def filter_in_those_within_coincides_histogram_range(jogo, coincides_ranges, up_to_nDoConc=None, LOOKUP_DEPTH=1000):
'''
coincides_ranges is a histogram (or dict in Python parlance) that has the tuple min and max cutoff filter values for all n_coincides
An example of a coincides_ranges dict is cr below:
cr[0]=(500, 520) # means 0 repeats in LOOKUP_DEPTH games must not be lower than 500, nor higher than 520
cr[1]=(300, 400) # means 1 repeat in LOOKUP_DEPTH games must be within the range 300 to 400
cr[2]=( 80, 90) # similar explanation as above
cr[3]=( 35, 55) # similar explanation as above
cr[4]=( 5, 15) # similar explanation as above
cr[5]=( 0, 0) # similar explanation as below
cr[6]=( 0, 0) # means that 6 repeats (ie, equality) must not happen against all LOOKUP_DEPTH games
n_coincides, for Megasena for example, is the tuple (0,1,2,3,4,5,6), ie, any possible number of dozens that repeat between two games.
If n_coincides is 6, then, the two games are [[[ the same ]]].
If n_coincides is 5, then, one game has a "quina" in the other (and vice versa!)
eg. [ 1 2 3 4 5 6] and [ 1 2 3 4 5 7]
If n_coincides is 0, then, the two comparing games are fully different
eg. [ 1 2 3 4 5 6] and [ 7 8 9 10 11 12]
The LOOKUP_DEPTH determines how many games will be considered, down from up_to_nDoConc, to make up the coincides_histogram
This coincides_histogram is got from a "jogos function: get_coincides_histogram_against_a_lookup_depth()".
To further visualize the coincides_histogram, let ch (with LOOKUP_DEPTH=1000) look like:
ch[0]=518
ch[1]=345
ch[2]=82
ch[3]=42
ch[4]=13
ch[5]=0
ch[6]=0
Notice also that sum(ch.values()) must equal LOOKUP_DEPTH, ie. 518+345+82+42+13+0+0=1000
These values will be "contrasted" against coincides_ranges. Let's see a filter-in "passing" example:
Let coincides_ranges be cr below:
cr[0]=(500, 520) # seen above, 518 is within (500, 520), pass
cr[1]=(300, 400) # seen above, 345 is within (300, 400), pass
cr[2]=( 80, 90) # seen above, 82 is within ( 80, 90), pass
cr[3]=( 35, 55) # seen above, 42 is within ( 35, 55), pass
cr[4]=( 5, 15) # seen above, 15 is within ( 5, 15), pass
cr[5]=( 0, 0) # seen above, 0 is within ( 0, 0), pass
cr[6]=( 0, 0) # seen above, 0 is within ( 0, 0), pass
A filter-in "not passing" example, further developing the example above, is when ch has values outside the ranges given in cr.
'''
coincides_histogram = jogos_fd.get_coincides_histogram_against_a_lookup_depth(jogo, up_to_nDoConc, LOOKUP_DEPTH)
each_coincide_list = coincides_histogram.keys()
for n_coincide in each_coincide_list:
min_concursos_against_depth_with_n_coincides = coincides_ranges[n_coincide][0]
max_concursos_against_depth_with_n_coincides = coincides_ranges[n_coincide][1]
if coincides_histogram[n_coincide] < min_concursos_against_depth_with_n_coincides or coincides_histogram[n_coincide] > max_concursos_against_depth_with_n_coincides:
return False
return True
def report():
slider = ConcursoExt()
concurso = slider.get_last_concurso()
aggregated_histogram = {}
N_SORTEADAS = len(concurso.get_dezenas())
N_SORTEADAS_MAIS_NENHUMA_COINCIDENCIA = N_SORTEADAS + 1
for i in range(N_SORTEADAS_MAIS_NENHUMA_COINCIDENCIA):
aggregated_histogram[i] = []
for i in range(10):
print i, concurso.nDoConc, concurso.get_dezenas()
coincides_histogram = jogos_fd.get_coincides_histogram_against_a_lookup_depth(concurso.get_dezenas(), up_to_nDoConc=concurso.nDoConc-1, LOOKUP_DEPTH=1000)
if coincides_histogram == None:
continue
print coincides_histogram
the_n_coincides = coincides_histogram.values()
print the_n_coincides, sum(the_n_coincides)
for i, coincide_quantity in enumerate(the_n_coincides):
aggregated_histogram[i].append(coincide_quantity)
concurso = concurso.get_previous()
analyzer = AnalyzerOfCoincides(aggregated_histogram)
analyzer.analyze()
