python - Pandas/Numpy: Calculate current state series based on binary signals -

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I have 2 chains of binary "signal", let's call them "entry" and "steve".

Entry == 1 means adding from current to 1 (for some maximum time) and staying with == 0 to set the current position in 0. 

  Entry: 0 My code now calculates a combined current state:  
  State: 0 1 2 2 0 1   
 Currently, I use the following code, unfortunately, it is very slow (depending on the maximum time) (PDS time series in state / stay / entry):  
  State = copy.deepcopy (entry) state [stay == 0] = 0 # state first run [(entry.shift (1) == 1) & amp; (Stay == 1)] + = 1 # Maximum time (2, maximum_time + 1) second for the interval in the range: state [(entry.shift (lag) == 1) & amp; (Pd.rolling_mean (stay, interval) == 1)] + = 1   
 How to vector this code for better performance? thanks a lot! Finally, using some nimety functions, now found a solution:  
 
 
 
  def calc_state_series (insertion, stay, max_time = 5): reduce = (copy.deepcopy * entry) * 0) .filana (0) # to enter initalization ([entry.shift (max_time) == 1 ) & Amp; ; (Pd.rolling_mean (stay, max_time) == 1)] - = 1 Entry = (Entry + .shift (1)). Fill (0) #readuce condition max_time x = entry.values ​​x = np.concatenate (([0], x) y = stay.values ​​y = np.concatenate (([0], y)) Nan = y == 0x = np.array (x) x [nans] = 0 reset_idx = np .zeros (len (x), dtype = int) reset_idx [nans] = np.arange (len (x)) [ nans] reset_idx = np.maximum.accumulate (reset_idx) cumsum = np.cumsum (x) cumsum = cumsum - cumsum [reset_idx] returns pd.Series (cumsum [1:], index = entry.index)  < / Pre> 
 I manage to avoid the loop and this solution (depends on max_time) is still 100x faster for me - but probably still possible to optimize.

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