add spectrum analyzer animation

This commit is contained in:
eBrnd 2015-09-21 22:52:09 +02:00
parent 95c04d0042
commit 3a39fc5d37
1 changed files with 170 additions and 0 deletions

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animations/spectrum.py Normal file
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#!/usr/bin/env python2
import alsaaudio
import numpy
import random
import time
import sys
WIDTH = 6
HEIGHT = 8
AUDIO_RATE = 4000 # sampling rate in Hz
WINDOW_SIZE = 8 # average fft over WINDOW_SIZE audio frames
BOX_MAP = [
[357, 18, 369, 186, 249, 228, 51],
[279, 9, 57, 159, 300, 108, 204],
[261, 42, 183, 201, 273, 246, 15],
[306, 168, 24, 138, 309, 165, 39],
[258, 222, 87, 363, 291, 231, 243],
[252, 114, 180, 75, 282, 141, 33],
[264, 288, 120, 135, 255, 99, 105],
[285, 207, 102, 45, 297, 216, 63],
]
COLORS = [
[50, 255, 50], [50, 255, 120], [50, 251, 255], [50, 120, 255],
[50, 50, 255], [180, 50, 255], [255, 50, 137], [255, 50, 50]
]
channels = {}
def set_box(x,y,r,g,b):
if x >= 0 and y >= 0 and x < WIDTH and y < HEIGHT:
base_address = BOX_MAP[y][x]
channels[base_address] = r
channels[base_address + 1] = g
channels[base_address + 2] = b
def output_channels():
for channel, value in channels.items():
print("%d : %d" % (channel, value))
print("")
sys.stdout.flush()
class Background:
"""clear the light wall to a pseudorandomly changing/fading solid color"""
def __init__(self):
self._current_bg_color = [ 0, 0, 0 ]
self._target_bg_color = [ 128, 128, 128 ]
self._bg_time = 0
def clear(self):
for i in range(3):
if self._current_bg_color[i] < self._target_bg_color[i]:
self._current_bg_color[i] = self._current_bg_color[i] + 1
elif self._current_bg_color[i] > self._target_bg_color[i]:
self._current_bg_color[i] = self._current_bg_color[i] - 1
if self._current_bg_color[i] > 128:
self._current_bg_color[i] = 128
elif self._current_bg_color[i] < 0:
self._current_bg_color[i] = 0
self._bg_time = self._bg_time + 1
if self._bg_time == 64:
for i in range(3):
self._target_bg_color[i] = random.randint(0, 128)
self._bg_time = 0
for x in range(0, WIDTH):
for y in range(0, HEIGHT):
set_box(x, y, self._current_bg_color[0], self._current_bg_color[1], self._current_bg_color[2])
def audio_from_raw(raw):
"""convert bytewise signed 16bit little endian to int list"""
out = []
high = False
current = 0
for value in raw:
value = ord(value[0])
if high:
sign = value & 0x80
current = current + 256 * (value & 0x7F)
if sign:
current = -((~current & 0x7FFF) + 1)
out.append(current)
high = False
else:
current = value
high = True
return out
class Fft_output:
"""aggregate several fft'ed samples, does postprocessing to make it look nice and outputs
the aggregated result"""
def __init__(self, width, height, windowsize):
self._background = Background()
self._width = width
self._height = height
self._windowsize = windowsize
self._count = 0
self._data = []
for _ in range(width):
self._data.append(0)
def add(self, data):
"""add a set of fft data to the internal store, output the result if enough data is
available"""
abss = numpy.absolute(data[1:self._width+1])
for i in range(self._width):
self._data[i] = self._data[i] + abss[i]
self._count = self._count + 1
if (self._count == self._windowsize):
self.output_twinkl()
self._count = 0
for i in range(self._width):
self._data[i] = 0
def output_twinkl(self):
"""output graph to twinkl client"""
# correct for disproportionately large first and second column
self._data[0] = self._data[0] / 2.5
self._data[1] = self._data[1] / 1.5
abss = numpy.absolute(self._data)
self._background.clear()
for col in range(self._width):
normalized = min(int(abss[col] / (self._height * self._windowsize * 1000)), self._height)
color = COLORS[normalized-1]
for row in range(self._height - normalized, self._height):
set_box(col, row, color[0], color[1], color[2])
output_channels()
def init_audio(rate):
"""init sound input to 16bit little endian and user defined sampling rate"""
ain = alsaaudio.PCM(alsaaudio.PCM_CAPTURE, alsaaudio.PCM_NORMAL)
ain.setformat(alsaaudio.PCM_FORMAT_S16_LE)
ain.setrate(rate)
return ain
def main():
ain = init_audio(AUDIO_RATE)
fft_out = Fft_output(WIDTH, HEIGHT, WINDOW_SIZE)
while True:
data = ain.read();
audio = audio_from_raw(data[1])
fft = numpy.fft.fft(audio)
fft_out.add(fft)
main()