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play.py: FFT spectrum analyzer with colored frequency bands
Logarithmic frequency bins via FFT. Bass (green) → mids (yellow) → highs (red). Block characters ▁▂▃▄▅▆▇█. 40fps at 48 bands. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -291,7 +291,9 @@ def play_audio(buf, sample_rate, title="", info_lines=None, offset_sec=0.0):
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print()
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print(" [+/f] +5s [-/s] -5s [d] +30s [a] -30s [space] pause [n] next [p] prev [q] quit")
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print()
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print() # blank lines for oscilloscope + progress
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print() # blank lines for oscilloscope + whitespace + progress
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print()
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print()
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stream = sd.OutputStream(
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samplerate=sample_rate,
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@@ -320,39 +322,48 @@ def play_audio(buf, sample_rate, title="", info_lines=None, offset_sec=0.0):
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bar = "█" * filled + "░" * (bar_w - filled)
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icon = "▶" if playing else "⏸"
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# Oscilloscope — braille waveform centered on zero crossing
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scope_w = 60
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# Braille: each char is 2 wide x 4 tall dots. We use pairs of columns.
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# For a simpler approach: use block + braille for a centered waveform
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wave_chars = " ·∘○◌●◉⦿⬤"
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window_size = int(sample_rate * 0.03) # 30ms window
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# Spectrum analyzer — FFT frequency bands displayed as EQ bars
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import numpy as np
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scope_w = 48
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bars = " ▁▂▃▄▅▆▇█"
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window_size = int(sample_rate * 0.05)
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start_s = max(0, pos - window_size // 2)
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end_s = min(total_frames, start_s + window_size)
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if end_s > start_s and playing:
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if end_s > start_s and playing and (end_s - start_s) > 64:
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chunk = buf[start_s:end_s]
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if chunk.ndim == 2:
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chunk = chunk.mean(axis=1)
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step = max(1, len(chunk) // scope_w)
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# Center the waveform — show positive and negative
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samples = [chunk[i * step] if i * step < len(chunk) else 0
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for i in range(scope_w)]
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peak = max(abs(s) for s in samples) if any(samples) else 1
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# Map to centered display: ▁▂▃▄█▄▃▂▁
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top = "▁▂▃▄▅▆▇█"
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scope_chars = []
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for s in samples:
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norm = s / peak if peak > 0 else 0
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idx = int(abs(norm) * 7)
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idx = min(7, idx)
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if idx == 0:
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scope_chars.append("─")
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# FFT
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fft = np.abs(np.fft.rfft(chunk))
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# Group into logarithmic frequency bands
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n_bins = len(fft)
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band_edges = np.logspace(np.log10(1), np.log10(n_bins), scope_w + 1).astype(int)
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band_edges = np.clip(band_edges, 0, n_bins - 1)
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bands = []
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for j in range(scope_w):
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lo, hi = band_edges[j], band_edges[j + 1]
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if hi <= lo:
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hi = lo + 1
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bands.append(np.mean(fft[lo:hi]))
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peak = max(bands) if max(bands) > 0 else 1
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# Color each bar: low freq green, mid yellow, high red
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scope_parts = []
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for j, b in enumerate(bands):
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idx = min(8, int(b / peak * 8))
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frac = j / scope_w
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if frac < 0.33:
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c = "\033[32m" # green — bass
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elif frac < 0.66:
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c = "\033[33m" # yellow — mids
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else:
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scope_chars.append(top[idx])
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scope = "".join(scope_chars)
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c = "\033[31m" # red — highs
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scope_parts.append(f"{c}{bars[idx]}")
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scope = "".join(scope_parts) + "\033[0m"
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else:
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scope = "─" * scope_w
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scope = "\033[90m" + "─" * scope_w + "\033[0m"
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sys.stderr.write(f"\033[2A\r \033[33m{scope}\033[0m\n")
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sys.stderr.write(f"\033[3A\r\n {scope}\n\n")
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sys.stderr.write(f"\r {icon} {cur_m}:{cur_s:02d} / {tot_m}:{tot_s:02d} {bar} \n")
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sys.stderr.flush()
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