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__init__.py
lookup_tables.py
resources.py
waveforms.py
lookup_tables.py
#!/usr/bin/python2.5 # # Copyright 2014 Olivier Gillet. # # Author: Olivier Gillet (ol.gillet@gmail.com) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # # See http://creativecommons.org/licenses/MIT/ for more information. # # ----------------------------------------------------------------------------- # # Lookup table definitions. import numpy lookup_tables = [] lookup_tables_32 = [] """---------------------------------------------------------------------------- Easing ----------------------------------------------------------------------------""" def BounceEaseIn(t, b, c, d): return c - BounceEaseOut(d - t, 0, c, d) + b def BounceEaseOut(t, b, c, d): t /= d if t < 1 / 2.75: return c * (7.5625 * t * t) + b elif t < 2 / 2.75: t -= 1.5/2.75 return c * (7.5625 * t * t + .75) + b elif t < 2.5 / 2.75: t -= 2.25 / 2.75 return c * (7.5625 * t * t + .9375) + b; else: t -= 2.625 / 2.75 return c * (7.5625 * t * t + .984375) + b; def BounceEaseInOut(t, b, c, d): if (t < d / 2): return BounceEaseIn(t * 2, 0, c, d) * 0.5 + b else: return BounceEaseOut(t * 2 - d, 0, c, d) * 0.5 + c * 0.5 + b x = numpy.arange(0, 1025.0) / 1024.0 steps = numpy.sign(x - 0.5) * 32767.5 + 32767.5 linear = x * 65535.0 quartic_in = (x ** 4) * 65535.0 quartic_out = (1 - (1 - x) ** 4) * 65535.0 in_out_sine = (1.0 - numpy.cos(x * numpy.pi)) / 2.0 * 65535.0 in_out_bounce = x + 0 for i in xrange(len(x)): in_out_bounce[i] = BounceEaseOut(x[i], 0, 65535.0, 1.0) # lookup_tables.append(('easing_steps', steps)) # lookup_tables.append(('easing_linear', linear)) lookup_tables.append(('easing_in_quartic', quartic_in)) lookup_tables.append(('easing_out_quartic',quartic_out)) lookup_tables.append(('easing_in_out_sine', in_out_sine)) lookup_tables.append(('easing_in_out_bounce', in_out_bounce)) """---------------------------------------------------------------------------- 2164 variable-skew normalization ----------------------------------------------------------------------------""" x = numpy.arange(0, 256.0) / 255.0 lookup_tables.append(('response_balance', numpy.round(32767 * (x ** 1.5)))) gain = numpy.linspace(1.0 / 4096, 1.0, 1025) voltage = 65535 / 2.5 * -2.0 / 3.0 * numpy.log10(gain) vca_linear = numpy.maximum(numpy.minimum(numpy.round(voltage), 65535), 0) lookup_tables.append(('vca_linear', vca_linear)) """---------------------------------------------------------------------------- Simple expo table for LED brightness adjustment ----------------------------------------------------------------------------""" x = numpy.arange(0, 256.0) / 255.0 expo = numpy.exp(-8.0 * (1.0 - x)) lookup_tables.append(('exponential', 65535 * expo)) """---------------------------------------------------------------------------- Phase increment lookup table for LFO mode ----------------------------------------------------------------------------""" frequency = 110 * 2 ** (numpy.arange(0, 159.0) / 158.0 - 13) phase_increment = frequency / 24000 * (1 << 32) lookup_tables_32 = [('increments', numpy.round(phase_increment).astype(int))]
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