Ausgabe DieHarder auf 11,6 GiByte Zufallsdaten der Rev. K

Diese Seite zeigt die Ausgabe der DieHarder Testsuite für Zufallszahlen:

#=============================================================================#
#            dieharder version 3.31.1 Copyright 2003 Robert G. Brown          #
#=============================================================================#
   rng_name    |rands/second|   Seed   |
stdin_input_raw|  3.78e+02  |1911919715|

#==================================================================
#                Diehard "Birthdays" test (modified).
# Each test determines the number of matching intervals from 512
# "birthdays" (by default) drawn on a 24-bit "year" (by
# default).  This is repeated 100 times (by default) and the
# results cumulated in a histogram.  Repeated intervals should be
# distributed in a Poisson distribution if the underlying generator
# is random enough, and a a chisq and p-value for the test are
# evaluated relative to this null hypothesis.
#
# It is recommended that you run this at or near the original
# 100 test samples per p-value with -t 100.
#
# Two additional parameters have been added. In diehard, nms=512
# but this CAN be varied and all Marsaglia's formulae still work.  It
# can be reset to different values with -x nmsvalue.
# Similarly, nbits "should" 24, but we can really make it anything
# we want that's less than or equal to rmax_bits = 32.  It can be
# reset to a new value with -y nbits.  Both default to diehard's
# values if no -x or -y options are used.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     12|    |    |    |    |    |****|    |    |    |    |
#       |    |****|    |****|    |****|****|    |****|****|
#     10|****|****|    |****|****|****|****|****|****|****|
#       |****|****|    |****|****|****|****|****|****|****|
#      8|****|****|    |****|****|****|****|****|****|****|
#       |****|****|    |****|****|****|****|****|****|****|
#      6|****|****|    |****|****|****|****|****|****|****|
#       |****|****|    |****|****|****|****|****|****|****|
#      4|****|****|    |****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
   diehard_birthdays|   0|       100|     100|0.68060996|  PASSED  

#==================================================================
#          Diehard Overlapping 5-Permutations Test.
# This is the OPERM5 test.  It looks at a sequence of one mill- 
# ion 32-bit random integers.  Each set of five consecutive     
# integers can be in one of 120 states, for the 5! possible or- 
# derings of five numbers.  Thus the 5th, 6th, 7th,...numbers   
# each provide a state. As many thousands of state transitions  
# are observed,  cumulative counts are made of the number of    
# occurences of each state.  Then the quadratic form in the     
# weak inverse of the 120x120 covariance matrix yields a test   
# equivalent to the likelihood ratio test that the 120 cell     
# counts came from the specified (asymptotically) normal dis-   
# tribution with the specified 120x120 covariance matrix (with  
# rank 99).  This version uses 1,000,000 integers, twice.       
#
# Note that Dieharder runs the test 100 times, not twice, by
# default.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |****|    |    |
#     12|****|    |    |    |    |    |    |****|    |    |
#       |****|****|    |    |    |    |    |****|    |    |
#     10|****|****|****|    |    |****|    |****|****|****|
#       |****|****|****|    |    |****|    |****|****|****|
#      8|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
      diehard_operm5|   0|   1000000|     100|0.96288413|  PASSED  

#==================================================================
#                Diehard 32x32 Binary Rank Test
# This is the BINARY RANK TEST for 32x32 matrices. A random 32x
# 32 binary matrix is formed, each row a 32-bit random integer.
# The rank is determined. That rank can be from 0 to 32, ranks
# less than 29 are rare, and their counts are pooled with those
# for rank 29.  Ranks are found for 40,000 such random matrices
# and a chisquare test is performed on counts for ranks  32,31,
# 30 and <=29.
#
# As always, the test is repeated and a KS test applied to the
# resulting p-values to verify that they are approximately uniform.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |****|
#     14|    |    |    |    |    |    |    |    |    |****|
#       |****|    |    |    |    |    |    |    |    |****|
#     12|****|    |    |    |    |    |    |    |    |****|
#       |****|    |****|****|    |****|****|    |    |****|
#     10|****|    |****|****|    |****|****|    |    |****|
#       |****|    |****|****|    |****|****|    |    |****|
#      8|****|    |****|****|****|****|****|    |    |****|
#       |****|****|****|****|****|****|****|****|    |****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
  diehard_rank_32x32|   0|     40000|     100|0.72396526|  PASSED  

#==================================================================
#              Diehard 6x8 Binary Rank Test
# This is the BINARY RANK TEST for 6x8 matrices.  From each of
# six random 32-bit integers from the generator under test, a
# specified byte is chosen, and the resulting six bytes form a
# 6x8 binary matrix whose rank is determined.  That rank can be
# from 0 to 6, but ranks 0,1,2,3 are rare; their counts are
# pooled with those for rank 4. Ranks are found for 100,000
# random matrices, and a chi-square test is performed on
# counts for ranks 6,5 and <=4.
#
# As always, the test is repeated and a KS test applied to the
# resulting p-values to verify that they are approximately uniform.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |****|
#       |    |    |    |    |    |    |    |    |    |****|
#     14|    |    |    |    |    |    |    |    |    |****|
#       |    |****|    |****|    |    |    |    |    |****|
#     12|    |****|    |****|    |    |    |    |    |****|
#       |    |****|    |****|    |    |****|    |    |****|
#     10|    |****|****|****|    |    |****|    |    |****|
#       |    |****|****|****|    |****|****|    |****|****|
#      8|****|****|****|****|    |****|****|    |****|****|
#       |****|****|****|****|    |****|****|****|****|****|
#      6|****|****|****|****|    |****|****|****|****|****|
#       |****|****|****|****|    |****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
    diehard_rank_6x8|   0|    100000|     100|0.72737480|  PASSED  

#==================================================================
#                  Diehard Bitstream Test.
# The file under test is viewed as a stream of bits. Call them  
# b1,b2,... .  Consider an alphabet with two "letters", 0 and 1 
# and think of the stream of bits as a succession of 20-letter  
# "words", overlapping.  Thus the first word is b1b2...b20, the 
# second is b2b3...b21, and so on.  The bitstream test counts   
# the number of missing 20-letter (20-bit) words in a string of 
# 2^21 overlapping 20-letter words.  There are 2^20 possible 20 
# letter words.  For a truly random string of 2^21+19 bits, the 
# number of missing words j should be (very close to) normally  
# distributed with mean 141,909 and sigma 428.  Thus            
# (j-141909)/428 should be a standard normal variate (z score) 
# that leads to a uniform [0,1) p value.  The test is repeated  
# twenty times.                                                 
#
#                         NOTE WELL!
#
# The test is repeated 100 times by default in dieharder, but the
# size of the sample is fixed (tsamples cannot/should not be
# varied from the default).  The sigma of this test REQUIRES the
# use of overlapping samples, and overlapping samples are not
# independent.  If one uses the non-overlapping version of this
# test, sigma = 290 is used instead, smaller because now there
# are 2^21 INDEPENDENT samples.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |****|    |
#     16|    |    |    |    |    |    |    |    |****|    |
#       |    |    |    |    |    |    |    |    |****|    |
#     14|    |    |    |    |    |    |    |****|****|    |
#       |****|****|    |    |    |    |    |****|****|    |
#     12|****|****|    |    |    |    |    |****|****|    |
#       |****|****|    |    |    |****|    |****|****|    |
#     10|****|****|    |    |    |****|    |****|****|    |
#       |****|****|    |****|    |****|    |****|****|    |
#      8|****|****|****|****|    |****|    |****|****|    |
#       |****|****|****|****|    |****|    |****|****|    |
#      6|****|****|****|****|****|****|    |****|****|    |
#       |****|****|****|****|****|****|****|****|****|    |
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
   diehard_bitstream|   0|   2097152|     100|0.74238785|  PASSED  
#==================================================================
#        Diehard Overlapping Pairs Sparse Occupance (OPSO)
# The OPSO test considers 2-letter words from an alphabet of    
# 1024 letters.  Each letter is determined by a specified ten   
# bits from a 32-bit integer in the sequence to be tested. OPSO 
# generates  2^21 (overlapping) 2-letter words  (from 2^21+1    
# "keystrokes")  and counts the number of missing words---that  
# is 2-letter words which do not appear in the entire sequence. 
# That count should be very close to normally distributed with  
# mean 141,909, sigma 290. Thus (missingwrds-141909)/290 should 
# be a standard normal variable. The OPSO test takes 32 bits at 
# a time from the test file and uses a designated set of ten    
# consecutive bits. It then restarts the file for the next de-  
# signated 10 bits, and so on.                                  
# 
#  Note 2^21 = 2097152, tsamples cannot be varied.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |****|    |
#     14|    |    |    |    |    |    |    |    |****|    |
#       |    |    |****|    |****|    |    |    |****|    |
#     12|    |    |****|    |****|    |    |    |****|    |
#       |    |    |****|    |****|    |****|    |****|    |
#     10|    |    |****|****|****|    |****|    |****|    |
#       |    |****|****|****|****|****|****|    |****|    |
#      8|    |****|****|****|****|****|****|    |****|    |
#       |    |****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_opso|   0|   2097152|     100|0.84058352|  PASSED  
#==================================================================
#   Diehard Overlapping Quadruples Sparce Occupancy (OQSO) Test
#
#  Similar, to OPSO except that it considers 4-letter 
#  words from an alphabet of 32 letters, each letter determined  
#  by a designated string of 5 consecutive bits from the test    
#  file, elements of which are assumed 32-bit random integers.   
#  The mean number of missing words in a sequence of 2^21 four-  
#  letter words,  (2^21+3 "keystrokes"), is again 141909, with   
#  sigma = 295.  The mean is based on theory; sigma comes from   
#  extensive simulation.                                         
# 
#  Note 2^21 = 2097152, tsamples cannot be varied.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |    |    |    |    |
#       |    |    |****|    |    |****|    |    |    |    |
#     12|    |    |****|    |    |****|    |    |    |    |
#       |****|    |****|****|    |****|    |    |    |    |
#     10|****|    |****|****|    |****|    |****|****|    |
#       |****|    |****|****|****|****|    |****|****|****|
#      8|****|****|****|****|****|****|    |****|****|****|
#       |****|****|****|****|****|****|    |****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_oqso|   0|   2097152|     100|0.91553991|  PASSED  
#==================================================================
#                    Diehard DNA Test.
# 
#   The DNA test considers an alphabet of 4 letters::  C,G,A,T,
# determined by two designated bits in the sequence of random   
# integers being tested.  It considers 10-letter words, so that 
# as in OPSO and OQSO, there are 2^20 possible words, and the   
# mean number of missing words from a string of 2^21  (over-    
# lapping)  10-letter  words (2^21+9 "keystrokes") is 141909.   
# The standard deviation sigma=339 was determined as for OQSO   
# by simulation.  (Sigma for OPSO, 290, is the true value (to   
# three places), not determined by simulation.                  
# 
# Note 2^21 = 2097152
# Note also that we don't bother with overlapping keystrokes 
# (and sample more rands -- rands are now cheap). 
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |****|    |    |    |
#     16|    |    |    |    |    |    |****|    |    |    |
#       |    |    |    |    |    |    |****|****|    |    |
#     14|    |    |    |    |    |****|****|****|    |    |
#       |    |    |    |    |    |****|****|****|    |    |
#     12|    |    |    |    |    |****|****|****|    |    |
#       |    |    |    |    |    |****|****|****|    |    |
#     10|    |    |****|    |****|****|****|****|    |    |
#       |    |    |****|****|****|****|****|****|    |****|
#      8|    |****|****|****|****|****|****|****|    |****|
#       |    |****|****|****|****|****|****|****|    |****|
#      6|    |****|****|****|****|****|****|****|    |****|
#       |    |****|****|****|****|****|****|****|    |****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
         diehard_dna|   0|   2097152|     100|0.21516088|  PASSED  
#==================================================================
#          Diehard Count the 1s (stream) (modified) Test.
# Consider the file under test as a stream of bytes (four per   
# 32 bit integer).  Each byte can contain from 0 to 8 1's,      
# with probabilities 1,8,28,56,70,56,28,8,1 over 256.  Now let  
# the stream of bytes provide a string of overlapping  5-letter 
# words, each "letter" taking values A,B,C,D,E. The letters are 
# determined by the number of 1's in a byte::  0,1,or 2 yield A,
# 3 yields B, 4 yields C, 5 yields D and 6,7 or 8 yield E. Thus 
# we have a monkey at a typewriter hitting five keys with vari- 
# ous probabilities (37,56,70,56,37 over 256).  There are 5^5   
# possible 5-letter words, and from a string of 256,000 (over-  
# lapping) 5-letter words, counts are made on the frequencies   
# for each word.   The quadratic form in the weak inverse of    
# the covariance matrix of the cell counts provides a chisquare 
# test::  Q5-Q4, the difference of the naive Pearson sums of    
# (OBS-EXP)^2/EXP on counts for 5- and 4-letter cell counts.    
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |****|    |    |****|    |
#       |    |    |    |    |    |****|    |    |****|****|
#     12|    |    |    |    |    |****|    |    |****|****|
#       |    |****|    |    |    |****|    |    |****|****|
#     10|    |****|    |    |****|****|    |    |****|****|
#       |    |****|****|****|****|****|    |****|****|****|
#      8|    |****|****|****|****|****|    |****|****|****|
#       |    |****|****|****|****|****|****|****|****|****|
#      6|    |****|****|****|****|****|****|****|****|****|
#       |    |****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
diehard_count_1s_str|   0|    256000|     100|0.51753616|  PASSED  
#==================================================================
#         Diehard Count the 1s Test (byte) (modified).
#     This is the COUNT-THE-1's TEST for specific bytes.        
# Consider the file under test as a stream of 32-bit integers.  
# From each integer, a specific byte is chosen , say the left-  
# most::  bits 1 to 8. Each byte can contain from 0 to 8 1's,   
# with probabilitie 1,8,28,56,70,56,28,8,1 over 256.  Now let   
# the specified bytes from successive integers provide a string 
# of (overlapping) 5-letter words, each "letter" taking values  
# A,B,C,D,E. The letters are determined  by the number of 1's,  
# in that byte::  0,1,or 2 ---> A, 3 ---> B, 4 ---> C, 5 ---> D,
# and  6,7 or 8 ---> E.  Thus we have a monkey at a typewriter  
# hitting five keys with with various probabilities::  37,56,70,
# 56,37 over 256. There are 5^5 possible 5-letter words, and    
# from a string of 256,000 (overlapping) 5-letter words, counts 
# are made on the frequencies for each word. The quadratic form 
# in the weak inverse of the covariance matrix of the cell      
# counts provides a chisquare test::  Q5-Q4, the difference of  
# the naive Pearson  sums of (OBS-EXP)^2/EXP on counts for 5-   
# and 4-letter cell counts.                                     
# 
# Note: We actually cycle samples over all 0-31 bit offsets, so 
# that if there is a problem with any particular offset it has 
# a chance of being observed.  One can imagine problems with odd 
# offsets but not even, for example, or only with the offset 7.
# tsamples and psamples can be freely varied, but you'll likely 
# need tsamples >> 100,000 to have enough to get a reliable kstest 
# result. 
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |****|    |    |    |    |    |    |    |
#       |    |****|****|    |    |    |    |    |    |    |
#     12|****|****|****|    |    |    |****|    |    |    |
#       |****|****|****|****|    |    |****|    |    |****|
#     10|****|****|****|****|    |    |****|****|    |****|
#       |****|****|****|****|    |    |****|****|    |****|
#      8|****|****|****|****|    |    |****|****|    |****|
#       |****|****|****|****|    |****|****|****|****|****|
#      6|****|****|****|****|    |****|****|****|****|****|
#       |****|****|****|****|    |****|****|****|****|****|
#      4|****|****|****|****|    |****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
diehard_count_1s_byt|   0|    256000|     100|0.07538205|  PASSED  
#==================================================================
#             Diehard Parking Lot Test (modified).
# This tests the distribution of attempts to randomly park a
# square car of length 1 on a 100x100 parking lot without
# crashing.  We plot n (number of attempts) versus k (number of
# attempts that didn't "crash" because the car squares 
# overlapped and compare to the expected result from a perfectly
# random set of parking coordinates.  This is, alas, not really
# known on theoretical grounds so instead we compare to n=12,000
# where k should average 3523 with sigma 21.9 and is very close
# to normally distributed.  Thus (k-3523)/21.9 is a standard
# normal variable, which converted to a uniform p-value, provides
# input to a KS test with a default 100 samples.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |    |    |    |    |
#       |****|****|    |    |    |    |****|    |    |    |
#     12|****|****|    |    |    |    |****|    |****|    |
#       |****|****|    |    |    |****|****|****|****|    |
#     10|****|****|    |    |    |****|****|****|****|    |
#       |****|****|    |    |    |****|****|****|****|    |
#      8|****|****|    |    |    |****|****|****|****|    |
#       |****|****|****|****|****|****|****|****|****|    |
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
 diehard_parking_lot|   0|     12000|     100|0.65655550|  PASSED  
#==================================================================
#         Diehard Minimum Distance (2d Circle) Test 
# It does this 100 times::   choose n=8000 random points in a   
# square of side 10000.  Find d, the minimum distance between   
# the (n^2-n)/2 pairs of points.  If the points are truly inde- 
# pendent uniform, then d^2, the square of the minimum distance 
# should be (very close to) exponentially distributed with mean 
# .995 .  Thus 1-exp(-d^2/.995) should be uniform on [0,1) and  
# a KSTEST on the resulting 100 values serves as a test of uni- 
# formity for random points in the square. Test numbers=0 mod 5 
# are printed but the KSTEST is based on the full set of 100    
# random choices of 8000 points in the 10000x10000 square.      
#
# This test uses a fixed number of samples -- tsamples is ignored.
# It also uses the default value of 100 psamples in the final
# KS test, for once agreeing precisely with Diehard.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |****|    |    |    |
#       |    |    |****|    |    |    |****|    |    |    |
#     12|    |    |****|    |    |    |****|    |    |    |
#       |****|    |****|    |    |****|****|****|    |    |
#     10|****|    |****|****|    |****|****|****|    |    |
#       |****|    |****|****|    |****|****|****|    |    |
#      8|****|****|****|****|    |****|****|****|****|    |
#       |****|****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
    diehard_2dsphere|   2|      8000|     100|0.76442050|  PASSED  
#==================================================================
#          Diehard 3d Sphere (Minimum Distance) Test
# Choose  4000 random points in a cube of edge 1000.  At each   
# point, center a sphere large enough to reach the next closest 
# point. Then the volume of the smallest such sphere is (very   
# close to) exponentially distributed with mean 120pi/3.  Thus  
# the radius cubed is exponential with mean 30. (The mean is    
# obtained by extensive simulation).  The 3DSPHERES test gener- 
# ates 4000 such spheres 20 times.  Each min radius cubed leads 
# to a uniform variable by means of 1-exp(-r^3/30.), then a     
#  KSTEST is done on the 20 p-values.                           
#
# This test ignores tsamples, and runs the usual default 100
# psamples to use in the final KS test.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |****|    |    |    |    |    |
#       |    |    |    |    |****|    |    |    |    |    |
#     14|    |    |    |    |****|****|    |    |    |    |
#       |    |    |    |    |****|****|    |    |    |    |
#     12|    |    |****|    |****|****|    |    |    |    |
#       |    |****|****|    |****|****|    |    |    |    |
#     10|    |****|****|****|****|****|    |    |    |    |
#       |    |****|****|****|****|****|    |    |    |    |
#      8|****|****|****|****|****|****|    |    |    |****|
#       |****|****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
    diehard_3dsphere|   3|      4000|     100|0.10045300|  PASSED  
#==================================================================
#                  Diehard Squeeze Test.
#  Random integers are floated to get uniforms on [0,1). Start- 
#  ing with k=2^31=2147483647, the test finds j, the number of  
#  iterations necessary to reduce k to 1, using the reduction   
#  k=ceiling(k*U), with U provided by floating integers from    
#  the file being tested.  Such j's are found 100,000 times,    
#  then counts for the number of times j was <=6,7,...,47,>=48  
#  are used to provide a chi-square test for cell frequencies.  
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |****|    |    |****|    |
#     14|    |    |    |    |    |****|    |    |****|****|
#       |    |    |    |    |    |****|    |    |****|****|
#     12|    |    |    |    |    |****|    |    |****|****|
#       |    |    |    |    |    |****|    |    |****|****|
#     10|    |    |    |    |****|****|****|****|****|****|
#       |    |    |    |    |****|****|****|****|****|****|
#      8|    |    |    |****|****|****|****|****|****|****|
#       |    |****|****|****|****|****|****|****|****|****|
#      6|    |****|****|****|****|****|****|****|****|****|
#       |    |****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
     diehard_squeeze|   0|    100000|     100|0.00586033|  PASSED  
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     40|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     36|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     32|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     28|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     24|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     20|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     16|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     12|****|    |    |    |****|    |    |    |    |    |
#       |****|    |    |    |****|    |    |    |    |    |
#      8|****|****|    |    |****|****|    |    |****|    |
#       |****|****|****|****|****|****|****|****|****|    |
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     100|0.00004253|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     60|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     54|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     48|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     42|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     36|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     30|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     24|****|    |    |    |    |****|    |    |    |    |
#       |****|    |    |    |****|****|    |    |    |    |
#     18|****|    |    |****|****|****|    |    |    |    |
#       |****|****|    |****|****|****|    |****|    |****|
#     12|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     200|0.00033963|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     80|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     72|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     64|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     56|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     48|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     40|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     32|****|    |    |    |****|****|    |    |    |    |
#       |****|    |    |    |****|****|    |    |    |****|
#     24|****|****|    |****|****|****|****|    |    |****|
#       |****|****|    |****|****|****|****|****|****|****|
#     16|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      8|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     300|0.00003129|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     80|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     72|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     64|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     56|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     48|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     40|****|****|    |    |****|****|    |    |    |    |
#       |****|****|    |****|****|****|****|    |    |****|
#     32|****|****|    |****|****|****|****|    |****|****|
#       |****|****|    |****|****|****|****|****|****|****|
#     24|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     16|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      8|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     400|0.00025429|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    100|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     90|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     80|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     70|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     60|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |****|
#     50|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |****|****|****|    |    |    |****|
#     40|****|****|****|****|****|****|****|    |    |****|
#       |****|****|****|****|****|****|****|****|****|****|
#     30|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     20|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     10|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     500|0.00013855|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    120|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    108|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     96|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     84|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     72|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     60|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|****|****|    |    |****|
#     48|****|****|****|****|****|****|****|    |****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     36|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     24|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     12|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     600|0.00035295|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    140|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    126|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    112|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     98|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     84|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     70|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|****|****|    |    |****|
#     56|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     42|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     28|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     14|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     700|0.00009854|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    140|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    126|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    112|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     98|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |****|
#     84|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |    |    |****|    |    |****|
#     70|****|****|    |    |****|****|****|    |    |****|
#       |****|****|****|    |****|****|****|****|****|****|
#     56|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     42|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     28|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     14|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     800|0.00004818|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    160|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    144|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    128|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    112|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#     96|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |    |    |****|    |    |****|
#     80|****|****|    |    |****|****|****|    |    |****|
#       |****|****|    |    |****|****|****|****|****|****|
#     64|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     48|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     32|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     16|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|     900|0.00006352|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    160|    |    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    144|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    128|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    112|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |    |    |    |    |    |****|
#     96|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|****|****|****|    |****|
#     80|****|****|    |****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     64|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     48|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     32|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     16|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|    1000|0.00002446|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    180|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    162|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    144|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    126|****|****|    |    |    |    |    |    |    |    |
#       |****|****|    |    |    |    |    |    |    |****|
#    108|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|****|****|****|    |****|
#     90|****|****|    |****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     72|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     54|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     36|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     18|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|    1100|0.00001315|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    200|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    180|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    160|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    140|****|****|    |    |    |    |    |    |    |    |
#       |****|****|    |    |    |    |    |    |    |****|
#    120|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|    |****|****|    |****|
#    100|****|****|    |    |****|****|****|****|    |****|
#       |****|****|****|****|****|****|****|****|****|****|
#     80|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     60|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     40|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     20|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|    1200|0.00000115|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    220|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    198|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    176|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    154|****|    |    |    |    |    |    |    |    |    |
#       |****|****|    |    |    |    |    |    |    |****|
#    132|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |****|    |****|****|    |****|
#    110|****|****|    |    |****|****|****|****|    |****|
#       |****|****|****|****|****|****|****|****|****|****|
#     88|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     66|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     44|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     22|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|    1300|0.00000205|   WEAK   
#==================================================================
#                  Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-  
# form [0,1) variables.  Then overlapping sums,                 
#   S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.    
# The S's are virtually normal with a certain covariance mat-   
# rix.  A linear transformation of the S's converts them to a   
# sequence of independent standard normals, which are converted 
# to uniform variables for a KSTEST. The  p-values from ten     
# KSTESTs are given still another KSTEST.                       
#
#                       Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word.  It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work.  Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance.  Use it
# instead.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#    240|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#    216|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    192|****|    |    |    |    |    |    |    |    |    |
#       |****|    |    |    |    |    |    |    |    |    |
#    168|****|    |    |    |    |    |    |    |    |    |
#       |****|****|    |    |    |    |    |    |    |****|
#    144|****|****|    |    |    |    |    |    |    |****|
#       |****|****|    |    |    |    |****|    |    |****|
#    120|****|****|    |    |****|****|****|****|    |****|
#       |****|****|****|****|****|****|****|****|****|****|
#     96|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     72|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     48|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#     24|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_sums|   0|       100|    1400|0.00000061|  FAILED  
#==================================================================
#                    Diehard Runs Test
#  This is the RUNS test.  It counts runs up, and runs down, 
# in a sequence of uniform [0,1) variables, obtained by float-  
# ing the 32-bit integers in the specified file. This example   
# shows how runs are counted:  .123,.357,.789,.425,.224,.416,.95
# contains an up-run of length 3, a down-run of length 2 and an 
# up-run of (at least) 2, depending on the next values.  The    
# covariance matrices for the runs-up and runs-down are well    
# known, leading to chisquare tests for quadratic forms in the  
# weak inverses of the covariance matrices.  Runs are counted   
# for sequences of length 10,000.  This is done ten times. Then 
# repeated.                                                     
#
# In Dieharder sequences of length tsamples = 100000 are used by
# default, and 100 p-values thus generated are used in a final
# KS test.
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |****|
#       |    |    |    |    |    |    |    |    |    |****|
#     14|    |    |    |    |    |    |    |    |    |****|
#       |    |****|    |    |    |    |    |    |    |****|
#     12|    |****|****|    |    |    |****|    |    |****|
#       |    |****|****|    |    |    |****|    |    |****|
#     10|    |****|****|    |    |    |****|    |****|****|
#       |    |****|****|****|    |    |****|    |****|****|
#      8|****|****|****|****|    |    |****|****|****|****|
#       |****|****|****|****|****|    |****|****|****|****|
#      6|****|****|****|****|****|    |****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_runs|   0|    100000|     100|0.53138878|  PASSED  
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |****|    |    |    |    |    |    |    |    |
#     14|    |****|    |    |    |    |    |    |    |    |
#       |    |****|    |    |    |    |    |    |    |    |
#     12|****|****|    |    |    |****|    |    |    |    |
#       |****|****|    |    |    |****|****|****|****|    |
#     10|****|****|    |    |    |****|****|****|****|    |
#       |****|****|    |    |    |****|****|****|****|****|
#      8|****|****|****|    |    |****|****|****|****|****|
#       |****|****|****|    |    |****|****|****|****|****|
#      6|****|****|****|****|    |****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
        diehard_runs|   0|    100000|     100|0.56967863|  PASSED  
#==================================================================
#                   Diehard Craps Test
#  This is the CRAPS TEST. It plays 200,000 games of craps, finds  
#  the number of wins and the number of throws necessary to end    
#  each game.  The number of wins should be (very close to) a      
#  normal with mean 200000p and variance 200000p(1-p), with        
#  p=244/495.  Throws necessary to complete the game can vary      
#  from 1 to infinity, but counts for all>21 are lumped with 21.   
#  A chi-square test is made on the no.-of-throws cell counts.     
#  Each 32-bit integer from the test file provides the value for   
#  the throw of a die, by floating to [0,1), multiplying by 6      
#  and taking 1 plus the integer part of the result.               
#==================================================================
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |****|    |
#       |    |    |    |****|    |    |    |    |****|    |
#     14|    |    |    |****|    |    |    |    |****|    |
#       |****|    |    |****|    |    |    |    |****|    |
#     12|****|    |    |****|    |    |    |    |****|    |
#       |****|    |    |****|    |    |    |    |****|    |
#     10|****|    |    |****|    |    |    |    |****|    |
#       |****|    |    |****|****|    |****|****|****|    |
#      8|****|    |    |****|****|****|****|****|****|****|
#       |****|****|    |****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
       diehard_craps|   0|    200000|     100|0.86443880|  PASSED  
#=============================================================================#
#                         Histogram of test p-values                          #
#=============================================================================#
# Bin scale = 0.100000
#     20|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     18|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     16|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |    |
#     14|    |    |    |    |    |    |    |    |    |    |
#       |    |    |    |    |    |    |    |    |    |****|
#     12|    |****|    |****|    |    |    |    |    |****|
#       |    |****|    |****|****|    |    |    |    |****|
#     10|    |****|    |****|****|    |    |****|    |****|
#       |****|****|****|****|****|    |    |****|****|****|
#      8|****|****|****|****|****|    |****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      6|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      4|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#      2|****|****|****|****|****|****|****|****|****|****|
#       |****|****|****|****|****|****|****|****|****|****|
#       |--------------------------------------------------
#       | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
        test_name   |ntup| tsamples |psamples|  p-value |Assessment
#=============================================================================#
       diehard_craps|   0|    200000|     100|0.90656575|  PASSED