rewrote wolff in c++ with templates so that any system can be run with it

3 files changed, 137 insertions, 624 deletions

diff --git a/src/wolff.cpp b/src/wolff.cpp
new file mode 100644
index 0000000..85df357
--- /dev/null
+++ b/src/wolff.cpp
@@ -0,0 +1,137 @@
+
+#include <time.h>
+#include <getopt.h>
+
+#include <cluster.h>
+
+double H_vector(vector_t <2, double> v1, double *H) {
+  vector_t <2, double> H_vec;
+  H_vec.x = H;
+  return dot <2, double> (v1, H_vec);
+}
+
+int main(int argc, char *argv[]) {
+
+  count_t N = (count_t)1e7;
+
+  D_t D = 2;
+  L_t L = 128;
+  double T = 2.26918531421;
+  double *H = (double *)calloc(MAX_Q, sizeof(double));
+
+  bool silent = false;
+
+  int opt;
+  q_t J_ind = 0;
+  q_t H_ind = 0;
+
+  while ((opt = getopt(argc, argv, "N:q:D:L:T:J:H:s")) != -1) {
+    switch (opt) {
+    case 'N': // number of steps
+      N = (count_t)atof(optarg);
+      break;
+    case 'D': // dimension
+      D = atoi(optarg);
+      break;
+    case 'L': // linear size
+      L = atoi(optarg);
+      break;
+    case 'T': // temperature 
+      T = atof(optarg);
+      break;
+    case 'H': // external field. nth call couples to state n
+      H[H_ind] = atof(optarg);
+      H_ind++;
+      break;
+    case 's': // don't print anything during simulation. speeds up slightly
+      silent = true;
+      break;
+    default:
+      exit(EXIT_FAILURE);
+    }
+  }
+
+  state_t <orthogonal_t <2, double>, vector_t <2, double>> s(D, L, T, dot <2, double>, std::bind(H_vector, std::placeholders::_1, H));
+
+  // initialize random number generator
+  gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
+  gsl_rng_set(r, rand_seed());
+
+  unsigned long timestamp;
+
+  {
+    struct timespec spec;
+    clock_gettime(CLOCK_REALTIME, &spec);
+    timestamp = spec.tv_sec*1000000000LL + spec.tv_nsec;
+  }
+
+  FILE *outfile_info = fopen("wolff_metadata.txt", "a");
+
+  fprintf(outfile_info, "<| \"ID\" -> %lu, \"D\" -> %" PRID ", \"L\" -> %" PRIL ", \"NV\" -> %" PRIv ", \"NE\" -> %" PRIv ", \"T\" -> %.15f, \"H\" -> {", timestamp, D, L, s.nv, s.ne, T);
+
+  for (q_t i = 0; i < 2; i++) {
+    fprintf(outfile_info, "%.15f", H[i]);
+    if (i < 2 - 1) {
+      fprintf(outfile_info, ", ");
+    }
+  }
+
+  fprintf(outfile_info, "} |>\n");
+
+  fclose(outfile_info);
+
+  char *filename_M = (char *)malloc(255 * sizeof(char));
+  char *filename_E = (char *)malloc(255 * sizeof(char));
+  char *filename_S = (char *)malloc(255 * sizeof(char));
+
+  sprintf(filename_M, "wolff_%lu_M.dat", timestamp);
+  sprintf(filename_E, "wolff_%lu_E.dat", timestamp);
+  sprintf(filename_S, "wolff_%lu_S.dat", timestamp);
+
+  FILE *outfile_M = fopen(filename_M, "wb");
+  FILE *outfile_E = fopen(filename_E, "wb");
+  FILE *outfile_S = fopen(filename_S, "wb");
+
+  free(filename_M);
+  free(filename_E);
+  free(filename_S);
+
+  v_t cluster_size = 0;
+
+  if (!silent) printf("\n");
+  for (count_t steps = 0; steps < N; steps++) {
+    if (!silent) printf("\033[F\033[JWOLFF: sweep %" PRIu64 " / %" PRIu64 ": E = %.2f, M_0 = %.2f, S = %" PRIv "\n", steps, N, s.E, s.M.x[0], cluster_size);
+
+    v_t v0 = gsl_rng_uniform_int(r, s.nv);
+     
+    orthogonal_t <2, double> step;
+    generate_rotation<2>(r, &step);
+
+    printf("(%g %g) . (%g %g) = %g or %g, H = %g\n\n", s.spins[0].x[0], s.spins[0].x[1], s.spins[1].x[0], s.spins[1].x[1], dot(s.spins[0], s.spins[1]), s.J(s.spins[0],s.spins[1]), s.H(s.spins[0]));
+
+    getchar();
+    cluster_size = flip_cluster <orthogonal_t <2, double>, vector_t <2, double>> (&s, v0, step, r);
+
+    free_spin(step);
+
+    fwrite(&(s.E), sizeof(double), 1, outfile_E);
+    fwrite(s.M.x, sizeof(double), 2, outfile_M);
+    fwrite(&cluster_size, sizeof(uint32_t), 1, outfile_S);
+
+  }
+  if (!silent) {
+    printf("\033[F\033[J");
+  }
+  printf("WOLFF: sweep %" PRIu64 " / %" PRIu64 ": E = %.2f, M_0 = %.2f, S = %" PRIv "\n", N, N, s.E, s.M.x[0], cluster_size);
+
+  fclose(outfile_M);
+  fclose(outfile_E);
+  fclose(outfile_S);
+
+  gsl_rng_free(r);
+
+  free(H);
+
+  return 0;
+}
+
diff --git a/src/wolff_dgm.c b/src/wolff_dgm.c
deleted file mode 100644
index f11b296..0000000
--- a/src/wolff_dgm.c
+++ /dev/null
@@ -1,247 +0,0 @@
-
-#include <getopt.h>
-
-#include <cluster.h>
-
-double identity(h_t x) {
-  return -pow(x, 2);
-}
-
-double basic_H(double *H, h_t x) {
-  return -H[0] * pow(x, 2);
-}
-
-int main(int argc, char *argv[]) {
-
-  L_t L = 128;
-  count_t N = (count_t)1e7;
-  count_t min_runs = 10;
-  count_t n = 3;
-  D_t D = 2;
-  double T = 2.26918531421;
-  double *H = (double *)calloc(MAX_Q, sizeof(double));
-  double eps = 0;
-  bool silent = false;
-  bool record_autocorrelation = false;
-  count_t ac_skip = 1;
-  count_t W = 10;
-
-  int opt;
-  q_t H_ind = 0;
-
-  while ((opt = getopt(argc, argv, "N:n:D:L:T:H:m:e:saS:W:")) != -1) {
-    switch (opt) {
-    case 'N':
-      N = (count_t)atof(optarg);
-      break;
-    case 'n':
-      n = (count_t)atof(optarg);
-      break;
-    case 'D':
-      D = atoi(optarg);
-      break;
-    case 'L':
-      L = atoi(optarg);
-      break;
-    case 'T':
-      T = atof(optarg);
-      break;
-    case 'H':
-      H[H_ind] = atof(optarg);
-      H_ind++;
-      break;
-    case 'm':
-      min_runs = atoi(optarg);
-      break;
-    case 'e':
-      eps = atof(optarg);
-      break;
-    case 's':
-      silent = true;
-      break;
-    case 'a':
-      record_autocorrelation = true;
-      break;
-    case 'S':
-      ac_skip = (count_t)atof(optarg);
-      break;
-    case 'W':
-      W = (count_t)atof(optarg);
-      break;
-    default:
-      exit(EXIT_FAILURE);
-    }
-  }
-
-  gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
-  gsl_rng_set(r, rand_seed());
-
-  dgm_state_t *s = (dgm_state_t *)calloc(1, sizeof(dgm_state_t));
-
-  graph_t *h = graph_create_square(D, L);
-  s->g = graph_add_ext(h);
-
-  s->spins = (h_t *)calloc(h->nv, sizeof(h_t));
-
-  s->H_info = H;
-  s->T = T;
-  s->H = basic_H;
-  s->J = identity;
-
-  s->R = (dihinf_t *)calloc(1, sizeof(dihinf_t));
-
-  s->M = 0;
-  s->E = 0;
-
-  double diff = 1e31;
-  count_t n_runs = 0;
-  count_t n_steps = 0;
-
-  meas_t *E, *clust, *M, *dM;
-
-  M = (meas_t *)calloc(1, sizeof(meas_t ));
-  dM = (meas_t *)calloc(1, sizeof(meas_t ));
-
-  E = calloc(1, sizeof(meas_t));
-  clust = calloc(1, sizeof(meas_t));
-
-  autocorr_t *autocorr;
-  if (record_autocorrelation) {
-    autocorr = (autocorr_t *)calloc(1, sizeof(autocorr_t));
-    autocorr->W = 2 * W + 1;
-    autocorr->OO = (double *)calloc(2 * W + 1, sizeof(double));
-  }
-
-  if (!silent) printf("\n");
-  while (((diff > eps || diff != diff) && n_runs < N) || n_runs < min_runs) {
-    if (!silent) printf("\033[F\033[JWOLFF: sweep %" PRIu64
-           ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
-           n_runs, fabs(meas_dx(E) / E->x), meas_dx(M) / M->x, meas_dc(E) / meas_c(E), meas_dc(M) / meas_c(M), h->nv / clust->x);
-
-    count_t n_flips = 0;
-
-    while (n_flips / h->nv < n) {
-      v_t v0 = gsl_rng_uniform_int(r, h->nv);
-      h_t step = round((((double)s->M) / h->nv) + gsl_ran_gaussian(r, 5));
-
-      v_t tmp_flips = flip_cluster_dgm(s, v0, step, r);
-      n_flips += tmp_flips;
-
-      if (n_runs > 0) {
-        n_steps++;
-        meas_update(clust, tmp_flips);
-      }
-
-      if (record_autocorrelation && n_runs > 0) {
-        if (n_steps % ac_skip == 0) {
-          update_autocorr(autocorr, s->E);
-        }
-      }
-    }
-
-    meas_update(M, s->M);
-    h_t min_h, max_h;
-    min_h = MAX_H;
-    max_h = MIN_H;
-    for (v_t i = 0; i < h->nv; i++) {
-      if (s->spins[i] < min_h) {
-        min_h = s->spins[i];
-      } else if (s->spins[i] > max_h) {
-        max_h = s->spins[i];
-      }
-    }
-    meas_update(dM, max_h - min_h);
-    meas_update(E, s->E);
-
-    diff = fabs(meas_dc(E) / meas_c(E));
-
-    n_runs++;
-  }
-  if (!silent) {
-    printf("\033[F\033[J");
-  }
-  printf("WOLFF: sweep %" PRIu64
-         ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
-         n_runs, fabs(meas_dx(E) / E->x), meas_dx(M) / M->x, meas_dc(E) / meas_c(E), meas_dc(M) / meas_c(M), h->nv / clust->x);
-
-  double tau = 0;
-  bool tau_failed = false;
-
-  if (record_autocorrelation) {
-    double *Gammas = (double *)malloc((W + 1) * sizeof(double));
-
-    Gammas[0] = 1 + rho(autocorr, 0);
-    for (uint64_t i = 0; i < W; i++) {
-      Gammas[1 + i] = rho(autocorr, 2 * i + 1) + rho(autocorr, 2 * i + 2);
-    } 
-
-    uint64_t n;
-    for (n = 0; n < W + 1; n++) {
-      if (Gammas[n] <= 0) {
-        break;
-      }
-    }
-
-    if (n == W + 1) {
-      printf("WARNING: correlation function never hit the noise floor.\n");
-      tau_failed = true;
-    }
-
-    if (n < 2) {
-      printf("WARNING: correlation function only has one nonnegative term.\n");
-      tau_failed = true;
-    }
-
-    double *conv_Gamma = get_convex_minorant(n, Gammas);
-
-    double ttau = - 0.5;
-
-    for (uint64_t i = 0; i < n + 1; i++) {
-      ttau += conv_Gamma[i];
-    }
-    
-    free(Gammas);
-    free(autocorr->OO);
-    while (autocorr->Op != NULL) {
-      stack_pop_d(&(autocorr->Op));
-    }
-    free(autocorr);
-    
-    tau = ttau * ac_skip * clust->x / h->nv;
-  }
-
-  if (tau_failed) {
-    tau = 0;
-  }
-
-  FILE *outfile = fopen("out.m", "a");
-
-  fprintf(outfile, "<|D->%" PRID ",L->%" PRIL ",T->%.15f", D, L, T);
-  fprintf(outfile, ",E->%.15f,\\[Delta]E->%.15f,C->%.15f,\\[Delta]C->%.15f,M->%.15f", E->x / h->nv, meas_dx(E) / h->nv, meas_c(E) / h->nv, meas_dc(E) / h->nv, M->x / h->nv);
-  fprintf(outfile, ",\\[Delta]M->%.15f", meas_dx(M) / h->nv);
-  fprintf(outfile, ",\\[Chi]->%.15f", meas_c(M) / h->nv);
-  fprintf(outfile, ",\\[Delta]\\[Chi]->%.15f", meas_dc(M) / h->nv);
-  fprintf(outfile, ",w->%.15f,\\[Delta]w->%.15f,wc->%.15f,\\[Delta]wc->%.15f,Subscript[n,\"clust\"]->%.15f,Subscript[\\[Delta]n,\"clust\"]->%.15f,Subscript[m,\"clust\"]->%.15f,Subscript[\\[Delta]m,\"clust\"]->%.15f,\\[Tau]->%.15f|>\n", dM->x, meas_dx(dM), meas_c(dM), meas_dc(dM), clust->x / h->nv, meas_dx(clust) / h->nv, meas_c(clust) / h->nv, meas_dc(clust) / h->nv,tau);
-
-  fclose(outfile);
-
-  FILE *image = fopen("out.dat", "a");
-  for (v_t i = 0; i < h->nv; i++) {
-    fprintf(image, "%" PRIh " ", s->spins[i]);
-  }
-  fprintf(image, "\n");
-  fclose(image);
-
-  free(E);
-  free(clust);
-  free(H);
-  free(s->R);
-  free(s->spins);
-  graph_free(s->g);
-  free(s);
-  graph_free(h);
-  gsl_rng_free(r);
-
-  return 0;
-}
-
diff --git a/src/wolff_vector.c b/src/wolff_vector.c
deleted file mode 100644
index c5ebcb5..0000000
--- a/src/wolff_vector.c
+++ /dev/null
@@ -1,377 +0,0 @@
-
-#include <getopt.h>
-
-#include <cluster.h>
-
-double identity(double x) {
-  return x;
-}
-
-double zero(q_t n, double *H, double *x) {
-  return 0.0;
-}
-
-double dot(q_t n, double *H, double *x) {
-  double total = 0;
-  for (q_t i = 0; i < n; i++) {
-    total += H[i] * x[i];
-  }
-  return total;
-}
-
-double theta(double x, double y) {
-  double val = atan(y / x);
-
-  if (x < 0.0 && y > 0.0) {
-    return M_PI + val;
-  } else if ( x < 0.0 && y < 0.0 ) {
-    return - M_PI + val;
-  } else {
-    return val;
-  }
-}
-
-double modulated(q_t n, double *H_info, double *x) {
-  return H_info[0] * cos(H_info[1] * theta(x[0], x[1]));
-}
-
-double cubic(q_t n, double *H_info, double *x) {
-  double v_sum = 0;
-
-  for (q_t i = 0; i < n; i++) {
-    v_sum += pow(x[i], 4);
-  }
-
-  return - H_info[0] * v_sum;
-}
-
-double quadratic(q_t n, double *H_info, double *x) {
-  double tmp = 0;
-
-  tmp += pow(x[0], 2);
-
-  for (q_t i = 1; i < n; i++) {
-    tmp += - 1.0 / (n - 1.0) * pow(x[i], 2);
-  }
-
-  return - 0.5 *  H_info[0] * tmp;
-}
-
-int main(int argc, char *argv[]) {
-
-  L_t L = 128;
-  count_t N = (count_t)1e7;
-  count_t min_runs = 10;
-  count_t n = 3;
-  q_t q = 2;
-  D_t D = 2;
-  double T = 2.26918531421;
-  double *H = (double *)calloc(MAX_Q, sizeof(double));
-  double eps = 0;
-  bool silent = false;
-  bool record_autocorrelation = false;
-  vector_field_t H_type = VECTOR;
-  count_t ac_skip = 1;
-  count_t W = 10;
-
-  int opt;
-  q_t H_ind = 0;
-
-  while ((opt = getopt(argc, argv, "N:n:D:L:q:T:H:m:e:saS:W:f:")) != -1) {
-    switch (opt) {
-    case 'N':
-      N = (count_t)atof(optarg);
-      break;
-    case 'n':
-      n = (count_t)atof(optarg);
-      break;
-    case 'D':
-      D = atoi(optarg);
-      break;
-    case 'L':
-      L = atoi(optarg);
-      break;
-    case 'q':
-      q = atoi(optarg);
-      break;
-    case 'T':
-      T = atof(optarg);
-      break;
-    case 'H':
-      H[H_ind] = atof(optarg);
-      H_ind++;
-      break;
-    case 'm':
-      min_runs = atoi(optarg);
-      break;
-    case 'e':
-      eps = atof(optarg);
-      break;
-    case 's':
-      silent = true;
-      break;
-    case 'a':
-      record_autocorrelation = true;
-      break;
-    case 'S':
-      ac_skip = (count_t)atof(optarg);
-      break;
-    case 'W':
-      W = (count_t)atof(optarg);
-      break;
-    case 'f':
-      switch (atoi(optarg)) {
-        case 0:
-          H_type = VECTOR;
-          break;
-        case 1:
-          H_type = MODULATED;
-          break;
-        case 2:
-          H_type = CUBIC;
-          break;
-        case 3:
-          H_type = QUADRATIC;
-          break;
-      }
-      break;
-    default:
-      exit(EXIT_FAILURE);
-    }
-  }
-
-  gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
-  gsl_rng_set(r, rand_seed());
-
-  vector_state_t *s = (vector_state_t *)calloc(1, sizeof(vector_state_t));
-
-  graph_t *h = graph_create_square(D, L);
-  s->g = graph_add_ext(h);
-
-  s->n = q;
-
-  s->spins = (double *)calloc(n * h->nv, sizeof(double));
-
-  for (v_t i = 0; i < h->nv; i++) {
-    s->spins[q * i] = 1.0;
-  }
-
-  s->H_info = H;
-  s->T = T;
-  switch (H_type) {
-    case VECTOR:
-      s->H = dot;
-      break;
-    case MODULATED:
-      s->H = modulated;
-      break;
-    case CUBIC:
-      s->H = cubic;
-      break;
-    case QUADRATIC:
-      s->H = quadratic;
-      break;
-  }
-  s->J = identity;
-
-  s->R = (double *)calloc(q * q, sizeof(double));
-
-  for (q_t i = 0; i < q; i++) {
-    s->R[q * i + i] = 1.0;
-  }
-
-  s->M = (double *)calloc(q, sizeof(double));
-  s->M[0] = 1.0;
-  s->E = - ((double)h->ne) * s->J(1.0) - (double)h->nv * s->H(s->n, s->H_info, s->M);
-  s->M[0] *= (double)h->nv;
-
-  double diff = 1e31;
-  count_t n_runs = 0;
-  count_t n_steps = 0;
-
-  meas_t *E, *clust, **M, *aM;
-
-  M = (meas_t **)malloc(q * sizeof(meas_t *));
-  aM = (meas_t *)calloc(q, sizeof(meas_t ));
-  for (q_t i = 0; i < q; i++) {
-    M[i] = (meas_t *)calloc(1, sizeof(meas_t));
-  }
-
-  E = calloc(1, sizeof(meas_t));
-  clust = calloc(1, sizeof(meas_t));
-
-  autocorr_t *autocorr;
-  if (record_autocorrelation) {
-    autocorr = (autocorr_t *)calloc(1, sizeof(autocorr_t));
-    autocorr->W = 2 * W + 1;
-    autocorr->OO = (double *)calloc(2 * W + 1, sizeof(double));
-  }
-
-  if (!silent) printf("\n");
-  while (((diff > eps || diff != diff) && n_runs < N) || n_runs < min_runs) {
-    if (!silent) printf("\033[F\033[JWOLFF: sweep %" PRIu64
-           ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
-           n_runs, fabs(meas_dx(E) / E->x), meas_dx(aM) / aM->x, meas_dc(E) / meas_c(E), meas_dc(aM) / meas_c(aM), h->nv / clust->x);
-
-    count_t n_flips = 0;
-
-    while (n_flips / h->nv < n) {
-      v_t v0 = gsl_rng_uniform_int(r, h->nv);
-      double *step = gen_rot(r, q);
-
-      v_t tmp_flips = flip_cluster_vector(s, v0, step, r);
-      free(step);
-      n_flips += tmp_flips;
-
-      if (n_runs > 0) {
-        n_steps++;
-        meas_update(clust, tmp_flips);
-
-        if (record_autocorrelation && n_steps % ac_skip == 0) {
-          update_autocorr(autocorr, s->E);
-        }
-      }
-    }
-
-    double aM_val = 0;
-
-    for (q_t i = 0; i < q; i++) {
-      meas_update(M[i], s->M[i]);
-      aM_val += s->M[i] * s->M[i];
-    }
-
-    meas_update(aM, sqrt(aM_val));
-    meas_update(E, s->E);
-
-    diff = fabs(meas_dx(clust) / clust->x);
-
-    n_runs++;
-  }
-
-  if (!silent) {
-    printf("\033[F\033[J");
-  }
-  printf("WOLFF: sweep %" PRIu64
-         ", dH/H = %.4f, dM/M = %.4f, dC/C = %.4f, dX/X = %.4f, cps: %.1f\n",
-         n_runs, fabs(meas_dx(E) / E->x), meas_dx(M[0]) / M[0]->x, meas_dc(E) / meas_c(E), meas_dc(M[0]) / meas_c(M[0]), h->nv / clust->x);
-
-  double tau = 0;
-  bool tau_failed = false;
-
-  if (record_autocorrelation) {
-    double *Gammas = (double *)malloc((W + 1) * sizeof(double));
-
-    Gammas[0] = 1 + rho(autocorr, 0);
-    for (uint64_t i = 0; i < W; i++) {
-      Gammas[1 + i] = rho(autocorr, 2 * i + 1) + rho(autocorr, 2 * i + 2);
-    } 
-
-    uint64_t n;
-    for (n = 0; n < W + 1; n++) {
-      if (Gammas[n] <= 0) {
-        break;
-      }
-    }
-
-    if (n == W + 1) {
-      printf("WARNING: correlation function never hit the noise floor.\n");
-      tau_failed = true;
-    }
-
-    if (n < 2) {
-      printf("WARNING: correlation function only has one nonnegative term.\n");
-      tau_failed = true;
-    }
-
-    double *conv_Gamma = get_convex_minorant(n, Gammas);
-
-    double ttau = - 0.5;
-
-    for (uint64_t i = 0; i < n + 1; i++) {
-      ttau += conv_Gamma[i];
-    }
-
-    FILE *autocorr_file = fopen("autocorr.dat", "a");
-
-    printf("%g %g\n", Gammas[0], conv_Gamma[0]);
-
-    for (count_t i = 0; i < n+1; i++) {
-      fprintf(autocorr_file, "%g ", conv_Gamma[i]);
-    }
-    fprintf(autocorr_file, "\n");
-
-    fclose(autocorr_file);
-    
-    free(Gammas);
-    free(autocorr->OO);
-    while (autocorr->Op != NULL) {
-      stack_pop_d(&(autocorr->Op));
-    }
-    free(autocorr);
-    
-    tau = ttau * ac_skip * clust->x / h->nv;
-  }
-
-  if (tau_failed) {
-    tau = 0;
-  }
-
-  FILE *outfile = fopen("out.m", "a");
-
-  fprintf(outfile, "<|N->%" PRIcount ",n->%" PRIcount ",D->%" PRID ",L->%" PRIL ",q->%" PRIq ",T->%.15f,H->{", N, n, D, L, q, T);
-  for (q_t i = 0; i < q; i++) {
-    fprintf(outfile, "%.15f", H[i]);
-    if (i != q-1) {
-      fprintf(outfile, ",");
-    }
-  }
-  fprintf(outfile, "},E->%.15f,\\[Delta]E->%.15f,C->%.15f,\\[Delta]C->%.15f,M->{", E->x / h->nv, meas_dx(E) / h->nv, meas_c(E) / h->nv, meas_dc(E) / h->nv);
-  for (q_t i = 0; i < q; i++) {
-    fprintf(outfile, "%.15f", M[i]->x / h->nv);
-    if (i != q-1) {
-      fprintf(outfile, ",");
-    }
-  }
-  fprintf(outfile, "},\\[Delta]M->{");
-  for (q_t i = 0; i < q; i++) {
-    fprintf(outfile, "%.15f", meas_dx(M[i]) / h->nv);
-    if (i != q-1) {
-      fprintf(outfile, ",");
-    }
-  }
-  fprintf(outfile, "},\\[Chi]->{");
-  for (q_t i = 0; i < q; i++) {
-    fprintf(outfile, "%.15f", meas_c(M[i]) / h->nv);
-    if (i != q-1) {
-      fprintf(outfile, ",");
-    }
-  }
-  fprintf(outfile, "},\\[Delta]\\[Chi]->{");
-  for (q_t i = 0; i < q; i++) {
-    fprintf(outfile, "%.15f", meas_dc(M[i]) / h->nv);
-    if (i != q-1) {
-      fprintf(outfile, ",");
-    }
-  }
-  fprintf(outfile, "},aM->%.15f,\\[Delta]aM->%.15f,a\\[Chi]->%.15f,\\[Delta]a\\[Chi]->%.15f,Subscript[n,\"clust\"]->%.15f,Subscript[\\[Delta]n,\"clust\"]->%.15f,Subscript[m,\"clust\"]->%.15f,Subscript[\\[Delta]m,\"clust\"]->%.15f,\\[Tau]->%.15f|>\n", aM->x / h->nv, meas_dx(aM) / h->nv, meas_c(aM) / h->nv, meas_dc(aM) / h->nv, clust->x / h->nv, meas_dx(clust) / h->nv, meas_c(clust) / h->nv, meas_dc(clust) / h->nv,tau);
-
-  fclose(outfile);
-
-  free(E);
-  free(clust);
-  for (q_t i = 0; i < q; i++) {
-    free(M[i]);
-  }
-  free(M);
-  free(H);
-  free(s->M);
-  free(s->R);
-  free(s->spins);
-  graph_free(s->g);
-  free(s);
-  graph_free(h);
-  gsl_rng_free(r);
-
-  return 0;
-}
-