def compute_bin_edges()

in src/lookoutequipment/plot.py [0:0]

• 25 lines of code
• 13 McCabe index (conditional complexity)

def compute_bin_edges(signals, num_bins=10):
    """
    Computes aligned bin edges for all signals passed in argument
    
    Parameters:
        signals (array_like):
            An array holding the elements we want to compute histogram bins
            for. Could be two pandas.Series, numpy arrays, lists...
        num_bins (integer):
            Number of bins to compute (defaults to 10)
            
    Returns:
        list: a list of (num_bins + 1) edges that can be used to plot a
        histogram
    """
    # Checks if the argument is a nested type or a numeric one:
    if isinstance(signals[0], (int, float)):
        all_signals_min = np.min(signals)
        all_signals_max = np.max(signals)
        
    # For nested type (list of pandas.Series, list of lists...), we
    # need to compute the min and max of each component of the list:
    else:
        all_signals_max = None
        all_signals_min = None
        for s in signals:
            signal_max = np.max(s)
            if (all_signals_max is not None) and (signal_max > all_signals_max):
                all_signals_max = signal_max
            elif all_signals_max is None:
                all_signals_max = signal_max
                
            signal_min = np.min(s)
            if (all_signals_min is not None) and (signal_min < all_signals_min):
                all_signals_min = signal_min
            elif all_signals_min is None:
                all_signals_min = signal_min
        
    # Now we can compute the bin width and their edges:
    bin_width = (all_signals_max - all_signals_min)/num_bins
    bins = np.arange(
        all_signals_min, 
        all_signals_max + bin_width, 
        bin_width
    )
    
    return bins