diff --git a/.gitignore b/.gitignore
index 2066ba7d5f8a7554184023eb528436996e8cdaa3..f85ebc57eb97c2e778fd57ad701d9b907168c526 100644
--- a/.gitignore
+++ b/.gitignore
@@ -136,6 +136,8 @@ tests/testOutputList
tests/testCbrt
tests/testFormat.sh
tests/testCooling
+tests/*.png
+tests/*.txt
theory/latex/swift.pdf
theory/SPH/Kernels/kernels.pdf
diff --git a/src/random.h b/src/random.h
index 4d665a2697076a139c6e4e614b223302b04ad7a6..660ae21db8dc78a8bde78b3f541bff6b621253cd 100644
--- a/src/random.h
+++ b/src/random.h
@@ -1,6 +1,7 @@
/*******************************************************************************
* This file is part of SWIFT.
* Copyright (c) 2018 Matthieu Schaller (schaller@strw.leidenuniv.nl)
+ * 2019 Folkert Nobels (nobels@strw.leidenuniv.nl)
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published
@@ -28,15 +29,23 @@
/**
* @brief The categories of random number generated.
*
- * The values of the fields are carefully chose prime
- * numbers. Only change them if you know what you are
- * doing!
+ * The values of the fields are carefully chose numbers
+ * the numbers are very large primes such that the IDs
+ * will not have a prime factorization with this coefficient
+ * this results in a very high period for the random number
+ * generator.
+ * Only change when you know what you are doing, changing
+ * the numbers to bad values will break the random number
+ * generator.
+ * In case new numbers need to be added other possible
+ * numbers could be:
+ * 4947009007, 5947309451, 6977309513
*/
enum random_number_type {
- random_number_star_formation = 7,
- random_number_stellar_feedback = 53,
- random_number_stellar_enrichment = 197,
- random_number_BH_feedback = 491
+ random_number_star_formation = 0LL,
+ random_number_stellar_feedback = 3947008991LL,
+ random_number_stellar_enrichment = 2936881973LL,
+ random_number_BH_feedback = 1640531371LL
};
/**
@@ -59,15 +68,45 @@ INLINE static double random_unit_interval(const long long int id,
/* Range used for the seeds. Best if prime */
static const long long seed_range = RAND_MAX;
static const double RAND_MAX_inv = 1. / ((double)RAND_MAX);
+ static const long long mwc_number = (1LL << 32) - 1LL;
/* Calculate the seed */
/* WARNING: Only change the math if you really know what you are doing!
- The numbers are carefully chosen prime numbers that prevent correlation
- with either the current integer time or the particle IDs.
- The calculation overflows on purpose. */
- unsigned int seed = ((937LL * id + 1109LL) % 2147987LL +
- (ti_current - 1LL) % 1514917LL + (long long)type) %
- seed_range;
+ * The numbers are carefully chosen prime numbers that prevent correlation
+ * with either the current integer time or the particle IDs. The current
+ * method also prevents any correlation between different random number
+ * types.
+ * The calculation overflows on purpose.
+ * 1. The first step is calculating the seed by using a multiply with carry
+ * (MWC) method, this method depends on the type of random number and
+ * this therefore also prevents that there is any correlation between
+ * the different types of random numbers.
+ * 2. After this we use the 64 bit Xorshift method to randomize the seeds
+ * even more.
+ * 3. We calculate a prime multiplication for the id with a quadratic
+ * term.
+ * 4. We calculate the seed by using a Quadratic congruential generator,
+ * in which we use the id part and the current time step bin.
+ */
+ unsigned long long number = ti_current;
+
+ /* Multiply with carry (MWC), (adviced variables by NR) */
+ number = 4294957665LL * (number & (mwc_number)) + (number >> 32);
+
+ /* 64-bit Xorshift (adviced variables by NR) */
+ number ^= number << 21;
+ number ^= number >> 35;
+ number ^= number << 4;
+
+ /* Add constant to ID */
+ const unsigned long long idt = id + type;
+
+ /* Nonlinear congruential generator */
+ const unsigned long long idpart =
+ 3457LL * idt + 593LL * idt * ti_current + 5417LL * idt * idt;
+ unsigned int seed =
+ (937LL * number + 5171LL * number * number + idpart + 1109LL) %
+ 9996361LL % seed_range;
/* Generate a random number between 0 and 1. */
return rand_r(&seed) * RAND_MAX_inv;
diff --git a/tests/testRandom.c b/tests/testRandom.c
index 4ac3230705f7c119ce2a4868d2d131375ff20858..1b2a6a5d480be23998a4278443b095eed1fc9755 100644
--- a/tests/testRandom.c
+++ b/tests/testRandom.c
@@ -1,6 +1,7 @@
/*******************************************************************************
* This file is part of SWIFT.
* Copyright (C) 2019 Matthieu Schaller (schaller@strw.leidenuniv.nl)
+ * 2019 Folkert Nobels (nobels@strw.leidenuniv.nl)
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published
@@ -25,6 +26,63 @@
/* Local headers. */
#include "swift.h"
+/**
+ * @brief Compute the Pearson correlation coefficient for two sets of numbers
+ *
+ * The pearson correlation coefficient between two sets of numbers can be
+ * calculated as:
+ *
+ * <x*y> - <x>*<y>
+ * r_xy = ----------------------
+ * (var(x) * var(y))^.5
+ *
+ * In the case that both sets are purely uncorrelated the value of the
+ * Pearson correlation function is expected to be close to 0. In the case that
+ * there is positive correlation r_xy > 0 and in the case of negative
+ * correlation, the function has r_xy < 0.
+ *
+ * @param mean1 average of first series of numbers
+ * @param mean2 average of second series of numbers
+ * @param total12 sum of x_i * y_i of both series of numbers
+ * @param var1 variance of the first series of numbers
+ * @param var2 variance of the second series of numbers
+ * @param number of elements in both series
+ * @return the Pearson correlation coefficient
+ * */
+double pearsonfunc(double mean1, double mean2, double total12, double var1,
+ double var2, int counter) {
+
+ const double mean12 = total12 / (double)counter;
+ const double correlation = (mean12 - mean1 * mean2) / sqrt(var1 * var2);
+ return fabs(correlation);
+}
+
+/**
+ * @brief Test to check that the pseodo-random numbers in SWIFT are random
+ * enough for our purpose.
+ *
+ * The test initializes with the current time and than creates 20 ID numbers
+ * it runs the test using these 20 ID numbers. Using these 20 ID numbers it
+ * Checks 4 different things:
+ * 1. The mean and variance are correct for random numbers generated by this
+ * ID number.
+ * 2. The random numbers from this ID number do not cause correlation in time.
+ * Correlation is checked using the Pearson correlation coefficient which
+ * should be sufficiently close to zero.
+ * 3. A small offset in ID number of 2, doesn't cause correlation between
+ * the two sets of random numbers (again with the Pearson correlation
+ * coefficient) and the mean and variance of this set is
+ * also correct.
+ * 4. Different physical processes in random.h are also uncorrelated and
+ * produce the correct mean and variance as expected. Again the correlation
+ * is calculated using the Pearson correlation coefficient.
+ *
+ * More information about the Pearson correlation coefficient can be found in
+ * the function pearsonfunc above this function.
+ *
+ * @param none
+ * @return 0 if everything is fine, 1 if random numbers are not random enough.
+ */
int main(int argc, char* argv[]) {
/* Initialize CPU frequency, this also starts time. */
@@ -49,12 +107,39 @@ int main(int argc, char* argv[]) {
const long long id = rand() * (1LL << 31) + rand();
const integertime_t increment = (1LL << time_bin);
+ const long long idoffset = id + 2;
message("Testing id=%lld time_bin=%d", id, time_bin);
double total = 0., total2 = 0.;
int count = 0;
+ /* Pearson correlation variables for different times */
+ double sum_previous_current = 0.;
+ double previous = 0.;
+
+ /* Pearson correlation for two different IDs */
+ double pearsonIDs = 0.;
+ double totalID = 0.;
+ double total2ID = 0.;
+
+ /* Pearson correlation for different processes */
+ double pearson_star_sf = 0.;
+ double pearson_star_se = 0.;
+ double pearson_star_bh = 0.;
+ double pearson_sf_se = 0.;
+ double pearson_sf_bh = 0.;
+ double pearson_se_bh = 0.;
+
+ /* Calculate the mean and <x^2> for these processes */
+ double total_sf = 0.;
+ double total_se = 0.;
+ double total_bh = 0.;
+
+ double total2_sf = 0.;
+ double total2_se = 0.;
+ double total2_bh = 0.;
+
/* Check that the numbers are uniform over the full-range of useful
* time-steps */
for (integertime_t ti_current = 0LL; ti_current < max_nr_timesteps;
@@ -68,18 +153,151 @@ int main(int argc, char* argv[]) {
total += r;
total2 += r * r;
count++;
+
+ /* Calculate for correlation between time.
+ * For this we use the pearson correlation of time i and i-1 */
+ sum_previous_current += r * previous;
+ previous = r;
+
+ /* Calculate if there is a correlation between different ids */
+ const double r_2ndid = random_unit_interval(idoffset, ti_current,
+ random_number_star_formation);
+
+ /* Pearson correlation for small different IDs */
+ pearsonIDs += r * r_2ndid;
+ totalID += r_2ndid;
+ total2ID += r_2ndid * r_2ndid;
+
+ /* Calculate random numbers for the different processes and check
+ * that they are uncorrelated */
+
+ const double r_sf =
+ random_unit_interval(id, ti_current, random_number_stellar_feedback);
+
+ const double r_se = random_unit_interval(
+ id, ti_current, random_number_stellar_enrichment);
+
+ const double r_bh =
+ random_unit_interval(id, ti_current, random_number_BH_feedback);
+
+ /* Calculate the correlation between the different processes */
+ total_sf += r_sf;
+ total_se += r_se;
+ total_bh += r_bh;
+
+ total2_sf += r_sf * r_sf;
+ total2_se += r_se * r_se;
+ total2_bh += r_bh * r_bh;
+
+ pearson_star_sf += r * r_sf;
+ pearson_star_se += r * r_se;
+ pearson_star_bh += r * r_bh;
+ pearson_sf_se += r_sf * r_se;
+ pearson_sf_bh += r_sf * r_bh;
+ pearson_se_bh += r_se * r_bh;
}
const double mean = total / (double)count;
const double var = total2 / (double)count - mean * mean;
+ /* Pearson correlation calculation for different times */
+ // const double mean_xy = sum_previous_current / ((double)count - 1.f);
+ // const double correlation = (mean_xy - mean * mean) / var;
+ const double correlation =
+ pearsonfunc(mean, mean, sum_previous_current, var, var, count - 1);
+
+ /* Mean for different IDs */
+ const double meanID = totalID / (double)count;
+ const double varID = total2ID / (double)count - meanID * meanID;
+
+ /* Pearson correlation between different IDs*/
+ const double correlationID =
+ pearsonfunc(mean, meanID, pearsonIDs, var, varID, count);
+
+ /* Mean and <x^2> for different processes */
+ const double mean_sf = total_sf / (double)count;
+ const double mean_se = total_se / (double)count;
+ const double mean_bh = total_bh / (double)count;
+
+ const double var_sf = total2_sf / (double)count - mean_sf * mean_sf;
+ const double var_se = total2_se / (double)count - mean_se * mean_se;
+ const double var_bh = total2_bh / (double)count - mean_bh * mean_bh;
+
+ /* Correlation between different processes */
+ const double corr_star_sf =
+ pearsonfunc(mean, mean_sf, pearson_star_sf, var, var_sf, count);
+ const double corr_star_se =
+ pearsonfunc(mean, mean_se, pearson_star_se, var, var_se, count);
+ const double corr_star_bh =
+ pearsonfunc(mean, mean_bh, pearson_star_bh, var, var_bh, count);
+ const double corr_sf_se =
+ pearsonfunc(mean_sf, mean_se, pearson_sf_se, var_sf, var_se, count);
+ const double corr_sf_bh =
+ pearsonfunc(mean_sf, mean_bh, pearson_sf_bh, var_sf, var_bh, count);
+ const double corr_se_bh =
+ pearsonfunc(mean_se, mean_bh, pearson_se_bh, var_se, var_bh, count);
+
/* Verify that the mean and variance match the expected values for a uniform
* distribution */
- if ((fabs(mean - 0.5) / 0.5 > 1e-4) ||
- (fabs(var - 1. / 12.) / (1. / 12.) > 1e-4)) {
+ const double tolmean = 2e-4;
+ const double tolvar = 1e-3;
+ const double tolcorr = 4e-4;
+
+ if ((fabs(mean - 0.5) / 0.5 > tolmean) ||
+ (fabs(var - 1. / 12.) / (1. / 12.) > tolvar) ||
+ (correlation > tolcorr) || (correlationID > tolcorr) ||
+ (fabs(meanID - 0.5) / 0.5 > tolmean) ||
+ (fabs(varID - 1. / 12.) / (1. / 12.) > tolvar) ||
+ (corr_star_sf > tolcorr) || (corr_star_se > tolcorr) ||
+ (corr_star_bh > tolcorr) || (corr_sf_se > tolcorr) ||
+ (corr_sf_bh > tolcorr) || (corr_se_bh > tolcorr) ||
+ (fabs(mean_sf - 0.5) / 0.5 > tolmean) ||
+ (fabs(mean_se - 0.5) / 0.5 > tolmean) ||
+ (fabs(mean_bh - 0.5) / 0.5 > tolmean) ||
+ (fabs(var_sf - 1. / 12.) / (1. / 12.) > tolvar) ||
+ (fabs(var_se - 1. / 12.) / (1. / 12.) > tolvar) ||
+ (fabs(var_bh - 1. / 12.) / (1. / 12.) > tolvar)) {
message("Test failed!");
- message("Result: count=%d mean=%f var=%f", count, mean, var);
- message("Expected: count=%d mean=%f var=%f", count, 0.5f, 1. / 12.);
+ message("Global result:");
+ message("Result: count=%d mean=%f var=%f, correlation=%f", count, mean,
+ var, correlation);
+ message("Expected: count=%d mean=%f var=%f, correlation=%f", count, 0.5f,
+ 1. / 12., 0.);
+ message("ID part");
+ message(
+ "Result: count%d mean=%f var=%f"
+ " correlation=%f",
+ count, meanID, varID, correlationID);
+ message(
+ "Expected: count%d mean=%f var=%f"
+ " correlation=%f",
+ count, .5f, 1. / 12., 0.);
+ message("Different physical processes:");
+ message(
+ "Means: stars=%f stellar feedback=%f stellar "
+ " enrichement=%f black holes=%f",
+ mean, mean_sf, mean_se, mean_bh);
+ message(
+ "Expected: stars=%f stellar feedback=%f stellar "
+ " enrichement=%f black holes=%f",
+ .5f, .5f, .5f, .5f);
+ message(
+ "Var: stars=%f stellar feedback=%f stellar "
+ " enrichement=%f black holes=%f",
+ var, var_sf, var_se, var_bh);
+ message(
+ "Expected: stars=%f stellar feedback=%f stellar "
+ " enrichement=%f black holes=%f",
+ 1. / 12., 1. / 12., 1 / 12., 1. / 12.);
+ message(
+ "Correlation: stars-sf=%f stars-se=%f stars-bh=%f"
+ "sf-se=%f sf-bh=%f se-bh=%f",
+ corr_star_sf, corr_star_se, corr_star_bh, corr_sf_se, corr_sf_bh,
+ corr_se_bh);
+ message(
+ "Expected: stars-sf=%f stars-se=%f stars-bh=%f"
+ "sf-se=%f sf-bh=%f se-bh=%f",
+ 0., 0., 0., 0., 0., 0.);
return 1;
}
}