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Choose a better colourmap

For plot 2 the colourmap is rainbow, which isn't colourblind safe.

count_stars_bins in tidy up function?

count_stars_bins may also need to be put through the tidy up function. Check it over, I may be remembering it wrong.

VSAT/calc_drdv_and_sort.py

Lines 96 to 148 in 77d3c3f

    
           def tidy_up(n_bins, edges, mean_dv, error, n_in_bins): 
        
               # Get rid of high dv bins. 
        
               # Define high dr as the dr beyond which the average number of pairs per 
        
               # bin is < 10. Find that cutoff. 
        
               for i in range(n_bins): 
        
                   # Get the average number of pairs per bin for bins with dr >= this one 
        
                   av_numb_in_bins = sum(n_in_bins[i:])/float(n_bins - i) 
        
                   # It the average number of pairs per bin is < 10 then the bins 
        
                   # beyond this point are likely noisy and unreliable 
        
                   # Therefore want to cut any bins beyond this cutoff point 
        
                   if av_numb_in_bins < 10: 
        
                       cutoff_bin = i 
        
                       break 
        
               # Get rid of the bins above the cutoff bin 
        
               edges = edges[:cutoff_bin] 
        
               mean_dv = mean_dv[:cutoff_bin] 
        
               error = error[:cutoff_bin] 
        
               n_in_bins = n_in_bins[:cutoff_bin] 
        
               # The number of bins has changed now the outermost have been cut. 
        
               # Recalculate. 
        
               n_bins = len(mean_dv) 
        
               # - - - - - - - - - - - - - - - - - 
        
               # Get rid of any empty bins. 
        
               # Figure out which bins to remove 
        
               # Reversing the order just makes it easier to delete them without 
        
               # screwing up the indecies 
        
               remove_list = [i for i in range(n_bins) if n_in_bins[i] == 0.] 
        
               remove_list.reverse() 
        
               # Remove the empty bins 
        
               for empty in remove_list: 
        
                   edges = np.delete(edges, empty) 
        
                   mean_dv = np.delete(mean_dv, empty) 
        
                   error = np.delete(error, empty) 
        
                   n_in_bins = np.delete(n_in_bins, empty) 
        
               # The number of bins has changed now the empty ones have been 
        
               # deleted. Recalculate. 
        
               n_bins = len(edges) 
        
               return n_bins, edges, mean_dv, error, n_in_bins

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