Summarizing the questions I have:

1. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 111 in f033a74

   result[vv, i] = lifetime if lifetime < 1000 else 1000 # normalize long lifetimes to 1000

   
   How could you reach a lifetime of 1000? A value larger than 70 (in bins) is irrelevant and should be made an NA. Also, use np.min() instead of a ternary. Also also, I'm not sure how you're accessing result as a 2D array (i.e. with only 2 indices). Isn't result 3D? Not sure what is going on, I might need a debugger here.

2. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 123 in f033a74

   return 0.0

NA, not 0.0.

3. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 129 in f033a74

   lowest_bin_idx = hist[:bins_lowest_idx_upper_bound].argmin()

Why can't the smallest bin be after slot 65?

4. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 134 in f033a74

   with objmode(tau='float32'):

😱

5. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 137 in f033a74

   popt, _ = curve_fit(_exp_decay, x, sg_hist, p0=[max(sg_hist), 1/np.average(sg_hist), min(sg_hist)],

You're using many Python built-in functions here. It will be better if you use their numpy equivalents.

6. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 202 in f033a74

   np.zeros([Vdim, nbin.prod()], dtype=np.float32), values)

Start with a numpy array full of nans instead of zeros.

7. python-pysight/src/pysight/nd_hist_generator/binned_lifetime.py

   Line 223 in f033a74

   def _bin_edges(sample, bins=None, range=None):

Was this copied from scipy?