734 lines
33 KiB
Python
734 lines
33 KiB
Python
from __future__ import print_function
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from __future__ import division
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import time
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import sys
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import numpy as np
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from scipy.ndimage.filters import gaussian_filter1d
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from collections import deque
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from qrangeslider import QRangeSlider
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from qfloatslider import QFloatSlider
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import config
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import microphone
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import dsp
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import led
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if config.USE_GUI:
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import pyqtgraph as pg
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from PyQt5.QtCore import *
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from PyQt5.QtWidgets import *
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class Visualizer():
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def __init__(self):
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self.effects = {"Scroll":self.visualize_scroll,
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"Energy":self.visualize_energy,
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"Spectrum":self.visualize_spectrum,
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#"Power":self.visualize_power,
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"Wavelength":self.visualize_wavelength,
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"Beat":self.visualize_beat,
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"Wave":self.visualize_wave,}
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#"Auto":self.visualize_auto}
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self.colors = {"Red":(255,0,0),
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"Orange":(255,40,0),
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"Yellow":(255,255,0),
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"Green":(0,255,0),
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"Blue":(0,0,255),
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"Light blue":(1,247,161),
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"Purple":(80,5,252),
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"Pink":(255,0,178)}
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self.wavelength_color_modes = {"Spectral":"rgb",
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"Dancefloor":"rpb",
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"Brilliance":"ywb",
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"Jungle":"ryg"}
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self.current_effect = "Wavelength"
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# Setup for frequency detection algorithm
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self.freq_channel_history = 40
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self.beat_count = 0
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self.freq_channels = [deque(maxlen=self.freq_channel_history) for i in range(config.N_FFT_BINS)]
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self.prev_output = np.array([[0 for i in range(config.N_PIXELS)] for i in range(3)])
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self.prev_spectrum = [0 for i in range(config.N_PIXELS//2)]
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self.current_freq_detects = {"beat":False,
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"low":False,
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"mid":False,
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"high":False}
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self.prev_freq_detects = {"beat":0,
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"low":0,
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"mid":0,
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"high":0}
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self.detection_ranges = {"beat":(0,1),
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"low":(1,int(config.N_FFT_BINS*0.2)),
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"mid":(int(config.N_FFT_BINS*0.4),int(config.N_FFT_BINS*0.6)),
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"high":(int(config.N_FFT_BINS*0.7),int(config.N_FFT_BINS))}
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self.min_detect_amplitude = {"beat":0.7,
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"low":0.5,
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"mid":0.3,
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"high":0.05}
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# Configurable options for effects go in here.
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# Usage: self.effect_opts[effect][option]
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self.effect_opts = {"Energy":{"blur": 1, # Amount of blur to apply
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"scale":0.9}, # Width of effect on strip
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"Wave":{"color_wave": "Red", # Colour of moving bit
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"wipe_len":5, # Initial length of colour bit after beat
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"wipe_speed":2}, # Number of pixels added to colour bit every frame
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"Wavelength":{"roll": False, # Cycle colour overlay across strip
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"color_mode": "Spectral", # Colour mode of overlay (rgb, rpb, ywb, ryg)
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"mirror": False} # Reflect output down centre of strip?
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}
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# Configurations for dynamic ui generation. Effect options can be changed by widgets created at runtime,
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# meaning that you don't need to worry about the user interface - it's all done for you.
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# Each effect key points to a list. Each list contains lists giving config for each option.
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# Syntax: effect:[variable, label_text, ui_element, opts]
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# effect - the effect which you want to change options for. MUST have a key in self.effect_opts
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# variable - the key of thing you want to be changed. MUST be in self.effect_opts[effect], otherwise it won't work.
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# label - the text displayed on the ui
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# ui_element - how you want the variable to be changed
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# opts - options for the ui element. Must be a tuple.
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# UI Elements + opts:
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# slider, (min, max, interval, default) (for integer values in a given range)
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# float_slider, (min, max, interval, default) (for floating point values in a given range)
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# checkbox, (default) (for True/False values)
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# dropdown, (dict, default) (dict example see self.colors above)
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#
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self.dynamic_effects_config = {"Energy":[["blur", "Blur", "float_slider", (0.1,4.0,0.1,1.0)],
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["scale", "Scale", "float_slider", (0.4,1.0,0.05,0.9)]],
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"Wave":[["color_wave", "Wave Color", "dropdown", self.colors],
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["wipe_len", "Wave Start Length", "slider", (0,config.N_PIXELS//4,1,5)],
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["wipe_speed", "Wave Speed", "slider", (1,10,1,2)]],
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"Wavelength":[["roll", "Roll Colors", "checkbox", False],
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["color_mode", "Color Mode", "dropdown", self.wavelength_color_modes]]
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}
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# Setup for "Wave" (don't change these)
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self.wave_wipe_count = 0
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# Setup for "Wavelength" (don't change these)
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self._wavelength_set_color_mode(self.effect_opts["Wavelength"]["color_mode"])
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def _wavelength_set_color_mode(self, mode):
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# chunks of colour gradients
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self.rgb_overlay = np.zeros((3,242))
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# used to construct rgb overlay. [0-255,255...] whole length of strip
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_gradient_whole = [int(i*255/(config.N_PIXELS//2)) for i in range(config.N_PIXELS//2)] +\
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[255 for i in range(config.N_PIXELS//2)]
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# used to construct rgb overlay. [0-255,255...] 1/2 length of strip
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_gradient_half = _gradient_whole[::2]
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if self.wavelength_color_modes[self.effect_opts["Wavelength"]["color_mode"]] == "rgb":
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self.rgb_overlay[0, :config.N_PIXELS//2] = _gradient_half[::-1]
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self.rgb_overlay[1, :] = _gradient_half + _gradient_half[::-1]
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self.rgb_overlay[2, :] = np.flipud(self.rgb_overlay[0])
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elif self.wavelength_color_modes[self.effect_opts["Wavelength"]["color_mode"]] == "rpb":
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self.rgb_overlay[0, :] = _gradient_whole[::-1]
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self.rgb_overlay[2, :] = _gradient_whole
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elif self.wavelength_color_modes[self.effect_opts["Wavelength"]["color_mode"]] == "ywb":
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self.rgb_overlay[0, :] = _gradient_whole[::-1]
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self.rgb_overlay[1, :] = 255
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self.rgb_overlay[2, :] = _gradient_whole
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elif self.wavelength_color_modes[self.effect_opts["Wavelength"]["color_mode"]] == "ryg":
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self.rgb_overlay[0, :] = _gradient_whole[::-1]
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self.rgb_overlay[1, :] = _gradient_whole
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else:
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raise ValueError("Colour mode '{}' not known. Leave an issue on github if you want it added!".format(mode))
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self.effect_opts["Wavelength"]["color_mode"] = mode
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def get_vis(self, y):
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self.update_freq_channels(y)
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self.detect_freqs()
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self.prev_output = np.copy(self.effects[self.current_effect](y))
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return self.prev_output
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def _split_equal(self, value, parts):
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value = float(value)
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return [int(round(i*value/parts)) for i in range(1,parts+1)]
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def update_freq_channels(self, y):
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for i in range(len(y)):
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self.freq_channels[i].appendleft(y[i])
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def detect_freqs(self):
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"""
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Function that updates current_freq_detects. Any visualisation algorithm can check if
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there is currently a beat, low, mid, or high by querying the self.current_freq_detects dict.
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"""
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channel_avgs = []
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differences = []
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for i in range(config.N_FFT_BINS):
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channel_avgs.append(sum(self.freq_channels[i])/len(self.freq_channels[i]))
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differences.append(((self.freq_channels[i][0]-channel_avgs[i])*100)//channel_avgs[i])
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for i in ["beat", "low", "mid", "high"]:
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if any(differences[j] >= 100 and self.freq_channels[j][0] >= self.min_detect_amplitude[i]\
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for j in range(*self.detection_ranges[i]))\
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and (time.time() - self.prev_freq_detects[i] > 0.15)\
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and len(self.freq_channels[0]) == self.freq_channel_history:
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self.prev_freq_detects[i] = time.time()
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self.current_freq_detects[i] = True
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#print(i)
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else:
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self.current_freq_detects[i] = False
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#if self.current_freq_detects["beat"]:
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# print(time.time(),"Beat")
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#pass
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#print(differences[0], channel_avgs[0])
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#print("{1: <{0}}{2: <{0}}{4: <{0}}{4}".format(7, self.current_freq_detects["beat"],
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# self.current_freq_detects["low"],
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# self.current_freq_detects["mid"],
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# self.current_freq_detects["high"]))
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def visualize_scroll(self, y):
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"""Effect that originates in the center and scrolls outwards"""
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global p
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y = y**2.0
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gain.update(y)
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y /= gain.value
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y *= 255.0
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r = int(np.max(y[:len(y) // 3]))
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g = int(np.max(y[len(y) // 3: 2 * len(y) // 3]))
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b = int(np.max(y[2 * len(y) // 3:]))
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# Scrolling effect window
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p[:, 1:] = p[:, :-1]
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p *= 0.98
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p = gaussian_filter1d(p, sigma=0.2)
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# Create new color originating at the center
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p[0, 0] = r
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p[1, 0] = g
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p[2, 0] = b
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# Update the LED strip
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return np.concatenate((p[:, ::-1], p), axis=1)
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def visualize_energy(self, y):
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"""Effect that expands from the center with increasing sound energy"""
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global p
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y = np.copy(y)
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gain.update(y)
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y /= gain.value
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scale = self.effect_opts["Energy"]["scale"]
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# Scale by the width of the LED strip
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y *= float((config.N_PIXELS * scale) - 1)
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# Map color channels according to energy in the different freq bands
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r = int(np.mean(y[:len(y) // 3]**scale))
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g = int(np.mean(y[len(y) // 3: 2 * len(y) // 3]**scale))
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b = int(np.mean(y[2 * len(y) // 3:]**scale))
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# Assign color to different frequency regions
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p[0, :r] = 255.0
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p[0, r:] = 0.0
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p[1, :g] = 255.0
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p[1, g:] = 0.0
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p[2, :b] = 255.0
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p[2, b:] = 0.0
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p_filt.update(p)
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p = np.round(p_filt.value)
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# Apply blur to smooth the edges
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p[0, :] = gaussian_filter1d(p[0, :], sigma=self.effect_opts["Energy"]["blur"])
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p[1, :] = gaussian_filter1d(p[1, :], sigma=self.effect_opts["Energy"]["blur"])
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p[2, :] = gaussian_filter1d(p[2, :], sigma=self.effect_opts["Energy"]["blur"])
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# Set the new pixel value
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return np.concatenate((p[:, ::-1], p), axis=1)
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def visualize_wavelength(self, y):
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y = np.copy(interpolate(y, config.N_PIXELS // 2))
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common_mode.update(y)
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diff = y - self.prev_spectrum
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self.prev_spectrum = np.copy(y)
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# Color channel mappings
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r = r_filt.update(y - common_mode.value)
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g = np.abs(diff)
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b = b_filt.update(np.copy(y))
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if self.effect_opts["Wavelength"]["mirror"]:
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r = r.extend(r[::-1])
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r = r.extend(r[::-1])
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else:
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# stretch (double) r so it covers the entire spectrum
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r = np.array([j for i in zip(r,r) for j in i])
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b = np.array([j for i in zip(b,b) for j in i])
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output = [self.rgb_overlay[0]*r,self.rgb_overlay[1]*r,self.rgb_overlay[2]*r]
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self.prev_spectrum = y
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if self.effect_opts["Wavelength"]["roll"]:
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self.rgb_overlay = np.roll(self.rgb_overlay,1,axis=1)
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output[0] = gaussian_filter1d(output[0], sigma=4.0)
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output[1] = gaussian_filter1d(output[1], sigma=4.0)
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output[2] = gaussian_filter1d(output[2], sigma=4.0)
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return output
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#return np.concatenate((p[:, ::-1], p), axis=1)
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def visualize_power(self, y):
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"""Effect that pulses different reqions of the strip increasing sound energy"""
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global p
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_p = np.copy(p)
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y = np.copy(interpolate(y, config.N_PIXELS // 2))
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common_mode.update(y)
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diff = y - self.prev_spectrum
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self.prev_spectrum = np.copy(y)
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# Color channel mappings
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r = r_filt.update(y - common_mode.value)
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g = np.abs(diff)
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b = b_filt.update(np.copy(y))
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# I have no idea what any of this does but it looks cool
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r = [int(i*255) for i in r[::3]]
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g = [int(i*255) for i in g[::3]]
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b = [int(i*255) for i in b[::3]]
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_p[0, 0:len(r)] = r
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_p[1, len(r):len(r)+len(g)] = g
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_p[2, len(r)+len(g):config.N_PIXELS] = b[:39]
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p_filt.update(_p)
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# Clip it into range
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_p = np.clip(p, 0, 255).astype(int)
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# Apply substantial blur to smooth the edges
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_p[0, :] = gaussian_filter1d(_p[0, :], sigma=3.0)
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_p[1, :] = gaussian_filter1d(_p[1, :], sigma=3.0)
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_p[2, :] = gaussian_filter1d(_p[2, :], sigma=3.0)
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self.prev_spectrum = y
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return np.concatenate((_p[:, ::-1], _p), axis=1)
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def visualize_spectrum(self, y):
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"""Effect that maps the Mel filterbank frequencies onto the LED strip"""
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global p
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#print(len(y))
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#print(y)
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y = np.copy(interpolate(y, config.N_PIXELS // 2))
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common_mode.update(y)
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diff = y - self.prev_spectrum
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self.prev_spectrum = np.copy(y)
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# Color channel mappings
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r = r_filt.update(y - common_mode.value)
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g = np.abs(diff)
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b = b_filt.update(np.copy(y))
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# Mirror the color channels for symmetric output
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r = np.concatenate((r[::-1], r))
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g = np.concatenate((g[::-1], g))
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b = np.concatenate((b[::-1], b))
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output = np.array([r, g,b]) * 255
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self.prev_spectrum = y
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return output
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def visualize_auto(self,y):
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"""Automatically (intelligently?) cycle through effects"""
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return self.visualize_beat(y) # real intelligent
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def visualize_wave(self, y):
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"""Effect that flashes to the beat with scrolling coloured bits"""
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if self.current_freq_detects["beat"]:
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output = np.array([[255 for i in range(config.N_PIXELS)] for i in range(3)])
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self.wave_wipe_count = self.effect_opts["Wave"]["wipe_len"]
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else:
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output = np.copy(self.prev_output)
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#for i in range(len(self.prev_output)):
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# output[i] = np.hsplit(self.prev_output[i],2)[0]
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output = np.multiply(self.prev_output,0.7)
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for i in range(self.wave_wipe_count):
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output[0][i]=self.colors[self.effect_opts["Wave"]["color_wave"]][0]
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output[0][-i]=self.colors[self.effect_opts["Wave"]["color_wave"]][0]
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output[1][i]=self.colors[self.effect_opts["Wave"]["color_wave"]][1]
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output[1][-i]=self.colors[self.effect_opts["Wave"]["color_wave"]][1]
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output[2][i]=self.colors[self.effect_opts["Wave"]["color_wave"]][2]
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output[2][-i]=self.colors[self.effect_opts["Wave"]["color_wave"]][2]
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#output = np.concatenate([output,np.fliplr(output)], axis=1)
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self.wave_wipe_count += self.effect_opts["Wave"]["wipe_speed"]
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if self.wave_wipe_count > config.N_PIXELS//2:
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self.wave_wipe_count = config.N_PIXELS//2
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return output
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def visualize_beat(self, y):
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"""Effect that flashes to the beat"""
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if self.current_freq_detects["beat"]:
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output = np.array([[255 for i in range(config.N_PIXELS)] for i in range(3)])
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else:
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output = np.copy(self.prev_output)
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output = np.multiply(self.prev_output,0.7)
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return output
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class GUI(QWidget):
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def __init__(self):
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super().__init__()
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self.initUI()
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def initUI(self):
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# ==================================== Set up window and wrapping layout
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self.setWindowTitle("Visualization")
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wrapper = QVBoxLayout()
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# ========================================== Set up FPS and error labels
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labels_layout = QHBoxLayout()
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self.label_error = QLabel("")
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self.label_fps = QLabel("")
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self.label_fps.setAlignment(Qt.AlignRight | Qt.AlignVCenter)
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labels_layout.addWidget(self.label_error)
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labels_layout.addStretch()
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labels_layout.addWidget(self.label_fps)
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# ================================================== Set up graph layout
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graph_view = pg.GraphicsView()
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graph_layout = pg.GraphicsLayout(border=(100,100,100))
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graph_view.setCentralItem(graph_layout)
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# Mel filterbank plot
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fft_plot = graph_layout.addPlot(title='Filterbank Output', colspan=3)
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fft_plot.setRange(yRange=[-0.1, 1.2])
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fft_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
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x_data = np.array(range(1, config.N_FFT_BINS + 1))
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self.mel_curve = pg.PlotCurveItem()
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self.mel_curve.setData(x=x_data, y=x_data*0)
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fft_plot.addItem(self.mel_curve)
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# Visualization plot
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graph_layout.nextRow()
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led_plot = graph_layout.addPlot(title='Visualization Output', colspan=3)
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led_plot.setRange(yRange=[-5, 260])
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led_plot.disableAutoRange(axis=pg.ViewBox.YAxis)
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# Pen for each of the color channel curves
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r_pen = pg.mkPen((255, 30, 30, 200), width=4)
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g_pen = pg.mkPen((30, 255, 30, 200), width=4)
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b_pen = pg.mkPen((30, 30, 255, 200), width=4)
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# Color channel curves
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self.r_curve = pg.PlotCurveItem(pen=r_pen)
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self.g_curve = pg.PlotCurveItem(pen=g_pen)
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self.b_curve = pg.PlotCurveItem(pen=b_pen)
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# Define x data
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x_data = np.array(range(1, config.N_PIXELS + 1))
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self.r_curve.setData(x=x_data, y=x_data*0)
|
|
self.g_curve.setData(x=x_data, y=x_data*0)
|
|
self.b_curve.setData(x=x_data, y=x_data*0)
|
|
# Add curves to plot
|
|
led_plot.addItem(self.r_curve)
|
|
led_plot.addItem(self.g_curve)
|
|
led_plot.addItem(self.b_curve)
|
|
|
|
# ================================================= Set up button layout
|
|
label_active = QLabel("Active Effect")
|
|
button_grid = QGridLayout()
|
|
buttons = {}
|
|
connecting_funcs = {}
|
|
grid_width = 4
|
|
i = 0
|
|
j = 0
|
|
# Dynamically layout buttons and connect them to the visualisation effects
|
|
def connect_generator(effect):
|
|
def func():
|
|
visualizer.current_effect = effect
|
|
func.__name__ = effect
|
|
return func
|
|
# Where the magic happens
|
|
for effect in visualizer.effects:
|
|
connecting_funcs[effect] = connect_generator(effect)
|
|
buttons[effect] = QPushButton(effect)
|
|
buttons[effect].clicked.connect(connecting_funcs[effect])
|
|
button_grid.addWidget(buttons[effect], j, i)
|
|
i += 1
|
|
if i % grid_width == 0:
|
|
i = 0
|
|
j += 1
|
|
|
|
# ============================================== Set up frequency slider
|
|
# Frequency range label
|
|
label_slider = QLabel("Frequency Range")
|
|
# Frequency slider
|
|
def freq_slider_change(tick):
|
|
minf = freq_slider.tickValue(0)**2.0 * (config.MIC_RATE / 2.0)
|
|
maxf = freq_slider.tickValue(1)**2.0 * (config.MIC_RATE / 2.0)
|
|
t = 'Frequency range: {:.0f} - {:.0f} Hz'.format(minf, maxf)
|
|
freq_label.setText(t)
|
|
config.MIN_FREQUENCY = minf
|
|
config.MAX_FREQUENCY = maxf
|
|
dsp.create_mel_bank()
|
|
def set_freq_min():
|
|
config.MIN_FREQUENCY = freq_slider.start()
|
|
dsp.create_mel_bank()
|
|
def set_freq_max():
|
|
config.MAX_FREQUENCY = freq_slider.end()
|
|
dsp.create_mel_bank()
|
|
freq_slider = QRangeSlider()
|
|
freq_slider.show()
|
|
freq_slider.setMin(0)
|
|
freq_slider.setMax(20000)
|
|
freq_slider.setRange(config.MIN_FREQUENCY, config.MAX_FREQUENCY)
|
|
freq_slider.setBackgroundStyle('background: qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #222, stop:1 #333);')
|
|
freq_slider.setSpanStyle('background: qlineargradient(x1:0, y1:0, x2:0, y2:1, stop:0 #282, stop:1 #393);')
|
|
freq_slider.setDrawValues(True)
|
|
freq_slider.endValueChanged.connect(set_freq_max)
|
|
freq_slider.startValueChanged.connect(set_freq_min)
|
|
freq_slider.setStyleSheet("""
|
|
QRangeSlider * {
|
|
border: 0px;
|
|
padding: 0px;
|
|
}
|
|
QRangeSlider > QSplitter::handle {
|
|
background: #fff;
|
|
}
|
|
QRangeSlider > QSplitter::handle:vertical {
|
|
height: 3px;
|
|
}
|
|
QRangeSlider > QSplitter::handle:pressed {
|
|
background: #ca5;
|
|
}
|
|
""")
|
|
|
|
# ============================================ Set up option tabs layout
|
|
label_options = QLabel("Effect Options")
|
|
opts_tabs = QTabWidget()
|
|
# Dynamically set up tabs
|
|
tabs = {}
|
|
grid_layouts = {}
|
|
self.grid_layout_widgets = {}
|
|
options = visualizer.effect_opts.keys()
|
|
for effect in visualizer.effects:
|
|
# Make the tab
|
|
self.grid_layout_widgets[effect] = {}
|
|
tabs[effect] = QWidget()
|
|
grid_layouts[effect] = QGridLayout()
|
|
tabs[effect].setLayout(grid_layouts[effect])
|
|
opts_tabs.addTab(tabs[effect],effect)
|
|
# These functions make functions for the dynamic ui generation
|
|
# YOU WANT-A DYNAMIC I GIVE-A YOU DYNAMIC!
|
|
def gen_slider_valuechanger(effect, key):
|
|
def func():
|
|
visualizer.effect_opts[effect][key] = self.grid_layout_widgets[effect][key].value()
|
|
return func
|
|
def gen_float_slider_valuechanger(effect, key):
|
|
def func():
|
|
visualizer.effect_opts[effect][key] = self.grid_layout_widgets[effect][key].slider_value
|
|
return func
|
|
def gen_combobox_valuechanger(effect, key):
|
|
def func():
|
|
visualizer.effect_opts[effect][key] = self.grid_layout_widgets[effect][key].currentText()
|
|
visualizer._wavelength_set_color_mode(visualizer.effect_opts[effect][key])
|
|
return func
|
|
def gen_checkbox_valuechanger(effect, key):
|
|
def func():
|
|
visualizer.effect_opts[effect][key] = self.grid_layout_widgets[effect][key].isChecked()
|
|
return func
|
|
# Dynamically generate ui for settings
|
|
if effect in visualizer.dynamic_effects_config:
|
|
i = 0
|
|
connecting_funcs[effect] = {}
|
|
for key, label, ui_element, opts in visualizer.dynamic_effects_config[effect][:]:
|
|
if ui_element == "slider":
|
|
connecting_funcs[effect][key] = gen_slider_valuechanger(effect, key)
|
|
self.grid_layout_widgets[effect][key] = QSlider(Qt.Horizontal)
|
|
self.grid_layout_widgets[effect][key].setMinimum(opts[0])
|
|
self.grid_layout_widgets[effect][key].setMaximum(opts[1])
|
|
self.grid_layout_widgets[effect][key].setValue(opts[2])
|
|
self.grid_layout_widgets[effect][key].valueChanged.connect(
|
|
connecting_funcs[effect][key])
|
|
grid_layouts[effect].addWidget(QLabel(label),i,0)
|
|
grid_layouts[effect].addWidget(self.grid_layout_widgets[effect][key],i,1)
|
|
elif ui_element == "float_slider":
|
|
connecting_funcs[effect][key] = gen_float_slider_valuechanger(effect, key)
|
|
self.grid_layout_widgets[effect][key] = QFloatSlider(*opts)
|
|
self.grid_layout_widgets[effect][key].valueChanged.connect(
|
|
connecting_funcs[effect][key])
|
|
grid_layouts[effect].addWidget(QLabel(label),i,0)
|
|
grid_layouts[effect].addWidget(self.grid_layout_widgets[effect][key],i,1)
|
|
elif ui_element == "dropdown":
|
|
connecting_funcs[effect][key] = gen_combobox_valuechanger(effect, key)
|
|
self.grid_layout_widgets[effect][key] = QComboBox()
|
|
self.grid_layout_widgets[effect][key].addItems(opts.keys())
|
|
self.grid_layout_widgets[effect][key].currentIndexChanged.connect(
|
|
connecting_funcs[effect][key])
|
|
grid_layouts[effect].addWidget(QLabel(label),i,0)
|
|
grid_layouts[effect].addWidget(self.grid_layout_widgets[effect][key],i,1)
|
|
elif ui_element == "checkbox":
|
|
connecting_funcs[effect][key] = gen_checkbox_valuechanger(effect, key)
|
|
self.grid_layout_widgets[effect][key] = QCheckBox()
|
|
#self.grid_layout_widgets[effect][key].addItems(opts.keys())
|
|
self.grid_layout_widgets[effect][key].stateChanged.connect(
|
|
connecting_funcs[effect][key])
|
|
grid_layouts[effect].addWidget(QLabel(label),i,0)
|
|
grid_layouts[effect].addWidget(self.grid_layout_widgets[effect][key],i,1)
|
|
i += 1
|
|
#visualizer.effect_settings[effect]
|
|
else:
|
|
grid_layouts[effect].addWidget(QLabel("No customisable options for this effect :("),0,0)
|
|
|
|
|
|
|
|
# ============================================= Add layouts into wrapper
|
|
self.setLayout(wrapper)
|
|
wrapper.addLayout(labels_layout)
|
|
wrapper.addWidget(graph_view)
|
|
wrapper.addWidget(label_active)
|
|
wrapper.addLayout(button_grid)
|
|
wrapper.addWidget(label_slider)
|
|
wrapper.addWidget(freq_slider)
|
|
wrapper.addWidget(label_options)
|
|
wrapper.addWidget(opts_tabs)
|
|
self.show()
|
|
|
|
|
|
def frames_per_second():
|
|
"""Return the estimated frames per second
|
|
|
|
Returns the current estimate for frames-per-second (FPS).
|
|
FPS is estimated by measured the amount of time that has elapsed since
|
|
this function was previously called. The FPS estimate is low-pass filtered
|
|
to reduce noise.
|
|
|
|
This function is intended to be called one time for every iteration of
|
|
the program's main loop.
|
|
|
|
Returns
|
|
-------
|
|
fps : float
|
|
Estimated frames-per-second. This value is low-pass filtered
|
|
to reduce noise.
|
|
"""
|
|
global _time_prev, _fps
|
|
time_now = time.time() * 1000.0
|
|
dt = time_now - _time_prev
|
|
_time_prev = time_now
|
|
if dt == 0.0:
|
|
return _fps.value
|
|
return _fps.update(1000.0 / dt)
|
|
|
|
def memoize(function):
|
|
"""Provides a decorator for memoizing functions"""
|
|
from functools import wraps
|
|
memo = {}
|
|
|
|
@wraps(function)
|
|
def wrapper(*args):
|
|
if args in memo:
|
|
return memo[args]
|
|
else:
|
|
rv = function(*args)
|
|
memo[args] = rv
|
|
return rv
|
|
return wrapper
|
|
|
|
@memoize
|
|
def _normalized_linspace(size):
|
|
return np.linspace(0, 1, size)
|
|
|
|
def interpolate(y, new_length):
|
|
"""Intelligently resizes the array by linearly interpolating the values
|
|
|
|
Parameters
|
|
----------
|
|
y : np.array
|
|
Array that should be resized
|
|
|
|
new_length : int
|
|
The length of the new interpolated array
|
|
|
|
Returns
|
|
-------
|
|
z : np.array
|
|
New array with length of new_length that contains the interpolated
|
|
values of y.
|
|
"""
|
|
if len(y) == new_length:
|
|
return y
|
|
x_old = _normalized_linspace(len(y))
|
|
x_new = _normalized_linspace(new_length)
|
|
z = np.interp(x_new, x_old, y)
|
|
return z
|
|
|
|
def microphone_update(audio_samples):
|
|
global y_roll, prev_rms, prev_exp, prev_fps_update
|
|
# Normalize samples between 0 and 1
|
|
y = audio_samples / 2.0**15
|
|
# Construct a rolling window of audio samples
|
|
y_roll[:-1] = y_roll[1:]
|
|
y_roll[-1, :] = np.copy(y)
|
|
y_data = np.concatenate(y_roll, axis=0).astype(np.float32)
|
|
|
|
vol = np.max(np.abs(y_data))
|
|
if vol < config.MIN_VOLUME_THRESHOLD:
|
|
if config.USE_GUI:
|
|
gui.label_error.setText("No audio input. Volume below threshold.")
|
|
else:
|
|
print("No audio input. Volume below threshold. Volume: {}".format(vol))
|
|
visualizer.prev_output = np.multiply(visualizer.prev_output,0.95)
|
|
led.pixels = visualizer.prev_output
|
|
led.update()
|
|
else:
|
|
# Transform audio input into the frequency domain
|
|
N = len(y_data)
|
|
N_zeros = 2**int(np.ceil(np.log2(N))) - N
|
|
# Pad with zeros until the next power of two
|
|
y_data *= fft_window
|
|
y_padded = np.pad(y_data, (0, N_zeros), mode='constant')
|
|
YS = np.abs(np.fft.rfft(y_padded)[:N // 2])
|
|
# Construct a Mel filterbank from the FFT data
|
|
mel = np.atleast_2d(YS).T * dsp.mel_y.T
|
|
# Scale data to values more suitable for visualization
|
|
# mel = np.sum(mel, axis=0)
|
|
mel = np.sum(mel, axis=0)
|
|
mel = mel**2.0
|
|
# Gain normalization
|
|
mel_gain.update(np.max(gaussian_filter1d(mel, sigma=1.0)))
|
|
mel /= mel_gain.value
|
|
mel = mel_smoothing.update(mel)
|
|
# Map filterbank output onto LED strip
|
|
led.pixels = visualizer.get_vis(mel)
|
|
led.update()
|
|
if config.USE_GUI:
|
|
# Plot filterbank output
|
|
x = np.linspace(config.MIN_FREQUENCY, config.MAX_FREQUENCY, len(mel))
|
|
gui.mel_curve.setData(x=x, y=fft_plot_filter.update(mel))
|
|
gui.label_error.setText("")
|
|
if config.USE_GUI:
|
|
fps = frames_per_second()
|
|
if time.time() - 0.5 > prev_fps_update:
|
|
prev_fps_update = time.time()
|
|
app.processEvents()
|
|
# Plot the color channels
|
|
gui.r_curve.setData(y=led.pixels[0])
|
|
gui.g_curve.setData(y=led.pixels[1])
|
|
gui.b_curve.setData(y=led.pixels[2])
|
|
# Update fps counter
|
|
gui.label_fps.setText('{:.0f} / {:.0f} FPS'.format(fps, config.FPS))
|
|
if config.DISPLAY_FPS:
|
|
print('FPS {:.0f} / {:.0f}'.format(fps, config.FPS))
|
|
|
|
# Initialise visualiser and GUI
|
|
visualizer = Visualizer()
|
|
if config.USE_GUI:
|
|
# Create GUI window
|
|
app = QApplication([])
|
|
app.setApplicationName('Visualization')
|
|
gui = GUI()
|
|
app.processEvents()
|
|
|
|
# Initialise filter stuff
|
|
fft_plot_filter = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
|
|
alpha_decay=0.5, alpha_rise=0.99)
|
|
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
|
|
alpha_decay=0.01, alpha_rise=0.99)
|
|
mel_smoothing = dsp.ExpFilter(np.tile(1e-1, config.N_FFT_BINS),
|
|
alpha_decay=0.5, alpha_rise=0.99)
|
|
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
|
|
alpha_decay=0.02, alpha_rise=0.02)
|
|
fft_window = np.hamming(int(config.MIC_RATE / config.FPS) * config.N_ROLLING_HISTORY)
|
|
prev_fps_update = time.time()
|
|
|
|
# Initialise more filter stuff
|
|
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
|
|
alpha_decay=0.2, alpha_rise=0.99)
|
|
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
|
|
alpha_decay=0.05, alpha_rise=0.3)
|
|
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
|
|
alpha_decay=0.1, alpha_rise=0.5)
|
|
common_mode = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS // 2),
|
|
alpha_decay=0.99, alpha_rise=0.01)
|
|
p_filt = dsp.ExpFilter(np.tile(1, (3, config.N_PIXELS // 2)),
|
|
alpha_decay=0.1, alpha_rise=0.99)
|
|
p = np.tile(1.0, (3, config.N_PIXELS // 2))
|
|
gain = dsp.ExpFilter(np.tile(0.01, config.N_FFT_BINS),
|
|
alpha_decay=0.001, alpha_rise=0.99)
|
|
|
|
# The previous time that the frames_per_second() function was called
|
|
_time_prev = time.time() * 1000.0
|
|
# The low-pass filter used to estimate frames-per-second
|
|
_fps = dsp.ExpFilter(val=config.FPS, alpha_decay=0.2, alpha_rise=0.2)
|
|
|
|
|
|
# Number of audio samples to read every time frame
|
|
samples_per_frame = int(config.MIC_RATE / config.FPS)
|
|
# Array containing the rolling audio sample window
|
|
y_roll = np.random.rand(config.N_ROLLING_HISTORY, samples_per_frame) / 1e16
|
|
# Initialize LEDs
|
|
led.update()
|
|
# Start listening to live audio stream
|
|
microphone.start_stream(microphone_update)
|