3/3 (Source code used for analysis)

  import csv
  from collections import Counter
  from datetime import date, timedelta
  from itertools import accumulate, islice
  from matplotlib import pyplot as plt
  from scipy.stats import nchypergeom_wallenius
  from statistics import linear_regression
  
  def batched(iterable, n):
      # batched('ABCDEFG', 3) --> ABC DEF G
      if n < 1:
          raise ValueError('n must be at least one')
      it = iter(iterable)
      while batch := tuple(islice(it, n)):
          yield batch
  
  def dateab(a, b):
      # Lists dates from day a to day b.
      i = 0
      while (c := a + timedelta(days = i)) <= b:
          yield c.isoformat()
          i += 1
  
  def reader(file):
      with open(file, newline='', encoding='utf-8') as csvfile:
          for row in csv.reader(csvfile, delimiter=';', quotechar='"'):
              yield row[0:2]
  
  def sum_absdiff(iter1, iter2):
      # Compute absolute differences sum between vectors, as error measure.
      return sum(abs(x - y) for x, y in zip(iter1, iter2))
  
  days = list(dateab(date(2023, 10, 7), date(2024, 3, 1)))
  weeks = [e for e in batched(days, 7) if len(e) == 7]
  
  total_killings = [(d, int(n)) for d, n in islice(reader('gs_cummulative_total_killings.csv'), 1, None)]
  d = dict(total_killings)
  xs = list(max(d.setdefault(e, 0) for e in week) for week in weeks)
  
  journalist_killings = [t[0] for t in reader('gaza_strip_journalists_killed.csv')]
  c = Counter(journalist_killings)
  ys = list(accumulate(sum(c.setdefault(e, 0) for e in week) for week in weeks))
  
  slope, intercept = linear_regression(xs, ys, proportional=True)
  fitted = [slope * x + intercept for x in xs]
  print('Linear regression parameters: slope =', slope, ';', 'intercept =', intercept, '\n')
  
  
  M = 2375259    # 2022 population estimate (source: https://en.wikipedia.org/wiki/Gaza_Strip)
  ns = [i for i in range(1, 1200) if i % 5 == 0]
  odds = [i for i in range(1, 100) if i % 3 == 0]
  
  candidate_store = {}
  for n in ns:
      for odd in odds:
          L = []
          for x in xs:
              r = nchypergeom_wallenius(M, n, x, odd).mean()
              L.append(r)
          candidate_store[(n, odd)] = L[:]
  best_wallenius_fit = min(candidate_store, key= lambda e: sum_absdiff(candidate_store[e], ys))
  print('n, odds (best Wallenius fit):', best_wallenius_fit, '\n')
  top_20 = sorted(candidate_store, key= lambda e: sum_absdiff(candidate_store[e], ys))[:20]
  print('Top 20 (n, odds):', top_20, '\n')
  top_20_ns = [e[0] for e in top_20]
  top_20_odds = [e[1] for e in top_20]
  print('Top 20 n variation in range:', min(top_20_ns), '-', max(top_20_ns))
  print('Top 20 odds variation in range:', min(top_20_odds), '-', max(top_20_odds))
  
  fig, ax1 = plt.subplots()
  ax1.plot(xs, ys, label='Available data')
  ax1.plot(xs, fitted, label='Linear regression fit')
  ax1.plot(xs, candidate_store[best_wallenius_fit], label='Wallenius\' distribution fit')
  ax1.set_xlabel('General population (source: ochaopt.org)')
  ax1.set_ylabel('Journalists (source: wikipedia.org)')
  ax1.set_title('Cummulative killings by Israeli forces in Gaza Strip\n2023-10-07 to 2024-03-01')
  ax1.legend()
  plt.show()

Output (in addition to figure above):

  Python 3.11.8 (main, Feb  7 2024, 00:00:00) [GCC 13.2.1 20231011 (Red Hat 13.2.1-4)] on linux
  Type "help", "copyright", "credits" or "license()" for more information.
  
  ========== RESTART: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX ==========
  Linear regression parameters: slope = 0.00344597608865967 ; intercept = 0.0 
  
  n, odds (best Wallenius fit): (155, 75) 
  
  Top 20 (n, odds): [(155, 75), (160, 72), (145, 84), (165, 69), (170, 66), (150, 78), (175, 63), (140, 87), (135, 93), (150, 81), (185, 60), (140, 90), (180, 60), (190, 57), (195, 54), (200, 54), (135, 96), (180, 63), (205, 51), (185, 57)] 
  
  Top 20 n variation in range: 135 - 205
  Top 20 odds variation in range: 51 - 96