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September 18, 2016 07:23
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fft node
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# ##### BEGIN GPL LICENSE BLOCK ##### | |
# | |
# This program is free software; you can redistribute it and/or | |
# modify it under the terms of the GNU General Public License | |
# as published by the Free Software Foundation; either version 2 | |
# of the License, or (at your option) any later version. | |
# | |
# This program is distributed in the hope that it will be useful, | |
# but WITHOUT ANY WARRANTY; without even the implied warranty of | |
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
# GNU General Public License for more details. | |
# | |
# You should have received a copy of the GNU General Public License | |
# along with this program; if not, write to the Free Software Foundation, | |
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. | |
# | |
# ##### END GPL LICENSE BLOCK ##### | |
import pyaudio | |
import numpy as np | |
import bpy | |
from bpy.types import Operator | |
from bpy.props import BoolProperty | |
from sverchok.node_tree import SverchCustomTreeNode | |
class SpectrumAnalyzer: | |
FORMAT = pyaudio.paFloat32 | |
CHANNELS = 1 | |
RATE = 16000 | |
CHUNK = 512 | |
START = 0 | |
N = 512 | |
wave_x = 0 | |
wave_y = 0 | |
spec_x = 0 | |
spec_y = 0 | |
data = [] | |
def __init__(self): | |
self.pa = pyaudio.PyAudio() | |
self.open_dataframe() | |
def open_dataframe(self): | |
self.stream = self.pa.open( | |
format=self.FORMAT, | |
channels=self.CHANNELS, | |
rate=self.RATE, | |
input=True, | |
output=False, | |
frames_per_buffer=self.CHUNK | |
) | |
def do_dataframe(self): | |
self.data = self.audioinput() | |
self.fft() | |
# return self.wave_x, self.wave_y, self.N, self.spec_x, self.spec_y, self.RATE | |
return self.wave_x, self.wave_y, self.spec_x, self.spec_y | |
def end_updates(self): | |
self.pa.close(stream=self.stream) | |
def audioinput(self): | |
ret = self.stream.read(self.CHUNK) | |
ret = np.fromstring(ret, np.float32) | |
return ret | |
def fft(self): | |
self.wave_x = range(self.START, self.START + self.N) | |
self.wave_y = self.data[self.START:self.START + self.N] | |
self.spec_x = np.fft.fftfreq(self.N, d=1.0 / self.RATE) | |
y = np.fft.fft(self.data[self.START:self.START + self.N]) | |
self.spec_y = [np.sqrt(c.real ** 2 + c.imag ** 2) for c in y] | |
class SvFFTCallback(Operator): | |
bl_idname = "node.fft_callback" | |
bl_label = "Short Name" | |
fn_name = bpy.props.StringProperty(default='') | |
def dispatch(self, context, type_op): | |
n = context.node | |
if type_op == 'on': | |
n.active = True | |
wik = SpectrumAnalyzer() | |
n.node_dict[hash(n)] = {'FFT': wik} | |
wik.open_dataframe() | |
elif type_op == 'off': | |
n.active = False | |
n.end_updates() | |
def execute(self, context): | |
self.dispatch(context, self.fn_name) | |
return {'FINISHED'} | |
class SvFFTClientNode(bpy.types.Node, SverchCustomTreeNode): | |
bl_idname = 'SvFFTClientNode' | |
bl_label = 'FFT Client' | |
active = BoolProperty(default=False, name='Active') | |
node_dict = {} | |
def draw_buttons(self, context, layout): | |
state = 'on' if not self.active else 'off' | |
layout.operator('node.fft_callback', text=state).fn_name = state | |
def sv_init(self, context): | |
self.inputs.new('StringsSocket', 'frame') | |
self.outputs.new('StringsSocket', 'wave_x') | |
self.outputs.new('StringsSocket', 'wave_y') | |
self.outputs.new('StringsSocket', 'spec_x') | |
self.outputs.new('StringsSocket', 'spec_y') | |
def process(self): | |
if not self.active: | |
return | |
data = None | |
input_value = self.inputs[0].sv_get() | |
if input_value: | |
current_node_dict = self.node_dict.get(hash(self)) | |
if current_node_dict: | |
# at this point FFT must be present.. | |
wik = self.node_dict[hash(self)].get('FFT') | |
data = wik.do_dataframe() | |
print('updated') | |
else: | |
print('failed?') | |
self.active = not self.active | |
return | |
outputs = self.outputs | |
for idx, sock in enumerate('wave_x wave_y spec_x spec_y'.split(' ')): | |
if outputs[sock].is_linked: | |
outputs[sock].sv_set([data[idx]]) | |
def end_updates(self): | |
wik = self.node_dict[hash(self)].get('FFT') | |
if wik: | |
wik.end_updates() | |
def register(): | |
bpy.utils.register_class(SvFFTCallback) | |
bpy.utils.register_class(SvFFTClientNode) | |
def unregister(): | |
bpy.utils.unregister_class(SvFFTClientNode) | |
bpy.utils.unregister_class(SvFFTCallback) | |
if __name__ == "__main__": | |
register() |
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Zeff, I want to try this FFT. My setup using Processing and OSC introduce delay. And with this, is it simple enough to read the array and pass it to Sverchok?
Seems to fail when I tried installing PyAudio. So it's on halt.