original_docstring: 
      Return D = difficulty sequence,
      T = dilated mining time, emuT = emulation running time.
      
docstring: Return D = difficulty sequence,
  T = dilated mining time, emuT = emulation running time.
docstring_tokens: Return D = difficulty sequence T = dilated mining time emuT = emulation running time .
code: def iterativeDifficulty(numIters, hashRate, initDifficulty=1048576, tdf=1.0):
      """
      Return D = difficulty sequence,
      T = dilated mining time, emuT = emulation running time.
      """
      C_T = 1
      hashRateFactor = 1.0
      D = [initDifficulty]  #[0x400000000]
      BN = [1]
      T, runningT = [0], [0]
      noiseFactor = 0.1
      explicitHP = 1009 * hashRate
      implicitHP = 10 * hashRate
      log.debug(f'Initial D = {D[-1]}')
      log.debug(f'HP_E = {explicitHP}')
      log.debug(f'Initial D - HP_E = {D[-1] - explicitHP}')
      state = 1 if initDifficulty <= explicitHP else 2
      log.info(f'Initial state = {state}')
      for i in range(numIters):
          newT = D[-1] / hashRate * C_T
          # if len(T) > 0:
          #     newT += noiseFactor * np.random.uniform(min(T), max(T))
          # if i != 0 and i % 1000 == 0:
          #     hashRate *= hashRateFactor
  
          if 1 - (D[-1] / hashRate) // 10 >= -99:
              if state == 2:
                  log.info(f'State transition from 2 to 1 at step {i}')
                  state = 1
          else:
              if state == 1:
                  log.info(f'State transition from 1 to 2 at step {i}')
                  state = 2
  
          T.append(T[-1] + newT)
          runningT.append(runningT[-1] + newT * tdf)
          newD = D[-1] + D[-1] // 2048 * max(1 - (D[-1] / hashRate) // 10, -99)
          BN.append(int(2**(i // 1e5 - 2)))
          D.append(newD)
  
      log.info(f'Min difficulty = {np.min(D)}, Max difficulty = {np.max(D)}')
      log.info(f'HP_I = {implicitHP}')
      log.debug(f'Min D - HP_I = {np.min(D) - implicitHP}')
      log.debug(f'Max D - HP_I = {np.max(D) - implicitHP}')
      return D, T, runningT
code_tokens: def iterativeDifficulty ( numIters , hashRate , initDifficulty = 1048576 , tdf = 1.0 ) : C_T = 1 hashRateFactor = 1.0 D = [ initDifficulty ] BN = [ 1 ] T , runningT = [ 0 ] , [ 0 ] noiseFactor = 0.1 explicitHP = 1009 * hashRate implicitHP = 10 * hashRate log . debug ( f'Initial D = {D[-1]}' ) log . debug ( f'HP_E = {explicitHP}' ) log . debug ( f'Initial D - HP_E = {D[-1] - explicitHP}' ) state = 1 if initDifficulty <= explicitHP else 2 log . info ( f'Initial state = {state}' ) for i in range ( numIters ) : newT = D [ - 1 ] / hashRate * C_T if 1 - ( D [ - 1 ] / hashRate ) // 10 >= - 99 : if state == 2 : log . info ( f'State transition from 2 to 1 at step {i}' ) state = 1 else : if state == 1 : log . info ( f'State transition from 1 to 2 at step {i}' ) state = 2 T . append ( T [ - 1 ] + newT ) runningT . append ( runningT [ - 1 ] + newT * tdf ) newD = D [ - 1 ] + D [ - 1 ] // 2048 * max ( 1 - ( D [ - 1 ] / hashRate ) // 10 , - 99 ) BN . append ( int ( 2 ** ( i // 1e5 - 2 ) ) ) D . append ( newD ) log . info ( f'Min difficulty = {np.min(D)}, Max difficulty = {np.max(D)}' ) log . info ( f'HP_I = {implicitHP}' ) log . debug ( f'Min D - HP_I = {np.min(D) - implicitHP}' ) log . debug ( f'Max D - HP_I = {np.max(D) - implicitHP}' ) return D , T , runningT
short_docstring: Return D = difficulty sequence,
  T = dilated mining time, emuT = emulation running time.
short_docstring_tokens: Return D = difficulty sequence T = dilated mining time emuT = emulation running time .
comment: """
      Return D = difficulty sequence,
      T = dilated mining time, emuT = emulation running time.
      """$SEP$#[0x400000000]$SEP$# if len(T) > 0:$SEP$#     newT += noiseFactor * np.random.uniform(min(T), max(T))$SEP$# if i != 0 and i % 1000 == 0:$SEP$#     hashRate *= hashRateFactor