parameters: (freq1, freq2, delta_p, delta_s)
return_statement: return n
docstring: FIR bandpass filter length estimator.  freq1 and freq2 are
      normalized to the sampling frequency.  delta_p is the passband
      deviation (ripple), delta_s is the stopband deviation (ripple).
  
      From Mintzer and Liu (1979)
docstring_summary: FIR bandpass filter length estimator.  freq1 and freq2 are
      normalized to the sampling frequency.  delta_p is the passband
      deviation (ripple), delta_s is the stopband deviation (ripple).
docstring_tokens: FIR bandpass filter length estimator . freq1 and freq2 are normalized to the sampling frequency . delta_p is the passband deviation ( ripple ) delta_s is the stopband deviation ( ripple ) .
function: def bporder(freq1, freq2, delta_p, delta_s):
      '''
      FIR bandpass filter length estimator.  freq1 and freq2 are
      normalized to the sampling frequency.  delta_p is the passband
      deviation (ripple), delta_s is the stopband deviation (ripple).
  
      From Mintzer and Liu (1979)
      '''
      df = abs(freq2 - freq1)
      ddp = math.log10(delta_p)
      dds = math.log10(delta_s)
  
      a1 = 0.01201
      a2 = 0.09664
      a3 = -0.51325
      a4 = 0.00203
      a5 = -0.57054
      a6 = -0.44314
  
      t1 = a1 * ddp * ddp
      t2 = a2 * ddp
      t3 = a4 * ddp * ddp
      t4 = a5 * ddp
  
      cinf = dds * (t1 + t2 + a3) + t3 + t4 + a6
      ginf = -14.6 * math.log10(delta_p / delta_s) - 16.9
      n = cinf / df + ginf * df + 1
      return n
function_tokens: def bporder ( freq1 , freq2 , delta_p , delta_s ) : df = abs ( freq2 - freq1 ) ddp = math . log10 ( delta_p ) dds = math . log10 ( delta_s ) a1 = 0.01201 a2 = 0.09664 a3 = - 0.51325 a4 = 0.00203 a5 = - 0.57054 a6 = - 0.44314 t1 = a1 * ddp * ddp t2 = a2 * ddp t3 = a4 * ddp * ddp t4 = a5 * ddp cinf = dds * ( t1 + t2 + a3 ) + t3 + t4 + a6 ginf = - 14.6 * math . log10 ( delta_p / delta_s ) - 16.9 n = cinf / df + ginf * df + 1 return n