10000
-    if len(freqs) > max_points:
-        step = len(freqs) // max_points
-        freqs = freqs[::step]
-        scaled_log_spectrum = scaled_log_spectrum[::step]
-
     fig = go.Figure()
-    fig.add_trace(go.Scattergl(x=freqs, y=scaled_log_spectrum, mode="lines", name="Spectrum", line=dict(width=0.4)))
+    fig.add_trace(go.Scatter(x=freqs, y=scaled_log_spectrum, mode="lines", name="Spectrum", line=dict(width=0.4)))
 
     fig.update_layout(
         title="Frequency Spectrum",
@@ -201,7 +174,7 @@ def constellation(rec: Recording) -> Figure:
     q_ds = complex_signal.imag[::step]
 
     fig = go.Figure()
-    fig.add_trace(go.Scattergl(x=i_ds, y=q_ds, mode="lines", name="Constellation", line=dict(width=0.2)))
+    fig.add_trace(go.Scatter(x=i_ds, y=q_ds, mode="lines", name="Constellation", line=dict(width=0.2)))
 
     fig.update_layout(
         title="Constellation",
@@ -248,7 +221,7 @@ def power_spectral_density(rec: Recording) -> Figure:
 
     fig = go.Figure()
     fig.add_trace(
-        go.Scattergl(x=frequencies_shifted, y=psd_db, mode="lines", name="PSD", line=dict(width=0.8, color="#00D9FF"))
+        go.Scatter(x=frequencies_shifted, y=psd_db, mode="lines", name="PSD", line=dict(width=0.8, color="#00D9FF"))
     )
 
     fig.update_layout(
@@ -284,16 +257,8 @@ def fft_plot(rec: Recording) -> Figure:
     magnitude = np.abs(fft_result)
     magnitude_db = 20 * np.log10(magnitude + 1e-10)
 
-    max_points = 10000
-    if len(freqs) > max_points:
-        step = len(freqs) // max_points
-        freqs = freqs[::step]
-        magnitude_db = magnitude_db[::step]
-
     fig = go.Figure()
-    fig.add_trace(
+    fig.add_trace(go.Scatter(x=freqs, y=magnitude_db, mode="lines", name="FFT", line=dict(width=0.6, color="#FF6B9D")))
-        go.Scattergl(x=freqs, y=magnitude_db, mode="lines", name="FFT", line=dict(width=0.6, color="#FF6B9D"))
-    )
 
     fig.update_layout(
         title="FFT Magnitude",
@@ -349,18 +314,6 @@ def spectrogram_3d(rec: Recording) -> Figure:
     frequencies_shifted = np.fft.fftshift(frequencies)
     Sxx_shifted = np.fft.fftshift(Sxx_log, axes=0)
 
-    # Downsample to prevent browser memory issues (max ~40,000 total points)
-    max_freq_bins = 200
-    max_time_bins = 200
-
-    freq_step = max(1, len(frequencies_shifted) // max_freq_bins)
-    time_step = max(1, len(times) // max_time_bins)
-
-    if freq_step > 1 or time_step > 1:
-        Sxx_shifted = Sxx_shifted[::freq_step, ::time_step]
-        frequencies_shifted = frequencies_shifted[::freq_step]
-        times = times[::time_step]
-
     fig = go.Figure(
         data=[
             go.Surface(