human
I am working on the file "S1_psd_mc_Erik.ipynb".
The first task is:
S1 model
Simulation
Can you write Python program for it?

What is a the Python program you requested?
assistant
#Here is the Python program you requested.

import numba

# def split_s1_groups(x, n_x, s1_min, s1_max):
#     """Splits x into groups with uniform(s1_min, s1_max) elements, then return matrix of histograms per group.
#     Returns: integer array (n_x, n_groups)
#     n_x: number of possible values in x. Assumed to be from 0 ... n_x - 1
#     s1_min: minimum S1 number of hits
#     s1_max: maximum S1 number of hits
#     """
#     # We want to exhaust the indices x. Simulate a generous amount of S1 sizes
#     n_s1_est = int(1.5 * 2 * len(x) / (s1_min + s1_max))
#     if 
#     hits_per_s1 = np.random.randint(s1_min, s1_max, size=n_s1_est)
    
#     result = np.zeros((n_x, n_s1_est), dtype=np.int)
#     s1_i = _split_s1_groups(x, hits_per_s1, result)
#     return result[:,:s1_i - 1]

# @numba.jit(nopython=True)
# def _split_s1_groups(x, hits_per_s1, result):
#     s1_i = 0
#     for i in x:
#         if hits_per_s1[s1_i] == 0:
#             s1_i += 1
#             continue
#         result[i, s1_i] += 1
#         hits_per_s1[s1_i] -= 1
#     return s1_i 

def split_s1_groups(x, n_x, areas, **params):
    """Splits x into groups with uniform (s1_min, s1_max) elements, then return matrix of histograms per group.
    Returns: integer array (n_x, n_groups)
    n_x: number of possible values in x. Assumed to be from 0 ... n_x - 1
    s1_min: minimum S1 number of hits
    s1_max: maximum S1 number of hits
    """
    params = get_params(params)
    # We want to exhaust the indices x. Simulate a generous amount of S1 sizes
    n_s1_est = int(1.5 * 2 * len(x) / (params['s1_min'] + params['s1_max']))
    
    if params['s1_sample'] == 'data' and 'xams_data' not in globals():
        print('Warning: data-derived s1 area distribution not possible, reverting to uniform...')
        params['s1_sample'] = 'uniform'
    if params['s1_sample'] == 'uniform':
        pe_per_s1 = (params['s1_max'] - params['s1_min']) * np.random.random(size=n_s1_est) + params['s1_min']
    elif params['s1_sample'] == 'data':
        # Take S1 from the data sample
        s1s_data =...