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Sensirion_SGP40_VOCAlgorithm.py
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Sensirion_SGP40_VOCAlgorithm.py
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VOCALGORITHM_SAMPLING_INTERVAL = (1.)
VOCALGORITHM_INITIAL_BLACKOUT = (45.)
VOCALGORITHM_VOC_INDEX_GAIN = (230.)
VOCALGORITHM_SRAW_STD_INITIAL = (50.)
VOCALGORITHM_SRAW_STD_BONUS = (220.)
VOCALGORITHM_TAU_MEAN_VARIANCE_HOURS = (12.)
VOCALGORITHM_TAU_INITIAL_MEAN = (20.)
VOCALGORITHM_INITI_DURATION_MEAN = (3600. * 0.75)
VOCALGORITHM_INITI_TRANSITION_MEAN = (0.01)
VOCALGORITHM_TAU_INITIAL_VARIANCE = (2500.)
VOCALGORITHM_INITI_DURATION_VARIANCE = ((3600. * 1.45))
VOCALGORITHM_INITI_TRANSITION_VARIANCE = (0.01)
VOCALGORITHM_GATING_THRESHOLD = (340.)
VOCALGORITHM_GATING_THRESHOLD_INITIAL = (510.)
VOCALGORITHM_GATING_THRESHOLD_TRANSITION = (0.09)
VOCALGORITHM_GATING_MAX_DURATION_MINUTES = ((60. * 3.))
VOCALGORITHM_GATING_MAX_RATIO = (0.3)
VOCALGORITHM_SIGMOID_L = (500.)
VOCALGORITHM_SIGMOID_K = (-0.0065)
VOCALGORITHM_SIGMOID_X0 = (213.)
VOCALGORITHM_VOC_INDEX_OFFSET_DEFAULT = (100.)
VOCALGORITHM_LP_TAU_FAST = (20.0)
VOCALGORITHM_LP_TAU_SLOW = (500.0)
VOCALGORITHM_LP_ALPHA = (-0.2)
VOCALGORITHM_PERSISTENCE_UPTIME_GAMMA = ((3. * 3600.))
VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING = (64.)
VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__FIX16_MAX = (32767.)
FIX16_MAXIMUM = 0x7FFFFFFF
FIX16_MINIMUM = 0x80000000
FIX16_OVERFLOW = 0x80000000
FIX16_ONE = 0x00010000
class Sensirion_vocalgorithmParams:
def __init__(self):
self.mvoc_index_offset = 0
self.mtau_mean_variance_hours = 0
self.mgating_max_duration_minutes = 0
self.msraw_std_initial=0
self.muptime=0
self.msraw=0
self.mvoc_index=0
self.m_mean_variance_estimator_gating_max_duration_minutes=0
self.m_mean_variance_estimator_initialized=0
self.m_mean_variance_estimator_mean=0
self.m_mean_variance_estimator_sraw_offset=0
self.m_mean_variance_estimator_std=0
self.m_mean_variance_estimator_gamma=0
self.m_mean_variance_estimator_gamma_initial_mean=0
self.m_mean_variance_estimator_gamma_initial_variance=0
self.m_mean_variance_estimator_gamma_mean=0
self.m_mean_variance_estimator__gamma_variance=0
self.m_mean_variance_estimator_uptime_gamma=0
self.m_mean_variance_estimator_uptime_gating=0
self.m_mean_variance_estimator_gating_duration_minutes=0
self.m_mean_variance_estimator_sigmoid_l=0
self.m_mean_variance_estimator_sigmoid_k=0
self.m_mean_variance_estimator_sigmoid_x0=0
self.m_mox_model_sraw_mean=0
self.m_sigmoid_scaled_offset=0
self.m_adaptive_lowpass_a1=0
self.m_adaptive_lowpass_a2=0
self.m_adaptive_lowpass_initialized=0
self.m_adaptive_lowpass_x1=0
self.m_adaptive_lowpass_x2=0
self.m_adaptive_lowpass_x3=0
class Sensirion_VOCAlgorithm:
def __init__(self):
self.params = Sensirion_vocalgorithmParams()
def _f16(self,x):
if x >= 0:
return int((x)*65536.0 + 0.5)
else:
return int((x)*65536.0 - 0.5)
def _fix16_from_int(self,a):
return int(a * FIX16_ONE)
def _fix16_cast_to_int(self,a):
return int(a) >> 16
def _fix16_mul(self,inarg0,inarg1):
inarg0=int(inarg0)
inarg1=int(inarg1)
A = (inarg0 >> 16)
if inarg0<0:
B = (inarg0&0xFFFFFFFF) & 0xFFFF
else:
B = inarg0&0xFFFF
C = (inarg1 >> 16)
if inarg1<0:
D = (inarg1&0xFFFFFFFF) & 0xFFFF
else:
D = inarg1&0xFFFF
AC = (A * C)
AD_CB = (A * D + C * B)
BD = (B * D)
product_hi = (AC + (AD_CB >> 16))
ad_cb_temp = ((AD_CB) << 16)&0xFFFFFFFF
product_lo = ((BD + ad_cb_temp))&0xFFFFFFFF
if product_lo < BD :
product_hi =product_hi+1
if ((product_hi >> 31) != (product_hi >>15)):
return FIX16_OVERFLOW
product_lo_tmp = product_lo&0xFFFFFFFF
product_lo = (product_lo - 0x8000)&0xFFFFFFFF
product_lo = (product_lo-((product_hi&0xFFFFFFFF) >> 31))&0xFFFFFFFF
if product_lo > product_lo_tmp:
product_hi = product_hi-1
result = (product_hi << 16)|(product_lo >> 16)
result +=1
return result
def _fix16_div(self,a, b):
a=int(a)
b=int(b)
if b==0 :
return FIX16_MINIMUM
if a>=0:
remainder = a
else:
remainder = (a*(-1))&0xFFFFFFFF
if b >= 0:
divider = b
else:
divider = (b*(-1))&0xFFFFFFFF
quotient = 0
bit =0x10000
while (divider < remainder):
divider = divider<<1
bit <<= 1
if not bit:
return FIX16_OVERFLOW
if (divider & 0x80000000):
if (remainder >= divider):
quotient |= bit
remainder -= divider
divider >>= 1
bit >>= 1
while bit and remainder:
if (remainder >= divider):
quotient |= bit
remainder -= divider
remainder <<= 1
bit >>= 1
if (remainder >= divider):
quotient+=1
result = quotient
if ((a ^ b) & 0x80000000):
if (result == FIX16_MINIMUM):
return FIX16_OVERFLOW
result = -result
return result
def _fix16_sqrt(self,x):
x=int(x)
num=x&0xFFFFFFFF
result = 0
bit = 1 << 30
while (bit > num):
bit >>=2
for n in range(0,2):
while (bit):
if (num >= result + bit):
num = num-(result + bit)&0xFFFFFFFF
result = (result >> 1) + bit
else:
result = (result >> 1)
bit >>= 2
if n==0:
if num > 65535:
num = (num -result)&0xFFFFFFFF
num = ((num << 16) - 0x8000)&0xFFFFFFFF
result = ((result << 16) + 0x8000)&0xFFFFFFFF
else:
num = ((num << 16)&0xFFFFFFFF)
result =((result << 16)&0xFFFFFFFF)
bit = 1 << 14
if (num > result):
result+=1
return result
def _fix16_exp(self,x):
x=int(x)
exp_pos_values=[self._f16(2.7182818), self._f16(1.1331485), self._f16(1.0157477), self._f16(1.0019550)]
exp_neg_values=[self._f16(0.3678794), self._f16(0.8824969), self._f16(0.9844964), self._f16(0.9980488)]
if (x >= self._f16(10.3972)):
return FIX16_MAXIMUM
if (x <= self._f16(-11.7835)):
return 0
if (x < 0):
x = -x
exp_values = exp_neg_values
else:
exp_values = exp_pos_values
res = FIX16_ONE
arg = FIX16_ONE
for i in range(0,4):
while (x >= arg):
res = self._fix16_mul(res, exp_values[i]);
x -= arg;
arg >>=3
return res
def vocalgorithm_init(self):
self.params.mvoc_index_offset = (self._f16(VOCALGORITHM_VOC_INDEX_OFFSET_DEFAULT))
self.params.mtau_mean_variance_hours = self._f16(VOCALGORITHM_TAU_MEAN_VARIANCE_HOURS)
self.params.mgating_max_duration_minutes =self._f16(VOCALGORITHM_GATING_MAX_DURATION_MINUTES)
self.params.msraw_std_initial = self._f16(VOCALGORITHM_SRAW_STD_INITIAL)
self.params.muptime = self._f16(0.)
self.params.msraw = self._f16(0.)
self.params.mvoc_index = 0
self._vocalgorithm__init_instances()
def _vocalgorithm__init_instances(self):
self._vocalgorithm__mean_variance_estimator__init()
self._vocalgorithm__mean_variance_estimator__set_parameters(self._f16(VOCALGORITHM_SRAW_STD_INITIAL), self.params.mtau_mean_variance_hours,self.params.mgating_max_duration_minutes)
self._vocalgorithm__mox_model__init()
self._vocalgorithm__mox_model__set_parameters(self._vocalgorithm__mean_variance_estimator__get_std(),self._vocalgorithm__mean_variance_estimator__get_mean())
self._vocalgorithm__sigmoid_scaled__init()
self._vocalgorithm__sigmoid_scaled__set_parameters(self.params.mvoc_index_offset)
self._vocalgorithm__adaptive_lowpass__init()
self._vocalgorithm__adaptive_lowpass__set_parameters()
def _vocalgorithm_get_states(self,state0,state1):
state0 = self._vocalgorithm__mean_variance_estimator__get_mean()
state1 = _vocalgorithm__mean_variance_estimator__get_std()
return state0,state1
def _vocalgorithm_set_states(self,state0,state1):
self._vocalgorithm__mean_variance_estimator__set_states(params, state0, state1, self._f16(VOCALGORITHM_PERSISTENCE_UPTIME_GAMMA))
self.params.msraw = state0
def _vocalgorithm_set_tuning_parameters(self, voc_index_offset, learning_time_hours, gating_max_duration_minutes, std_initial):
self.params.mvoc_index_offset = self._fix16_from_int(voc_index_offset)
self.params.mtau_mean_variance_hours = self._fix16_from_int(learning_time_hours)
self.params.mgating_max_duration_minutes =self._fix16_from_int(gating_max_duration_minutes)
self.params.msraw_std_initial = self._fix16_from_int(std_initial)
self._vocalgorithm__init_instances();
def vocalgorithm_process(self, sraw):
if ((self.params.muptime <= self._f16(VOCALGORITHM_INITIAL_BLACKOUT))):
self.params.muptime = self.params.muptime + self._f16(VOCALGORITHM_SAMPLING_INTERVAL)
else:
if (((sraw > 0) and (sraw < 65000))):
if ((sraw < 20001)):
sraw = 20001
elif((sraw > 52767)):
sraw = 52767
self.params.msraw = self._fix16_from_int((sraw - 20000))
self.params.mvoc_index =self._vocalgorithm__mox_model__process(self.params.msraw)
self.params.mvoc_index =self._vocalgorithm__sigmoid_scaled__process(self.params.mvoc_index)
self.params.mvoc_index =self._vocalgorithm__adaptive_lowpass__process(self.params.mvoc_index)
if ((self.params.mvoc_index < self._f16(0.5))):
self.params.mvoc_index = self._f16(0.5)
if self.params.msraw > self._f16(0.):
self._vocalgorithm__mean_variance_estimator__process(self.params.msraw, self.params.mvoc_index)
self._vocalgorithm__mox_model__set_parameters(self._vocalgorithm__mean_variance_estimator__get_std(),self._vocalgorithm__mean_variance_estimator__get_mean())
voc_index = self._fix16_cast_to_int((self.params.mvoc_index + self._f16(0.5)))
return voc_index
def _vocalgorithm__mean_variance_estimator__init(self):
self._vocalgorithm__mean_variance_estimator__set_parameters(self._f16(0.),self._f16(0.),self._f16(0.))
self._vocalgorithm__mean_variance_estimator___init_instances()
def _vocalgorithm__mean_variance_estimator___init_instances(self):
self._vocalgorithm__mean_variance_estimator___sigmoid__init()
def _vocalgorithm__mean_variance_estimator__set_parameters(self, std_initial, tau_mean_variance_hours, gating_max_duration_minutes):
self.params.m_mean_variance_estimator_gating_max_duration_minutes = gating_max_duration_minutes
self.params.m_mean_variance_estimator_initialized = 0
self.params.m_mean_variance_estimator_mean = self._f16(0.)
self.params.m_mean_variance_estimator_sraw_offset = self._f16(0.)
self.params.m_mean_variance_estimator_std = std_initial
self.params.m_mean_variance_estimator_gamma =self._fix16_div(self._f16((VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING *(VOCALGORITHM_SAMPLING_INTERVAL / 3600.))\
),(tau_mean_variance_hours +self._f16((VOCALGORITHM_SAMPLING_INTERVAL / 3600.))))
self.params.m_mean_variance_estimator_gamma_initial_mean =self._f16(((VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING *VOCALGORITHM_SAMPLING_INTERVAL) \
/(VOCALGORITHM_TAU_INITIAL_MEAN + VOCALGORITHM_SAMPLING_INTERVAL)))
self.params.m_mean_variance_estimator_gamma_initial_variance = self._f16(((VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING *VOCALGORITHM_SAMPLING_INTERVAL) \
/(VOCALGORITHM_TAU_INITIAL_VARIANCE + VOCALGORITHM_SAMPLING_INTERVAL)))
self.params.m_mean_variance_estimator_gamma_mean = self._f16(0.)
self.params.m_mean_variance_estimator__gamma_variance = self._f16(0.)
self.params.m_mean_variance_estimator_uptime_gamma = self._f16(0.)
self.params.m_mean_variance_estimator_uptime_gating = self._f16(0.)
self.params.m_mean_variance_estimator_gating_duration_minutes = self._f16(0.)
def _vocalgorithm__mean_variance_estimator__set_states(self, mean, std, uptime_gamma):
self.params.m_mean_variance_estimator_mean = mean
self.params.m_mean_variance_estimator_std = std
self.params.m_mean_variance_estimator_uptime_gamma = uptime_gamma
self.params.m_mean_variance_estimator_initialized = true
def _vocalgorithm__mean_variance_estimator__get_std(self):
return self.params.m_mean_variance_estimator_std
def _vocalgorithm__mean_variance_estimator__get_mean(self):
return (self.params.m_mean_variance_estimator_mean +self.params.m_mean_variance_estimator_sraw_offset)
def _vocalgorithm__mean_variance_estimator___calculate_gamma(self, voc_index_from_prior):
uptime_limit = self._f16((VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__FIX16_MAX -VOCALGORITHM_SAMPLING_INTERVAL))
if self.params.m_mean_variance_estimator_uptime_gamma < uptime_limit:
self.params.m_mean_variance_estimator_uptime_gamma =(self.params.m_mean_variance_estimator_uptime_gamma +self._f16(VOCALGORITHM_SAMPLING_INTERVAL))
if self.params.m_mean_variance_estimator_uptime_gating < uptime_limit:
self.params.m_mean_variance_estimator_uptime_gating =(self.params.m_mean_variance_estimator_uptime_gating +self._f16(VOCALGORITHM_SAMPLING_INTERVAL))
self._vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self._f16(1.), self._f16(VOCALGORITHM_INITI_DURATION_MEAN),self._f16(VOCALGORITHM_INITI_TRANSITION_MEAN))
sigmoid_gamma_mean =self._vocalgorithm__mean_variance_estimator___sigmoid__process(self.params.m_mean_variance_estimator_uptime_gamma)
gamma_mean =(self.params.m_mean_variance_estimator_gamma +(self._fix16_mul((self.params.m_mean_variance_estimator_gamma_initial_mean -self.params.m_mean_variance_estimator_gamma),sigmoid_gamma_mean)))
gating_threshold_mean =(self._f16(VOCALGORITHM_GATING_THRESHOLD)\
+(self._fix16_mul(self._f16((VOCALGORITHM_GATING_THRESHOLD_INITIAL -VOCALGORITHM_GATING_THRESHOLD)),\
self._vocalgorithm__mean_variance_estimator___sigmoid__process(self.params.m_mean_variance_estimator_uptime_gating))))
self._vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self._f16(1.),gating_threshold_mean,self._f16(VOCALGORITHM_GATING_THRESHOLD_TRANSITION))
sigmoid_gating_mean =self._vocalgorithm__mean_variance_estimator___sigmoid__process(voc_index_from_prior)
self.params.m_mean_variance_estimator_gamma_mean =(self._fix16_mul(sigmoid_gating_mean, gamma_mean))
self._vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self._f16(1.), self._f16(VOCALGORITHM_INITI_DURATION_VARIANCE),self._f16(VOCALGORITHM_INITI_TRANSITION_VARIANCE))
sigmoid_gamma_variance =self._vocalgorithm__mean_variance_estimator___sigmoid__process( self.params.m_mean_variance_estimator_uptime_gamma)
gamma_variance =(self.params.m_mean_variance_estimator_gamma +\
(self._fix16_mul((self.params.m_mean_variance_estimator_gamma_initial_variance \
-self.params.m_mean_variance_estimator_gamma),\
(sigmoid_gamma_variance - sigmoid_gamma_mean))))
gating_threshold_variance =(self._f16(VOCALGORITHM_GATING_THRESHOLD) \
+(self._fix16_mul(self._f16((VOCALGORITHM_GATING_THRESHOLD_INITIAL -VOCALGORITHM_GATING_THRESHOLD)),\
self._vocalgorithm__mean_variance_estimator___sigmoid__process( self.params.m_mean_variance_estimator_uptime_gating))))
self._vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self._f16(1.), gating_threshold_variance,self._f16(VOCALGORITHM_GATING_THRESHOLD_TRANSITION))
sigmoid_gating_variance =self._vocalgorithm__mean_variance_estimator___sigmoid__process( voc_index_from_prior)
self.params.m_mean_variance_estimator__gamma_variance =(self._fix16_mul(sigmoid_gating_variance, gamma_variance))
self.params.m_mean_variance_estimator_gating_duration_minutes =(self.params.m_mean_variance_estimator_gating_duration_minutes \
+(self._fix16_mul(self._f16((VOCALGORITHM_SAMPLING_INTERVAL / 60.)),\
((self._fix16_mul((self._f16(1.) - sigmoid_gating_mean),\
self._f16((1. + VOCALGORITHM_GATING_MAX_RATIO))))\
-self._f16(VOCALGORITHM_GATING_MAX_RATIO)))))
if ((self.params.m_mean_variance_estimator_gating_duration_minutes <self._f16(0.))):
self.params.m_mean_variance_estimator_gating_duration_minutes = self._f16(0.)
if ((self.params.m_mean_variance_estimator_gating_duration_minutes >self.params.m_mean_variance_estimator_gating_max_duration_minutes)):
self.params.m_mean_variance_estimator_uptime_gating = self._f16(0.)
def _vocalgorithm__mean_variance_estimator__process(self, sraw, voc_index_from_prior):
if ((self.params.m_mean_variance_estimator_initialized == 0)):
self.params.m_mean_variance_estimator_initialized = 1
self.params.m_mean_variance_estimator_sraw_offset = sraw
self.params.m_mean_variance_estimator_mean = self._f16(0.)
else:
if (((self.params.m_mean_variance_estimator_mean >= self._f16(100.)) or (self.params.m_mean_variance_estimator_mean <= self._f16(-100.)))):
self.params.m_mean_variance_estimator_sraw_offset =(self.params.m_mean_variance_estimator_sraw_offset +self.params.m_mean_variance_estimator_mean)
self.params.m_mean_variance_estimator_mean = self._f16(0.)
sraw = (sraw - self.params.m_mean_variance_estimator_sraw_offset)
self._vocalgorithm__mean_variance_estimator___calculate_gamma( voc_index_from_prior)
delta_sgp = (self._fix16_div((sraw - self.params.m_mean_variance_estimator_mean),self._f16(VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING)))
if ((delta_sgp < self._f16(0.))):
c = (self.params.m_mean_variance_estimator_std - delta_sgp)
else:
c = (self.params.m_mean_variance_estimator_std + delta_sgp)
additional_scaling = self._f16(1.)
if ((c > self._f16(1440.))):
additional_scaling = self._f16(4.)
self.params.m_mean_variance_estimator_std = self._fix16_mul(self._fix16_sqrt((self._fix16_mul(additional_scaling,\
(self._f16(VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING) -self.params.m_mean_variance_estimator__gamma_variance)))),\
self._fix16_sqrt(((self._fix16_mul(self.params.m_mean_variance_estimator_std,\
(self._fix16_div(self.params.m_mean_variance_estimator_std,\
(self._fix16_mul(self._f16(VOCALGORITHM_MEAN_VARIANCE_ESTIMATOR__GAMMA_SCALING),additional_scaling)))))) \
+(self._fix16_mul((self._fix16_div((self._fix16_mul(self.params.m_mean_variance_estimator__gamma_variance,delta_sgp)),additional_scaling))\
,delta_sgp)))))
self.params.m_mean_variance_estimator_mean =(self.params.m_mean_variance_estimator_mean +(self._fix16_mul(self.params.m_mean_variance_estimator_gamma_mean,delta_sgp)))
def _vocalgorithm__mean_variance_estimator___sigmoid__init(self):
self._vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self._f16(0.), self._f16(0.), self._f16(0.))
def _vocalgorithm__mean_variance_estimator___sigmoid__set_parameters(self, L, X0, K):
self.params.m_mean_variance_estimator_sigmoid_l = L;
self.params.m_mean_variance_estimator_sigmoid_k = K;
self.params.m_mean_variance_estimator_sigmoid_x0 = X0;
def _vocalgorithm__mean_variance_estimator___sigmoid__process(self, sample):
x = (self._fix16_mul(self.params.m_mean_variance_estimator_sigmoid_k,(sample - self.params.m_mean_variance_estimator_sigmoid_x0)))
if ((x < self._f16(-50.))):
return self.params.m_mean_variance_estimator_sigmoid_l
elif ((x > self._f16(50.))):
return self._f16(0.)
else:
return (self._fix16_div(self.params.m_mean_variance_estimator_sigmoid_l,(self._f16(1.) + self._fix16_exp(x))))
def _vocalgorithm__mox_model__init(self):
self._vocalgorithm__mox_model__set_parameters(self._f16(1.),self._f16(0.))
def _vocalgorithm__mox_model__set_parameters(self,SRAW_STD,SRAW_MEAN):
self.params.m_mox_model_sraw_std = SRAW_STD;
self.params.m_mox_model_sraw_mean = SRAW_MEAN;
def _vocalgorithm__mox_model__process(self,sraw):
return (self._fix16_mul((self._fix16_div((sraw - self.params.m_mox_model_sraw_mean),(-(self.params.m_mox_model_sraw_std +self._f16(VOCALGORITHM_SRAW_STD_BONUS))))),self._f16(VOCALGORITHM_VOC_INDEX_GAIN)))
def _vocalgorithm__sigmoid_scaled__init(self):
self._vocalgorithm__sigmoid_scaled__set_parameters(self._f16(0.))
def _vocalgorithm__sigmoid_scaled__set_parameters(self,offset):
self.params.m_sigmoid_scaled_offset = offset
def _vocalgorithm__sigmoid_scaled__process(self,sample):
x = (self._fix16_mul(self._f16(VOCALGORITHM_SIGMOID_K),(sample - self._f16(VOCALGORITHM_SIGMOID_X0))))
if ((x < self._f16(-50.))):
return self._f16(VOCALGORITHM_SIGMOID_L)
elif ((x > self._f16(50.))):
return self._f16(0.)
else:
if ((sample >= self._f16(0.))):
shift = (self._fix16_div((self._f16(VOCALGORITHM_SIGMOID_L) -(self._fix16_mul(self._f16(5.), self.params.m_sigmoid_scaled_offset))),self._f16(4.)))
return ((self._fix16_div((self._f16(VOCALGORITHM_SIGMOID_L) + shift),(self._f16(1.) + self._fix16_exp(x)))) -shift)
else:
return (self._fix16_mul((self._fix16_div(self.params.m_sigmoid_scaled_offset,self._f16(VOCALGORITHM_VOC_INDEX_OFFSET_DEFAULT))),\
(self._fix16_div(self._f16(VOCALGORITHM_SIGMOID_L),(self._f16(1.) + self._fix16_exp(x))))))
def _vocalgorithm__adaptive_lowpass__init(self):
self._vocalgorithm__adaptive_lowpass__set_parameters()
def _vocalgorithm__adaptive_lowpass__set_parameters(self):
self.params.m_adaptive_lowpass_a1 =self._f16((VOCALGORITHM_SAMPLING_INTERVAL /(VOCALGORITHM_LP_TAU_FAST + VOCALGORITHM_SAMPLING_INTERVAL)))
self.params.m_adaptive_lowpass_a2 =self._f16((VOCALGORITHM_SAMPLING_INTERVAL /(VOCALGORITHM_LP_TAU_SLOW + VOCALGORITHM_SAMPLING_INTERVAL)))
self.params.m_adaptive_lowpass_initialized = 0
def _vocalgorithm__adaptive_lowpass__process(self,sample):
if ((self.params.m_adaptive_lowpass_initialized == 0)):
self.params.m_adaptive_lowpass_x1 = sample;
self.params.m_adaptive_lowpass_x2 = sample;
self.params.m_adaptive_lowpass_x3 = sample;
self.params.m_adaptive_lowpass_initialized = 1;
self.params.m_adaptive_lowpass_x1 =((self._fix16_mul((self._f16(1.) - self.params.m_adaptive_lowpass_a1),self.params.m_adaptive_lowpass_x1)) +(self._fix16_mul(self.params.m_adaptive_lowpass_a1, sample)))
self.params.m_adaptive_lowpass_x2 =((self._fix16_mul((self._f16(1.) - self.params.m_adaptive_lowpass_a2),self.params.m_adaptive_lowpass_x2)) +(self._fix16_mul(self.params.m_adaptive_lowpass_a2, sample)))
abs_delta =(self.params.m_adaptive_lowpass_x1 - self.params.m_adaptive_lowpass_x2)
if ((abs_delta < self._f16(0.))):
abs_delta = (-abs_delta)
F1 = self._fix16_exp((self._fix16_mul(self._f16(VOCALGORITHM_LP_ALPHA), abs_delta)))
tau_a =((self._fix16_mul(self._f16((VOCALGORITHM_LP_TAU_SLOW - VOCALGORITHM_LP_TAU_FAST)),F1)) +self._f16(VOCALGORITHM_LP_TAU_FAST))
a3 = (self._fix16_div(self._f16(VOCALGORITHM_SAMPLING_INTERVAL),(self._f16(VOCALGORITHM_SAMPLING_INTERVAL) + tau_a)))
self.params.m_adaptive_lowpass_x3 =((self._fix16_mul((self._f16(1.) - a3), self.params.m_adaptive_lowpass_x3)) +(self._fix16_mul(a3, sample)))
return self.params.m_adaptive_lowpass_x3