From e5ce243a222b24a094a40391c79dc3ffcaf3f761 Mon Sep 17 00:00:00 2001
From: Aalhad-Bhatt <aalhadbhatt@gmail.com>
Date: Wed, 3 Sep 2025 15:47:55 -0400
Subject: [PATCH 1/4] Selective Sweep based on reading from a file with
 rudimentary ability to run on multiple demes

Code to run a selective sweep based on reading the events from a binary file in a specified format.  The C side also implements nearest neighbour migration with multiple demes. This does not interface with the Python version yet.
---
 lib/msprime.c            | 614 ++++++++++++++++++++++++++++++++++++++-
 lib/msprime.h            |  13 +-
 lib/tests/test_sweeps.c  |  66 ++++-
 msprime/__init__.py      |   1 +
 msprime/_msprimemodule.c |  76 ++++-
 msprime/ancestry.py      |  58 +++-
 6 files changed, 814 insertions(+), 14 deletions(-)

diff --git a/lib/msprime.c b/lib/msprime.c
index fc84f3cc3..b5d6c000b 100644
--- a/lib/msprime.c
+++ b/lib/msprime.c
@@ -4225,6 +4225,26 @@ msp_migration_event(msp_t *self, population_id_t source_pop, population_id_t des
     return ret;
 }
 
+/* Migration event in an arbitrary label
+ */
+static int MSP_WARN_UNUSED
+msp_migration_event_in_background(
+    msp_t *self, population_id_t source_pop, population_id_t dest_pop, label_id_t label)
+{
+    int ret = 0;
+    uint32_t j;
+    avl_node_t *node;
+    avl_tree_t *source = &self->populations[source_pop].ancestors[label];
+    size_t index = ((size_t) source_pop) * self->num_populations + (size_t) dest_pop;
+
+    self->num_migration_events[index]++;
+    j = (uint32_t) gsl_rng_uniform_int(self->rng, avl_count(source));
+    node = avl_at(source, j);
+    tsk_bug_assert(node != NULL);
+    ret = msp_move_individual(self, node, source, dest_pop, label);
+    return ret;
+}
+
 static int MSP_WARN_UNUSED
 msp_reset_memory_state(msp_t *self)
 {
@@ -5803,6 +5823,44 @@ msp_sweep_initialise(msp_t *self, double switch_proba)
     return ret;
 }
 
+/* Set up the intial populations for a sweep by moving individuals from
+ * label 0 to label 1 with the specified probability for multiple demes.
+ Should be merged with ms_sweep_initialise at some point.
+ */
+static int
+msp_sweep_reverse_initialise(msp_t *self, double *switch_proba)
+{
+    int ret = 0;
+    uint32_t j;
+    avl_node_t *node, *next;
+    avl_tree_t *pop;
+
+    /* We only support two labels for now */
+    if (self->num_labels != 2) {
+        ret = MSP_ERR_UNSUPPORTED_OPERATION;
+        goto out;
+    }
+
+    /* Move ancestors to new labels. */
+    for (j = 0; j < self->num_populations; j++) {
+        tsk_bug_assert(avl_count(&self->populations[j].ancestors[1]) == 0);
+        pop = &self->populations[j].ancestors[0];
+        node = pop->head;
+        while (node != NULL) {
+            next = node->next;
+            if (gsl_rng_uniform(self->rng) < switch_proba[j]) {
+                ret = msp_move_individual(self, node, pop, (population_id_t) j, 1);
+                if (ret != 0) {
+                    goto out;
+                }
+            }
+            node = next;
+        }
+    }
+out:
+    return ret;
+}
+
 /* Finalise the sweep by moving all lineages back to label 0.
  */
 static int
@@ -5882,6 +5940,230 @@ msp_sweep_recombination_event(
     return ret;
 }
 
+/* Recombination event during a sweep within a specified background with multiple demes.
+Should be merged with msp_sweep_recombination_event at some point.
+ */
+static int
+msp_sweep_reverse_recombination_event(msp_t *self, label_id_t label, double sweep_locus,
+    double *mutant_population_frequency)
+{
+    int ret = 0;
+    lineage_t *left_lin, *right_lin;
+    double r;
+
+    ret = msp_recombination_event(self, label, &left_lin, &right_lin);
+    if (ret != 0) {
+        goto out;
+    }
+
+    tsk_bug_assert(left_lin != NULL);
+    tsk_bug_assert(right_lin != NULL);
+
+    /* NOTE: we can look at rhs->left when we compare to the sweep site. */
+    r = gsl_rng_uniform(self->rng);
+    if (label == 0) {
+        if (sweep_locus < right_lin->head->left) {
+            if (r < mutant_population_frequency[right_lin->population]) {
+                ret = msp_change_label(self, right_lin, 1);
+                if (ret != 0) {
+                    goto out;
+                }
+            }
+        } else {
+            if (r < mutant_population_frequency[left_lin->population]) {
+                ret = msp_change_label(self, left_lin, 1);
+                if (ret != 0) {
+                    goto out;
+                }
+            }
+        }
+    }
+    if (label == 1) {
+        if (sweep_locus < right_lin->head->left) {
+            if (r < 1.0 - mutant_population_frequency[right_lin->population]) {
+                ret = msp_change_label(self, right_lin, 0);
+                if (ret != 0) {
+                    goto out;
+                }
+            }
+        } else {
+            if (r < 1.0 - mutant_population_frequency[left_lin->population]) {
+                ret = msp_change_label(self, left_lin, 0);
+                if (ret != 0) {
+                    goto out;
+                }
+            }
+        }
+    }
+out:
+    return ret;
+}
+
+/* Reads the trajectory of a sweep when fed a file.
+The file contains (in order)
+1. Number of steps
+2. Number of demes
+3. Pop size
+4. Migration rate
+5. State after finishing the backward sim
+6. state at the start of the backward sim
+7. Time, type, and start and end locations for each event
+No. 7 is currently read backward in time as well
+ */
+static int
+genic_selection_read_trajectory(const char *filename, size_t *num_steps_ret,
+    int *num_demes_ret, int *tot_pop_ret, double *migration_rate_ret,
+    int **final_mut_pop_ret, int **mut_pop_ret, double **allele_frequency_mut_ret,
+    double **t_of_forward_ev_ret, int **ev_type_ret, int **start_deme_ret,
+    int **end_deme_ret)
+{
+    int ret = 0;
+    size_t num_steps;
+    int num_demes;
+    double *allele_frequency_mut = NULL;
+    double *t_of_forward_ev = NULL;
+    size_t j = 0;
+    int i = 0;
+    double migration_rate = 0.0;
+    int tot_pop = 0;
+    int *mut_pop = NULL, *final_mut_pop = NULL;
+    int *ev_type = NULL, *start_deme = NULL, *end_deme = NULL;
+    FILE *file = fopen(filename, "r");
+
+    fread(&num_steps, sizeof(size_t), 1, file);
+    fread(&num_demes, sizeof(int), 1, file);
+    fread(&tot_pop, sizeof(int), 1, file);
+    fread(&migration_rate, sizeof(double), 1, file);
+
+    mut_pop = (int *) malloc(sizeof(*mut_pop) * (size_t) num_demes);
+    allele_frequency_mut
+        = (double *) malloc(sizeof(*allele_frequency_mut) * (size_t) num_demes);
+    final_mut_pop = (int *) malloc(sizeof(*final_mut_pop) * (size_t) num_demes);
+
+    for (i = 0; i < num_demes; i++) {
+        fread(&final_mut_pop[i], sizeof(int), 1, file);
+    }
+
+    for (i = 0; i < num_demes; i++) {
+        fread(&mut_pop[i], sizeof(int), 1, file);
+        allele_frequency_mut[i] = 1.0 * mut_pop[i] / tot_pop;
+    }
+
+    t_of_forward_ev = (double *) malloc(sizeof(*t_of_forward_ev) * num_steps);
+    ev_type = (int *) malloc(sizeof(*ev_type) * num_steps);
+    start_deme = (int *) malloc(sizeof(*start_deme) * num_steps);
+    end_deme = (int *) malloc(sizeof(*end_deme) * num_steps);
+    for (j = 1; j <= num_steps; j++) {
+        fread(&t_of_forward_ev[num_steps - j], sizeof(double), 1, file);
+        fread(&ev_type[num_steps - j], sizeof(int), 1, file);
+        fread(&start_deme[num_steps - j], sizeof(int), 1, file);
+        fread(&end_deme[num_steps - j], sizeof(int), 1, file);
+    }
+
+    fclose(file);
+    *num_demes_ret = num_demes;
+    *num_steps_ret = num_steps;
+    *tot_pop_ret = tot_pop;
+    *migration_rate_ret = migration_rate;
+    *final_mut_pop_ret = final_mut_pop;
+    *mut_pop_ret = mut_pop;
+    *allele_frequency_mut_ret = allele_frequency_mut;
+    *t_of_forward_ev_ret = t_of_forward_ev;
+    *ev_type_ret = ev_type;
+    *start_deme_ret = start_deme;
+    *end_deme_ret = end_deme;
+    return ret;
+}
+
+static int
+sweep_forward_event(msp_t *self, double t_of_next_forward_ev, int *ev_type,
+    size_t *curr_step, int *start_deme, int *end_deme, double **ancestral_bg_num_ind,
+    int **mut_pop, double **allele_frequency_mut, int tot_pop, int num_demes)
+{
+    int ret = 0;
+    int curr_ev_type = 0, start_deme_index = 0;
+    double p_forward_ev, tmp_rand;
+
+    self->time = t_of_next_forward_ev;
+    curr_ev_type = ev_type[*curr_step];
+    start_deme_index = start_deme[*curr_step];
+
+    if (start_deme_index == end_deme[*curr_step]) {
+        if (curr_ev_type == 0) { // mutant replaced with wildtype, mutant coalascence
+            p_forward_ev
+                = (float) ancestral_bg_num_ind[start_deme_index][1]
+                  * (ancestral_bg_num_ind[start_deme_index][1] - 1.0)
+                  / ((*mut_pop[start_deme_index]) * (*mut_pop[start_deme_index] - 1.0));
+            tmp_rand = gsl_rng_uniform(self->rng);
+            if (tmp_rand <= p_forward_ev) {
+                ret = self->common_ancestor_event(self, start_deme_index, 1);
+            }
+            (*mut_pop[start_deme_index])--;
+            *allele_frequency_mut[start_deme_index]
+                = 1.0 * *mut_pop[start_deme_index] / tot_pop;
+            (*curr_step)++;
+
+        } else if (curr_ev_type == 1) { // wildtype replaced with mutant, wt coalescence
+            p_forward_ev = (float) ancestral_bg_num_ind[start_deme_index][0]
+                           * (ancestral_bg_num_ind[start_deme_index][0] - 1.0)
+                           / ((tot_pop - *mut_pop[start_deme_index])
+                                 * ((tot_pop - *mut_pop[start_deme_index]) - 1.0));
+            tmp_rand = gsl_rng_uniform(self->rng);
+            if (tmp_rand <= p_forward_ev) {
+                ret = self->common_ancestor_event(self, start_deme_index, 0);
+            }
+            (*mut_pop[start_deme_index])++;
+            *allele_frequency_mut[start_deme_index]
+                = 1.0 * *mut_pop[start_deme_index] / tot_pop;
+            (*curr_step)++;
+        }
+
+    } else if (num_demes > 1) {
+
+        if (curr_ev_type == 2) { // wildtype replaced by mutant, mutant migration
+                                 // + coalascence
+            p_forward_ev = 1.0 * ancestral_bg_num_ind[start_deme_index][1]
+                           / *mut_pop[start_deme_index];
+            tmp_rand = gsl_rng_uniform(self->rng);
+            if (tmp_rand <= p_forward_ev) {
+                ret = msp_migration_event_in_background(
+                    self, start_deme_index, end_deme[*curr_step], 1);
+                p_forward_ev = 1.0 * ancestral_bg_num_ind[end_deme[*curr_step]][1]
+                               / *mut_pop[end_deme[*curr_step]];
+                tmp_rand = gsl_rng_uniform(self->rng);
+                if (tmp_rand <= p_forward_ev) {
+                    ret = self->common_ancestor_event(self, end_deme[*curr_step], 1);
+                }
+            }
+            (*mut_pop[start_deme[*curr_step]])--;
+            *allele_frequency_mut[start_deme[*curr_step]]
+                = 1.0 * *mut_pop[start_deme[*curr_step]] / tot_pop;
+            (*curr_step)++;
+
+        } else if (curr_ev_type == 3) { // mutant replaced by wildtype, wildtype
+                                        // migration + coalascence
+            p_forward_ev = 1.0 * ancestral_bg_num_ind[start_deme_index][0]
+                           / (tot_pop - *mut_pop[start_deme_index]);
+            tmp_rand = gsl_rng_uniform(self->rng);
+            if (tmp_rand <= p_forward_ev) {
+                ret = msp_migration_event_in_background(
+                    self, start_deme_index, end_deme[*curr_step], 0);
+                p_forward_ev = 1.0 * ancestral_bg_num_ind[end_deme[*curr_step]][0]
+                               / (tot_pop - *mut_pop[end_deme[*curr_step]]);
+                tmp_rand = gsl_rng_uniform(self->rng);
+                if (tmp_rand <= p_forward_ev) {
+                    ret = self->common_ancestor_event(self, end_deme[*curr_step], 0);
+                }
+            }
+            (*mut_pop[start_deme[*curr_step]])++;
+            *allele_frequency_mut[start_deme[*curr_step]]
+                = 1.0 * *mut_pop[start_deme[*curr_step]] / tot_pop;
+            (*curr_step)++;
+        }
+    }
+    return ret;
+}
+
 static int
 msp_run_sweep(msp_t *self)
 {
@@ -6064,6 +6346,304 @@ msp_run_sweep(msp_t *self)
     return ret;
 }
 
+/* Runs a backward in time sweep after reading the forward trajectory from a file
+ */
+static int
+msp_run_sweep_reverse(msp_t *self)
+{
+    int ret = 0;
+    simulation_model_t *model = &self->model;
+    size_t curr_step = 0;
+    size_t num_steps = 0;
+    double *allele_frequency_mut = NULL, *t_of_forward_ev = NULL;
+    int *ev_type = NULL, *start_deme = NULL, *end_deme = NULL;
+    double sweep_locus = model->params.sweep_reverse.position;
+    const char *filename = model->params.sweep_reverse.filename;
+    size_t j = 0;
+    int i = 0;
+    double recomb_mass = 0.0;
+    label_id_t label;
+    double rec_rates[] = { 0.0, 0.0 };
+    double **ancestral_bg_num_ind = NULL;
+    double *p_coal_wild = NULL, *p_coal_mut = NULL, *p_mig_wild_right = NULL,
+           *p_mig_mut_right = NULL, *p_mig_wild_left = NULL, *p_mig_mut_left = NULL;
+    double p_rec_wild = 0.0, p_rec_mut = 0.0;
+    double tmp_rand = 0.0, p_any_ev = 0.0, p_cum_ev = 0.0, t_of_next_forward_ev = 0.0;
+    double t_start = 0.0, t_end = 0.0, t_of_next_ev = 0.0, t_current = 0.0;
+    int num_demes = 0;
+    double migration_rate = 0.0;
+    int tot_pop = 0;
+    int *mut_pop = NULL, *final_mut_pop = NULL;
+    bool no_event_yet = true;
+
+    if (rate_map_get_total_mass(&self->gc_map) != 0.0) { // arrow?+
+        /* Could be, we just haven't implemented it */
+        ret = MSP_ERR_SWEEPS_GC_NOT_SUPPORTED;
+        goto out;
+    }
+
+    ret = genic_selection_read_trajectory(filename, &num_steps, &num_demes, &tot_pop,
+        &migration_rate, &final_mut_pop, &mut_pop, &allele_frequency_mut,
+        &t_of_forward_ev, &ev_type, &start_deme, &end_deme);
+
+    tsk_bug_assert(num_demes > 0);
+    tsk_bug_assert(tot_pop > 0);
+
+    p_coal_wild = (double *) malloc(sizeof(double) * (size_t) num_demes);
+    p_coal_mut = (double *) malloc(sizeof(double) * (size_t) num_demes);
+    if (num_demes > 1) {
+        p_mig_wild_right = (double *) malloc(sizeof(double) * (size_t)(num_demes - 1));
+        p_mig_mut_right = (double *) malloc(sizeof(double) * (size_t)(num_demes - 1));
+        p_mig_wild_left = (double *) malloc(sizeof(double) * (size_t)(num_demes - 1));
+        p_mig_mut_left = (double *) malloc(sizeof(double) * (size_t)(num_demes - 1));
+    }
+
+    ancestral_bg_num_ind = (double **) malloc(sizeof(double *) * (size_t) num_demes);
+    for (i = 0; i < num_demes; i++) {
+        ancestral_bg_num_ind[i] = (double *) malloc(sizeof(double) * 2);
+    }
+
+    curr_step = 0;
+
+    ret = msp_sweep_reverse_initialise(self, allele_frequency_mut);
+    if (ret != 0) {
+        goto out;
+    }
+
+    /* Sets the initial time of the sweep. Adds a small increment after the final event.
+     * Also makes the times of the forward step consistent with msprime
+     */
+    t_start = self->time;
+    t_current = t_start;
+    t_of_next_ev = 0.0;
+    t_end = t_of_forward_ev[0];
+    for (j = 0; j < num_steps; j++) { /*move this step to file io*/
+        t_of_forward_ev[j] = t_start + t_end - t_of_forward_ev[j]
+                             + 1e-5; /*Add a small jitter to avoid time travel*/
+    }
+
+    while (msp_get_num_ancestors(self) > 0 && curr_step < num_steps
+           && self->time < (t_start + t_end)) {
+        /* Set num ancestral individuals & rec_rates */
+        for (j = 0; j < self->num_labels; j++) {
+            label = (label_id_t) j;
+            recomb_mass = self->recomb_mass_index == NULL
+                              ? 0
+                              : fenwick_get_total(&self->recomb_mass_index[label]);
+            for (i = 0; i < num_demes; i++) {
+                ancestral_bg_num_ind[i][j]
+                    = avl_count(&self->populations[i].ancestors[label]);
+                /* We can get small negative rates by numerical jitter which causes
+                 * problems in later calculations and leads to assertion trips.
+                 * See https://github.com/tskit-dev/msprime/issues/1966
+                 */
+            }
+            rec_rates[j] = TSK_MAX(0, (recomb_mass));
+        }
+
+        /* Set coal and mig rates*/
+
+        for (i = 0; i < num_demes; i++) {
+            if (ancestral_bg_num_ind[i][0] > 1) {
+                tsk_bug_assert(mut_pop[i] != tot_pop);
+                p_coal_wild[i]
+                    = ((ancestral_bg_num_ind[i][0] * (ancestral_bg_num_ind[i][0] - 1.0))
+                          * 0.5)
+                      / (tot_pop - mut_pop[i]);
+            } else {
+                p_coal_wild[i] = 0;
+            }
+            if (ancestral_bg_num_ind[i][1] > 1) {
+                tsk_bug_assert(mut_pop[i] != 0);
+                p_coal_mut[i]
+                    = ((ancestral_bg_num_ind[i][1] * (ancestral_bg_num_ind[i][1] - 1.0))
+                          * 0.5)
+                      / mut_pop[i];
+            } else {
+                p_coal_mut[i] = 0;
+            }
+
+            if (num_demes > 1) {
+                if (i > 0) {
+                    p_mig_wild_left[i - 1] = migration_rate * ancestral_bg_num_ind[i][0]
+                                             * (1.0 - mut_pop[i - 1] / tot_pop);
+                    p_mig_mut_left[i - 1] = migration_rate * ancestral_bg_num_ind[i][1]
+                                            * mut_pop[i - 1] / tot_pop;
+                }
+                if (i < num_demes - 1) {
+                    p_mig_wild_right[i] = migration_rate * ancestral_bg_num_ind[i][0]
+                                          * (1.0 - mut_pop[i + 1] / tot_pop);
+                    p_mig_mut_right[i] = migration_rate * ancestral_bg_num_ind[i][1]
+                                         * mut_pop[i + 1] / tot_pop;
+                }
+            }
+        }
+
+        p_rec_wild = rec_rates[0];
+        p_rec_mut = rec_rates[1];
+        p_any_ev = 0;
+
+        /* Find rate of any event */
+        for (i = 0; i < num_demes; i++) {
+            p_any_ev += p_coal_wild[i];
+            p_any_ev += p_coal_mut[i];
+        }
+
+        if (num_demes > 1) {
+            for (i = 0; i < (num_demes - 1); i++) {
+                p_any_ev += p_mig_wild_left[i];
+                p_any_ev += p_mig_mut_left[i];
+                p_any_ev += p_mig_wild_right[i];
+                p_any_ev += p_mig_mut_right[i];
+            }
+        }
+
+        p_any_ev += p_rec_wild;
+        p_any_ev += p_rec_mut;
+
+        t_current = self->time;
+        t_of_next_ev = gsl_ran_exponential(self->rng, 1 / p_any_ev);
+        tsk_bug_assert(t_of_next_ev > 0);
+
+        t_of_next_forward_ev = t_of_forward_ev[curr_step];
+
+        if (t_current + t_of_next_ev >= t_of_next_forward_ev) {
+
+            ret = sweep_forward_event(self, t_of_next_forward_ev, ev_type, &curr_step,
+                start_deme, end_deme, ancestral_bg_num_ind, &mut_pop,
+                &allele_frequency_mut, tot_pop, num_demes);
+
+        } else {
+            self->time += t_of_next_ev;
+            p_cum_ev = 0.0;
+            no_event_yet = true;
+            tmp_rand = gsl_rng_uniform(self->rng);
+
+            for (i = 0; i < num_demes; i++) {
+                p_cum_ev += p_coal_wild[i];
+                if (tmp_rand < p_cum_ev / p_any_ev) {
+                    ret = self->common_ancestor_event(self, i, 0);
+                    no_event_yet = false;
+                    break;
+                } else {
+                    p_cum_ev += p_coal_mut[i];
+                    if (tmp_rand < p_cum_ev / p_any_ev) {
+                        ret = self->common_ancestor_event(self, i, 1);
+                        no_event_yet = false;
+                        break;
+                    }
+                }
+            }
+
+            if (num_demes > 1) {
+                if (no_event_yet) {
+                    for (i = 0; i < (num_demes - 1); i++) {
+                        p_cum_ev += p_mig_wild_left[i];
+                        if (tmp_rand < p_cum_ev / p_any_ev) {
+                            ret = msp_migration_event_in_background(self, i + 1, i, 0);
+                            no_event_yet = false;
+                            break;
+                        } else {
+                            p_cum_ev += p_mig_mut_left[i];
+                            if (tmp_rand < p_cum_ev / p_any_ev) {
+                                ret = msp_migration_event_in_background(
+                                    self, i + 1, i, 1);
+                                no_event_yet = false;
+                                break;
+                            } else {
+                                p_cum_ev += p_mig_wild_right[i];
+                                if (tmp_rand < p_cum_ev / p_any_ev) {
+                                    ret = msp_migration_event_in_background(
+                                        self, i, i + 1, 0);
+                                    no_event_yet = false;
+                                    break;
+                                } else {
+                                    p_cum_ev += p_mig_mut_right[i];
+                                    if (tmp_rand < p_cum_ev / p_any_ev) {
+                                        ret = msp_migration_event_in_background(
+                                            self, i, i + 1, 1);
+                                        no_event_yet = false;
+                                        break;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+
+            if (no_event_yet) {
+                p_cum_ev += p_rec_wild;
+                if (tmp_rand < p_cum_ev / p_any_ev) {
+                    ret = msp_sweep_reverse_recombination_event(
+                        self, 0, sweep_locus, allele_frequency_mut);
+                    no_event_yet = false;
+                }
+            }
+            if (no_event_yet) {
+                p_cum_ev += p_rec_mut;
+                if (tmp_rand < p_cum_ev / p_any_ev) {
+                    ret = msp_sweep_reverse_recombination_event(
+                        self, 1, sweep_locus, allele_frequency_mut);
+                    no_event_yet = false;
+                }
+            }
+        }
+
+        if (ret != 0) {
+            goto out;
+        }
+    }
+
+    /* TODO we should probably support fixed events here using
+     * msp_apply_fixed_events() like we do in the coalescent models.
+     * The point below about computing population sizes should be easily
+     * worked around using msp_compute_population_size(). */
+
+    /* Check if any demographic events should have happened during the
+     * event and raise an error if so. This is to keep computing population
+     * sizes simple */
+    if (self->next_demographic_event != NULL
+        && self->next_demographic_event->time <= self->time) {
+        ret = MSP_ERR_EVENTS_DURING_SWEEP;
+        goto out;
+    }
+    /* Rule out sampling events for now, but there's no reason we can't
+     * support them. */
+    if (self->next_sampling_event < self->num_sampling_events
+        && self->sampling_events[self->next_sampling_event].time <= self->time) {
+        ret = MSP_ERR_EVENTS_DURING_SWEEP;
+        goto out;
+    }
+    ret = msp_sweep_finalise(self);
+    if (ret != 0) {
+        goto out;
+    }
+    ret = MSP_EXIT_MODEL_COMPLETE;
+
+out:
+    msp_safe_free(p_coal_mut);
+    msp_safe_free(p_coal_wild);
+    if (num_demes > 1) {
+        msp_safe_free(p_mig_wild_left);
+        msp_safe_free(p_mig_mut_left);
+        msp_safe_free(p_mig_wild_right);
+        msp_safe_free(p_mig_mut_right);
+    }
+    for (i = 0; i < num_demes; i++) {
+        msp_safe_free(ancestral_bg_num_ind[i]);
+    }
+    msp_safe_free(ancestral_bg_num_ind);
+    msp_safe_free(mut_pop);
+    msp_safe_free(allele_frequency_mut);
+    msp_safe_free(final_mut_pop);
+    msp_safe_free(t_of_forward_ev);
+    msp_safe_free(ev_type);
+    msp_safe_free(start_deme);
+    msp_safe_free(end_deme);
+    return ret;
+}
+
 /* Runs the simulation backwards in time until either the sample has coalesced,
  * or specified maximum simulation time has been reached or the specified maximum
  * number of events has been reached.
@@ -6093,6 +6673,8 @@ msp_run(msp_t *self, double max_time, unsigned long max_events)
     } else if (self->model.type == MSP_MODEL_SWEEP) {
         /* FIXME making sweep atomic for now as it's non-rentrant */
         ret = msp_run_sweep(self);
+    } else if (self->model.type == MSP_MODEL_SWEEP_REVERSE) {
+        ret = msp_run_sweep_reverse(self);
     } else {
         ret = msp_run_coalescent(self, max_time, max_events);
     }
@@ -6345,6 +6927,9 @@ msp_get_model_name(msp_t *self)
         case MSP_MODEL_SWEEP:
             ret = "single-sweep";
             break;
+        case MSP_MODEL_SWEEP_REVERSE:
+            ret = "single-sweep, reverse only";
+            break;
         default:
             ret = "BUG: bad model in simulator!";
             break;
@@ -8171,7 +8756,8 @@ msp_set_simulation_model(msp_t *self, int model)
     if (model != MSP_MODEL_HUDSON && model != MSP_MODEL_SMC
         && model != MSP_MODEL_SMC_PRIME && model != MSP_MODEL_SMC_K
         && model != MSP_MODEL_DIRAC && model != MSP_MODEL_BETA && model != MSP_MODEL_DTWF
-        && model != MSP_MODEL_WF_PED && model != MSP_MODEL_SWEEP) {
+        && model != MSP_MODEL_WF_PED && model != MSP_MODEL_SWEEP
+        && model != MSP_MODEL_SWEEP_REVERSE) {
         ret = MSP_ERR_BAD_MODEL;
         goto out;
     }
@@ -8454,3 +9040,29 @@ msp_set_simulation_model_sweep_genic_selection(msp_t *self, double position,
 out:
     return ret;
 }
+
+int
+msp_set_simulation_model_sweep_genic_selection_reverse(
+    msp_t *self, double position, const char *filename)
+{
+    int ret = 0;
+    simulation_model_t *model = &self->model;
+    double L = self->sequence_length;
+
+    /* Check the inputs to make sure they make sense */
+    if (position < 0 || position >= L) {
+        ret = MSP_ERR_BAD_SWEEP_POSITION;
+        goto out;
+    }
+
+    ret = msp_set_simulation_model(self, MSP_MODEL_SWEEP_REVERSE);
+    if (ret != 0) {
+        goto out;
+    }
+
+    model->params.sweep_reverse.position = position;
+    model->params.sweep_reverse.filename = filename;
+
+out:
+    return ret;
+}
\ No newline at end of file
diff --git a/lib/msprime.h b/lib/msprime.h
index c352ef539..e84799371 100644
--- a/lib/msprime.h
+++ b/lib/msprime.h
@@ -43,8 +43,9 @@
 #define MSP_MODEL_DIRAC 4
 #define MSP_MODEL_DTWF 5
 #define MSP_MODEL_SWEEP 6
-#define MSP_MODEL_WF_PED 7
-#define MSP_MODEL_SMC_K 8
+#define MSP_MODEL_SWEEP_REVERSE 7
+#define MSP_MODEL_WF_PED 8
+#define MSP_MODEL_SMC_K 9
 
 /* Exit codes from msp_run to distinguish different reasons for exiting
  * before coalescence. */
@@ -231,6 +232,11 @@ typedef struct _sweep_t {
     void (*print_state)(struct _sweep_t *self, FILE *out);
 } sweep_t;
 
+typedef struct _sweep_reverse_t {
+    double position;
+    const char *filename;
+} sweep_reverse_t;
+
 typedef struct _simulation_model_t {
     int type;
     union {
@@ -238,6 +244,7 @@ typedef struct _simulation_model_t {
         beta_coalescent_t beta_coalescent;
         dirac_coalescent_t dirac_coalescent;
         sweep_t sweep;
+        sweep_reverse_t sweep_reverse;
     } params;
     /* If the model allocates memory this function should be non-null. */
     void (*free)(struct _simulation_model_t *model);
@@ -491,6 +498,8 @@ int msp_set_simulation_model_dirac(msp_t *self, double psi, double c);
 int msp_set_simulation_model_beta(msp_t *self, double alpha, double truncation_point);
 int msp_set_simulation_model_sweep_genic_selection(msp_t *self, double position,
     double start_frequency, double end_frequency, double s, double dt);
+int msp_set_simulation_model_sweep_genic_selection_reverse(
+    msp_t *self, double position, const char *filename);
 
 int msp_set_start_time(msp_t *self, double start_time);
 int msp_set_store_migrations(msp_t *self, bool store_migrations);
diff --git a/lib/tests/test_sweeps.c b/lib/tests/test_sweeps.c
index 93f107750..b070bfc3c 100644
--- a/lib/tests/test_sweeps.c
+++ b/lib/tests/test_sweeps.c
@@ -330,7 +330,7 @@ sweep_genic_selection_mimic_msms_single_run(unsigned long int seed)
     double s = 10000;
     double recom_rate = 0.0004;
     double start_frequency = 0.5 / 10000;
-    double end_frequency = 0.9;
+    double end_frequency = 1 - 0.5 / 10000;
     double dt = 1.0 / 400000;
     msp_t msp;
     gsl_rng *rng = safe_rng_alloc();
@@ -370,6 +370,61 @@ sweep_genic_selection_mimic_msms_single_run(unsigned long int seed)
     tsk_table_collection_free(&tables);
 }
 
+static void
+sweep_genic_selection_reverse_single_run(unsigned long int seed)
+{
+
+    /* Try to mimic the msms parameters used in verification.py
+           "100 300 -t 200 -r 200 500000"
+           " -SF 0 0.9 -Sp 0.5 -SaA 5000 -SAA 10000 -N 10000"
+     */
+    int ret;
+    const char *filename
+        = "/home/aalhadbhatt/Desktop/msprime/notebooks/PyNBmsprime/events.bin";
+    uint32_t n = 10;
+    double num_loci = 500001;
+    double position = num_loci / 2;
+    size_t num_demes = 1;
+    double recom_rate = 0.0004;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+
+    // Test over differnt seeds
+    gsl_rng_set(rng, seed);
+
+    ret = build_sim(&msp, &tables, rng, num_loci, num_demes, NULL, n);
+    CU_ASSERT_EQUAL(ret, 0);
+    CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
+    ret = msp_set_num_labels(&msp, 2);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    // To mimic the verfication.py call
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(
+        &msp, position, filename);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    msp_free(&msp);
+    gsl_rng_free(rng);
+    // free(samples);
+    tsk_table_collection_free(&tables);
+}
+
 static void
 test_sweep_genic_selection_mimic_msms(void)
 {
@@ -379,6 +434,14 @@ test_sweep_genic_selection_mimic_msms(void)
     }
 }
 
+static void
+test_sweep_genic_selection_reverse(void)
+{
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 300; i++)
+        sweep_genic_selection_reverse_single_run(i + 1);
+}
+
 int
 main(int argc, char **argv)
 {
@@ -395,6 +458,7 @@ main(int argc, char **argv)
             test_sweep_genic_selection_time_change },
         { "test_sweep_genic_selection_mimic_msms",
             test_sweep_genic_selection_mimic_msms },
+        { "test_sweep_genic_selection_reverse", test_sweep_genic_selection_reverse },
         CU_TEST_INFO_NULL,
     };
 
diff --git a/msprime/__init__.py b/msprime/__init__.py
index 63b18fe4b..cc903a225 100644
--- a/msprime/__init__.py
+++ b/msprime/__init__.py
@@ -47,6 +47,7 @@
     StandardCoalescent,
     SweepGenicSelection,
     FixedPedigree,
+    SweepGenicSelectionReverse,
     TimeUnitsMismatchWarning,
     NodeType,
 )
diff --git a/msprime/_msprimemodule.c b/msprime/_msprimemodule.c
index b3de9398e..a3c794e05 100644
--- a/msprime/_msprimemodule.c
+++ b/msprime/_msprimemodule.c
@@ -151,6 +151,20 @@ get_dict_number(PyObject *dict, const char *key_str)
     return ret;
 }
 
+static PyObject *
+get_dict_string(PyObject *dict, const char *key_str)
+{
+    PyObject *ret = NULL;
+    PyObject *value;
+    value = get_required_dict_value(dict, key_str);
+    if (value == NULL) {
+        goto out;
+    }
+    ret = value;
+out:
+    return ret;
+}
+
 static int
 parse_rate_map(PyObject *py_rate_map, size_t *ret_size,
         PyArrayObject **ret_position, PyArrayObject **ret_rate)
@@ -1063,6 +1077,35 @@ Simulator_parse_sweep_genic_selection_model(Simulator *self, PyObject *py_model)
     return ret;
 }
 
+static int
+Simulator_parse_sweep_genic_selection_reverse_model(Simulator *self, PyObject *py_model)
+{
+    int ret = -1;
+    int err;
+    double position;
+    const char *filename;
+    PyObject *value;
+    value = get_dict_number(py_model, "position");
+    if (value == NULL) {
+        goto out;
+    }
+    position = PyFloat_AsDouble(value);
+    value = get_dict_string(py_model, "filename");
+    if (value == NULL) {
+        goto out;
+    }
+    filename = PyUnicode_AsUTF8(value);
+    err = msp_set_simulation_model_sweep_genic_selection_reverse(self->sim,
+            position, filename);
+    if (err != 0) {
+        handle_input_error("sweep genic selection reverse", err);
+        goto out;
+    }
+    ret = 0;
+out:
+    return ret;
+}
+
 static int
 Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
 {
@@ -1078,9 +1121,10 @@ Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
     PyObject *dirac_s = NULL;
     PyObject *beta_s = NULL;
     PyObject *sweep_genic_selection_s = NULL;
+    PyObject *sweep_genic_selection_reverse_s = NULL;
     PyObject *value;
     int is_hudson, is_dtwf, is_smc, is_smc_prime, is_smc_k, is_dirac, is_beta,
-        is_sweep_genic_selection, is_fixed_pedigree;
+        is_sweep_genic_selection, is_sweep_genic_selection_reverse, is_fixed_pedigree;
     double psi, c, alpha, truncation_point, hull_offset;
 
     hudson_s = Py_BuildValue("s", "hudson");
@@ -1120,6 +1164,11 @@ Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
         goto out;
     }
 
+    sweep_genic_selection_reverse_s = Py_BuildValue("s", "sweep_genic_selection_reverse");
+    if (sweep_genic_selection_reverse_s == NULL) {
+        goto out;
+    }
+
     py_name = get_required_dict_value(py_model, "name");
     if (py_name == NULL) {
         goto out;
@@ -1242,8 +1291,20 @@ Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
         }
     }
 
+    is_sweep_genic_selection_reverse = PyObject_RichCompareBool(py_name,
+            sweep_genic_selection_reverse_s, Py_EQ);
+    if (is_sweep_genic_selection_reverse == -1) {
+        goto out;
+    }
+    if (is_sweep_genic_selection_reverse) {
+        ret = Simulator_parse_sweep_genic_selection_reverse_model(self, py_model);
+        if (ret != 0) {
+            goto out;
+        }
+    }
+
     if (! (is_hudson || is_dtwf || is_smc || is_smc_prime || is_smc_k || is_dirac
-                || is_beta || is_sweep_genic_selection || is_fixed_pedigree)) {
+                || is_beta || is_sweep_genic_selection || is_sweep_genic_selection_reverse || is_fixed_pedigree)) {
         PyErr_SetString(PyExc_ValueError, "Unknown simulation model");
         goto out;
     }
@@ -1262,6 +1323,7 @@ Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
     Py_XDECREF(beta_s);
     Py_XDECREF(dirac_s);
     Py_XDECREF(sweep_genic_selection_s);
+    Py_XDECREF(sweep_genic_selection_reverse_s);
     return ret;
 }
 
@@ -2024,6 +2086,16 @@ Simulator_get_model(Simulator *self, void *closure)
         Py_DECREF(value);
         value = NULL;
         /* TODO fill in the parameters for the different types of trajectories. */
+    } else if (model->type == MSP_MODEL_SWEEP_REVERSE) {
+        value = Py_BuildValue("d", model->params.sweep.position);
+        if (value == NULL) {
+            goto out;
+        }
+        if (PyDict_SetItemString(d, "locus", value) != 0) {
+            goto out;
+        }
+        Py_DECREF(value);
+        value = NULL;
     } else if (model->type == MSP_MODEL_SMC_K) {
         value = Py_BuildValue("d", model->params.smc_k_coalescent.hull_offset);
         if (value == NULL) {
diff --git a/msprime/ancestry.py b/msprime/ancestry.py
index 72b45b5d8..74af53c23 100644
--- a/msprime/ancestry.py
+++ b/msprime/ancestry.py
@@ -833,13 +833,6 @@ def _parse_sim_ancestry(
     models = _parse_model_arg(model)
     is_dtwf = isinstance(models[0], DiscreteTimeWrightFisher)
     is_pedigree = any(isinstance(model, FixedPedigree) for model in models)
-    is_smck = any(isinstance(model, SmcKApproxCoalescent) for model in models)
-
-    if is_smck and gene_conversion_rate is not None:
-        raise ValueError(
-            "Gene conversion is not supported for the SmcKApproxCoalescent model. "
-            "Please refer to issue #2399 on GitHub for details."
-        )
 
     if record_full_arg:
         if coalescing_segments_only is not None:
@@ -1486,7 +1479,9 @@ def _choose_num_labels(self, models):
         """
         num_labels = 1
         for model in models:
-            if isinstance(model, SweepGenicSelection):
+            if isinstance(model, SweepGenicSelection) or isinstance(
+                model, SweepGenicSelectionReverse
+            ):
                 num_labels = 2
         return num_labels
 
@@ -2116,3 +2111,50 @@ def __init__(
         self.end_frequency = end_frequency
         self.s = s
         self.dt = dt
+
+
+@dataclasses.dataclass
+class SweepGenicSelectionReverse(ParametricAncestryModel):
+    """
+    This is a modification of SweepGenicSelection such that the user feeds the
+    filename for a binary file containing the forward in time trajectory of the sweep
+    for a single allele and the simulator computes the trajectory for the entire
+    genome.
+
+    For more details see the definition of class SweepGenicSelection
+
+
+    .. warning::
+        Currently models with more than one population and a selective sweep
+        are not implemented. Population size changes during the sweep
+        are not yet possible in msprime.
+
+    :param float position: the location of the beneficial allele along the
+        chromosome.
+    :param filename: The path of the file that contains the forward trajectory
+        computed from a Guillespie simulation of a logistic birth and death
+        process stored in a binary file
+        Currently the file has the following format:
+        1. Number of events (uintp)
+        2. Number of demes (int) - currently 0
+        3. Population size (int)
+        4. Migration rate (float) - currently 0
+        5. The initial state of the forward sim  (1 int per deme)
+        6. the final state of the forward sim (1 int per deme)
+        7. For each event there is also:
+        a. time of event (float)
+        b. type of event (0 for wt birth, 1 for mutant birth - int)
+        c. deme of birth (int)
+        d. deme of parent (int)
+    """
+
+    name = "sweep_genic_selection_reverse"
+
+    position: float | None
+    filename: str | None
+
+    # We have to define an __init__ to enforce keyword-only behaviour
+    def __init__(self, *, duration=None, position=None, filename=None):
+        self.duration = duration
+        self.position = position
+        self.filename = filename

From fbf924397b2887a6f274339c952a38b24b53214a Mon Sep 17 00:00:00 2001
From: Aalhad-Bhatt <aalhadbhatt@gmail.com>
Date: Wed, 24 Sep 2025 16:51:51 -0400
Subject: [PATCH 2/4] Forward notebook

Folder notebooks, with forward sim and sample file
---
 notebooks/Forward_con_mig.ipynb | 349 ++++++++++++++++++++++++++++++++
 notebooks/events.bin            | Bin 0 -> 591572 bytes
 2 files changed, 349 insertions(+)
 create mode 100644 notebooks/Forward_con_mig.ipynb
 create mode 100644 notebooks/events.bin

diff --git a/notebooks/Forward_con_mig.ipynb b/notebooks/Forward_con_mig.ipynb
new file mode 100644
index 000000000..bb3590760
--- /dev/null
+++ b/notebooks/Forward_con_mig.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import struct\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GillespieSelectionSimulation:\n",
+    "    def __init__(self, L, N, s, migration_matrix, tfinal, seed=None, l0=0):\n",
+    "        self.L = L  # Total number of demes\n",
+    "        self.N = N  # Number of individuals in each deme\n",
+    "        self.s = s  # Selection coefficient\n",
+    "        self.migration_matrix = migration_matrix\n",
+    "        self.m = 0 #migration_matrix[0, 1]\n",
+    "        self.tfinal = tfinal\n",
+    "        self.l0 = l0  # Initial location for the mutant\n",
+    "        np.random.seed(seed)  # Setting the random seed\n",
+    "        print('Initialization complete. Seed:', seed)\n",
+    "\n",
+    "    def simulate(self):\n",
+    "        t = 0.0\n",
+    "        mutants = np.zeros(self.L, dtype = np.uint32)\n",
+    "        mutants[self.l0] = 1 #Starting mutation at location l0\n",
+    "        start_state = mutants.copy()\n",
+    "        events = []\n",
+    "        mut_traj = [[t, mutants.copy()]]  # Initialize with the starting state\n",
+    "\n",
+    "        print(\"Starting simulation...\")\n",
+    "        while t < self.tfinal:\n",
+    "\n",
+    "            if np.sum(mutants) == 0:\n",
+    "                print(f\"All mutants extinct at time {t}. Reintroducing...\")\n",
+    "                mutants = np.zeros(self.L, dtype = np.uint32)\n",
+    "                mutants[self.l0] = 1\n",
+    "                t = 0.0\n",
+    "                events = []\n",
+    "                mut_traj = [[t, mutants.copy()]] \n",
+    "                continue\n",
+    "            \n",
+    "                        \n",
+    "            f = mutants / self.N\n",
+    "            rate_mutant_replaces_wt = self.N * f * (1 - f) * (1 + self.s)\n",
+    "            rate_wt_replaces_mutant = self.N * f * (1 - f)\n",
+    "            rate_migration_mutant_to_i_from_j = np.zeros((self.L, self.L))\n",
+    "            rate_migration_wt_to_i_from_j = np.zeros((self.L, self.L))\n",
+    "\n",
+    "            for i in range(self.L):\n",
+    "                for j in range(self.L):\n",
+    "                    if i != j:\n",
+    "                        # Calculate migration rates of mutants and wild types from j to i\n",
+    "                        rate_migration_mutant_to_i_from_j[i][j] = self.N * f[j] * (1 - f[i]) * (1 + self.s) * self.migration_matrix[i][j]\n",
+    "                        rate_migration_wt_to_i_from_j[i][j] = self.N * f[i] * (1 - f[j]) * self.migration_matrix[i][j]\n",
+    "\n",
+    "            total_rate = np.sum(rate_mutant_replaces_wt + rate_wt_replaces_mutant) + np.sum(rate_migration_mutant_to_i_from_j) + np.sum(rate_migration_wt_to_i_from_j)\n",
+    "\n",
+    "            if total_rate == 0:\n",
+    "                print(\"No possible reactions. Skipping...\")\n",
+    "                break\n",
+    "\n",
+    "            tau = np.random.exponential(scale = 1 / total_rate)\n",
+    "            t += tau\n",
+    "\n",
+    "            combined_rates = np.concatenate((rate_mutant_replaces_wt, rate_wt_replaces_mutant, \n",
+    "                                             rate_migration_mutant_to_i_from_j.flatten(), rate_migration_wt_to_i_from_j.flatten()))\n",
+    "            reaction_probabilities = combined_rates / total_rate\n",
+    "            cumulative_prob = np.cumsum(reaction_probabilities)\n",
+    "            r = np.random.rand()\n",
+    "            reaction_index = np.where(cumulative_prob > r)[0][0]\n",
+    "\n",
+    "            if reaction_index < self.L:\n",
+    "                event_type = 0\n",
+    "                updated_deme = reaction_index\n",
+    "                parent_deme = reaction_index\n",
+    "                mutants[updated_deme] += 1\n",
+    "            elif reaction_index < 2 * self.L:\n",
+    "                event_type = 1\n",
+    "                updated_deme = reaction_index - self.L\n",
+    "                parent_deme = updated_deme\n",
+    "                mutants[updated_deme] -= 1\n",
+    "            elif reaction_index < 2 * self.L + self.L**2:\n",
+    "                event_type = 2\n",
+    "                index = reaction_index - 2 * self.L\n",
+    "                i, j = index // self.L, index % self.L\n",
+    "                updated_deme = i\n",
+    "                parent_deme = j\n",
+    "                mutants[i] += 1\n",
+    "            else:\n",
+    "                event_type = 3\n",
+    "                index = reaction_index - (2 * self.L + self.L**2)\n",
+    "                i, j = index // self.L, index % self.L\n",
+    "                updated_deme = i\n",
+    "                parent_deme = j\n",
+    "                mutants[i] -= 1\n",
+    "\n",
+    "            mut_traj.append([t, mutants.copy()])\n",
+    "            events.append([t, event_type, updated_deme, parent_deme])\n",
+    "            # print(f\"Event at time {t}: {event_type} in deme {updated_deme}, from deme {parent_deme}\")\n",
+    "        end_state = mutants.copy()\n",
+    "        demes = len(mutants)\n",
+    "        return events, mut_traj, start_state, end_state\n",
+    "    def save_events(self, events, start_state, end_state, filename):\n",
+    "        with open(filename, 'wb') as f:\n",
+    "            #Using struct.pack is not very efficient, but it seems to work fine for our sizes and I don't really care. It will cause problems for large deme sizes\n",
+    "            # Write the number of events as size_t. Can be L (8 bytes) or I (4 bytes)\n",
+    "            f.write(struct.pack('L', len(events))) # 'L' is long unsigned integer\n",
+    "            f.write(struct.pack('i', self.L))\n",
+    "            f.write(struct.pack('i', self.N)) # 'i' is int\n",
+    "            f.write(struct.pack('d', self.m)) # 'd' is double\n",
+    "            for i in start_state:\n",
+    "                f.write(struct.pack('i', i))\n",
+    "            for i in end_state:\n",
+    "                f.write(struct.pack('i', i))\n",
+    "            for i in events:\n",
+    "                f.write(struct.pack('d', i[0]))\n",
+    "                f.write(struct.pack('i', i[1]))\n",
+    "                f.write(struct.pack('i', i[2]))\n",
+    "                f.write(struct.pack('i', i[3]))\n",
+    "    def load_events(self, filename):\n",
+    "        with open(filename, 'rb') as f:\n",
+    "            output = []\n",
+    "            num_events = struct.unpack('L', f.read(8))\n",
+    "            demes = struct.unpack('i', f.read(4))\n",
+    "            pop_size = struct.unpack('i', f.read(4))\n",
+    "            m = struct.unpack('d', f.read(8))\n",
+    "            output.append(num_events)  # Read and unpack one integers\n",
+    "            output.append(demes)\n",
+    "            output.append(pop_size)\n",
+    "            output.append(m)\n",
+    "            for i in range(demes[0]):\n",
+    "                output.append(struct.unpack('i', f.read(4)))\n",
+    "                output.append(struct.unpack('i', f.read(4)))\n",
+    "            for i in range(num_events[0]):\n",
+    "                output.append(struct.unpack('d', f.read(8)))\n",
+    "                output.append(struct.unpack('i', f.read(4)))\n",
+    "                output.append(struct.unpack('i', f.read(4)))\n",
+    "                output.append(struct.unpack('i', f.read(4)))\n",
+    "            return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_mutant_trajectories(mut_traj, L):\n",
+    "    times = [state[0] for state in mut_traj]\n",
+    "    mutant_counts = [state[1] for state in mut_traj]\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    for deme in range(L):\n",
+    "        plt.plot(times, [m[deme] for m in mutant_counts], label=f'Deme {deme+1}')\n",
+    "    plt.xlabel('Time')\n",
+    "    plt.ylabel('Number of Mutants')\n",
+    "    plt.title('Mutant Trajectories Over Time')\n",
+    "    #plt.legend()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class MigrationMatrixGenerator:\n",
+    "    def __init__(self, deme_dimension, migration_rate):\n",
+    "        self.deme_dimension = deme_dimension\n",
+    "        self.migration_rate = migration_rate\n",
+    "    \n",
+    "\n",
+    "    def generate_1Dmatrix(self):\n",
+    "        M = np.zeros((self.deme_dimension, self.deme_dimension))\n",
+    "\n",
+    "        for i in range(self.deme_dimension):\n",
+    "            if i > 0:\n",
+    "                M[i, i - 1] = self.migration_rate\n",
+    "            if i < self.deme_dimension - 1:\n",
+    "                M[i, i + 1] = self.migration_rate\n",
+    "\n",
+    "        return M\n",
+    "    \n",
+    "    \n",
+    "    def generate_2Dmatrix(self):\n",
+    "        \"\"\"\n",
+    "        Generates a 2D migration matrix for a square grid of demes.\n",
+    "        Each deme can migrate to its immediate neighbors (up, down, left, right).\n",
+    "        Diagonal elements are set to 0, indicating no self-migration.\n",
+    "        \"\"\"\n",
+    "        z = math.sqrt(self.deme_dimension)\n",
+    "        if not z.is_integer():\n",
+    "            raise ValueError(\"Deme dimension must be a perfect square for a 2D grid.\")\n",
+    "        z = int(z)\n",
+    "\n",
+    "        M = np.zeros((self.deme_dimension, self.deme_dimension))\n",
+    "\n",
+    "        for i in range(self.deme_dimension):\n",
+    "            for j in range(self.deme_dimension):\n",
+    "                # Check for right neighbor\n",
+    "                if j == i + 1 and (i + 1) % z != 0:\n",
+    "                    M[i, j] = self.migration_rate\n",
+    "                # Check for down neighbor\n",
+    "                elif j == i + z and j < self.deme_dimension:\n",
+    "                    M[i, j] = self.migration_rate\n",
+    "\n",
+    "                # Ensure migration is bidirectional\n",
+    "                M[j, i] = M[i, j]\n",
+    "\n",
+    "        return M\n",
+    "\n",
+    "    def generate_3Dmatrix(self):\n",
+    "        \"\"\"\n",
+    "        Generates a 3D migration matrix for a cubic grid of demes.\n",
+    "        Each deme can migrate to its immediate neighbors along the x, y, and z axes.\n",
+    "        Diagonal elements are set to 0, indicating no self-migration.\n",
+    "        \"\"\"\n",
+    "        cube_root = round(self.deme_dimension ** (1/3))\n",
+    "        if cube_root ** 3 != self.deme_dimension:\n",
+    "            raise ValueError(\"Deme dimension must be a cube number for a 3D grid.\")\n",
+    "\n",
+    "        M = np.zeros((self.deme_dimension, self.deme_dimension))\n",
+    "\n",
+    "        for i in range(self.deme_dimension):\n",
+    "            x, y, z = np.unravel_index(i, (cube_root, cube_root, cube_root))\n",
+    "\n",
+    "            # Check neighbors in each direction (left, right, up, down, front, back)\n",
+    "            for dx, dy, dz in [(-1, 0, 0), (1, 0, 0), (0, -1, 0), (0, 1, 0), (0, 0, -1), (0, 0, 1)]:\n",
+    "                nx, ny, nz = x + dx, y + dy, z + dz\n",
+    "                if 0 <= nx < cube_root and 0 <= ny < cube_root and 0 <= nz < cube_root:\n",
+    "                    neighbor_index = np.ravel_multi_index((nx, ny, nz), (cube_root, cube_root, cube_root))\n",
+    "                    M[i, neighbor_index] = self.migration_rate\n",
+    "\n",
+    "        return M\n",
+    "\n",
+    "    def generate_island_model_matrix(self):\n",
+    "        \"\"\"\n",
+    "        Generates a migration matrix based on the island model.\n",
+    "        Each deme has an equal migration rate to every other deme.\n",
+    "        Diagonal elements are set to 0, indicating no self-migration.\n",
+    "        \"\"\"\n",
+    "        M = np.full((self.deme_dimension, self.deme_dimension), self.migration_rate)\n",
+    "        np.fill_diagonal(M, 0)  # Set diagonal elements to 0\n",
+    "        return M    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialization complete. Seed: None\n",
+      "Starting simulation...\n",
+      "No possible reactions. Skipping...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(9999,), (1,), (10000,), (0.0,), (1,), (10000,), (4.771818163941268e-05,), (0,), (0,), (0,), (0.0001269114533750033,), (0,), (0,), (0,), (0.00020479840692238415,), (0,), (0,), (0,), (0.0003868512648608837,), (0,), (0,), (0,), (0.00040125281940051754,), (0,), (0,), (0,), (0.0004396525870125214,), (0,), (0,), (0,), (0.0004409992368832654,), (0,), (0,), (0,), (0.0004516841209499256,), (0,), (0,), (0,), (0.00046635198488061293,), (0,), (0,), (0,), (0.0004817560144229769,), (0,), (0,), (0,), (0.0004936453628786726,), (0,), (0,), (0,), (0.000508589160153316,), (0,), (0,), (0,), (0.0005171767755911625,), (0,), (0,), (0,), (0.0005255528771130577,), (0,), (0,), (0,), (0.0005281068653429891,), (0,), (0,), (0,), (0.0005312812683494216,), (0,), (0,), (0,), (0.0005335997649747014,), (0,), (0,), (0,), (0.0005346801579408522,), (0,), (0,), (0,), (0.000537125171186308,), (0,), (0,), (0,), (0.0005380844238624733,), (0,), (0,), (0,), (0.0005404373053466639,), (0,), (0,), (0,), (0.0005405694505743195,), (0,), (0,), (0,), (0.000561812604564046,), (0,), (0,), (0,), (0.0005633734852782369,), (0,)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example usage\n",
+    "L = 1\n",
+    "N = 10000\n",
+    "s = 10000\n",
+    "m = 0\n",
+    "migration_matrix = MigrationMatrixGenerator(L, m).generate_1Dmatrix()\n",
+    "\n",
+    "# migration_matrix = np.zeros((L, L))  # Example migration matrix\n",
+    "\n",
+    "tfinal = 5000\n",
+    "simulation = GillespieSelectionSimulation(L, N, s, migration_matrix, tfinal)\n",
+    "events, mut_traj, start_state, end_state = simulation.simulate()\n",
+    "plot_mutant_trajectories(mut_traj, L)\n",
+    "simulation.save_events(events, start_state, end_state, 'events.bin')\n",
+    "loaded_events = simulation.load_events('events.bin')\n",
+    "\n",
+    "print(loaded_events[0:100])  # Optional: Print to verify contents\n",
+    "\n",
+    "# print(mut_traj)\n",
+    "#for event in events[0:10]:\n",
+    "#     print(event)  # Print each event\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "msprime_stable",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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HcmV?d00001


From a9456913920c15ee524d58c71b93fd1ea8a0923c Mon Sep 17 00:00:00 2001
From: Aalhad-Bhatt <aalhadbhatt@gmail.com>
Date: Wed, 3 Dec 2025 15:42:40 -0500
Subject: [PATCH 3/4] Well-mixed test cases

Hard-coded trajectories to test the well-mixed side of the code + 1 test for a 1D pop.

Also removes the C side file I/O. Breaks the Python side of the function.
---
 lib/msprime.c            | 234 ++++++++--------
 lib/msprime.h            |  20 +-
 lib/tests/test_sweeps.c  | 570 +++++++++++++++++++++++++++++++++++++--
 msprime/_msprimemodule.c |  81 +++---
 4 files changed, 700 insertions(+), 205 deletions(-)

diff --git a/lib/msprime.c b/lib/msprime.c
index b5d6c000b..574b3cb16 100644
--- a/lib/msprime.c
+++ b/lib/msprime.c
@@ -5469,7 +5469,7 @@ msp_dtwf_generation(msp_t *self)
     for (j = 0; j < self->num_populations; j++) {
 
         pop = &self->populations[j];
-        if (avl_count(&pop->ancestors[label]) == 0) {
+        if (avl_count(&pop->ancestors[label]) == 0) { // Change this to loop over both
             continue;
         }
         /* For the DTWF, N for each population is the reference population size
@@ -5477,22 +5477,26 @@ msp_dtwf_generation(msp_t *self)
          * the nearest integer. Thus, the population's size is always relative
          * to the reference model population size (which is also true for the
          * coalescent models. */
-        N = (uint32_t) round(get_population_size(pop, self->time));
+        N = (uint32_t) round(
+            get_population_size(pop, self->time)); // Ancestral bg pop size
         if (N == 0) {
             ret = MSP_ERR_DTWF_ZERO_POPULATION_SIZE;
             goto out;
         }
 
         // Allocate memory for linked list of offspring per parent
-        parents = calloc(N, sizeof(segment_list_t *));
-        segment_mem = malloc(msp_get_num_ancestors(self) * sizeof(segment_list_t));
+        parents = calloc(N, sizeof(segment_list_t *)); // Memory for gametes probably
+        segment_mem = malloc(
+            msp_get_num_ancestors(self)
+            * sizeof(segment_list_t)); // This should be the same as N for all demes
         if (parents == NULL || segment_mem == NULL) {
             ret = MSP_ERR_NO_MEMORY;
             goto out;
         }
         // Iterate through ancestors and draw parents
         segment_mem_offset = 0;
-        for (a = pop->ancestors[label].head; a != NULL; a = a->next) {
+        for (a = pop->ancestors[label].head; a != NULL;
+             a = a->next) { // Iterate through label
             s = segment_mem + segment_mem_offset;
             segment_mem_offset++;
             p = (uint32_t) gsl_rng_uniform_int(self->rng, N);
@@ -5507,7 +5511,8 @@ msp_dtwf_generation(msp_t *self)
 
         // Iterate through offspring of parent k, adding to avl_tree
         for (k = 0; k < N; k++) {
-            for (i = 0; i < 2; i++) {
+            for (i = 0; i < 2;
+                 i++) { // Is this because of diploid ness? Might need to remove it
                 parent_nodes[i] = TSK_NULL;
             }
             for (s = parents[k]; s != NULL; s = s->next) {
@@ -5517,7 +5522,8 @@ msp_dtwf_generation(msp_t *self)
                 // Recombine ancestor
                 // TODO Should this be the recombination rate going foward from x.left?
                 if (rate_map_get_total_mass(&self->recomb_map) > 0) {
-                    ret = msp_dtwf_recombine(self, x, &u[0], &u[1], parent_nodes);
+                    ret = msp_dtwf_recombine(self, x, &u[0], &u[1],
+                        parent_nodes); // Insert probability of being in sample here
                     if (ret != 0) {
                         goto out;
                     }
@@ -5999,82 +6005,6 @@ msp_sweep_reverse_recombination_event(msp_t *self, label_id_t label, double swee
     return ret;
 }
 
-/* Reads the trajectory of a sweep when fed a file.
-The file contains (in order)
-1. Number of steps
-2. Number of demes
-3. Pop size
-4. Migration rate
-5. State after finishing the backward sim
-6. state at the start of the backward sim
-7. Time, type, and start and end locations for each event
-No. 7 is currently read backward in time as well
- */
-static int
-genic_selection_read_trajectory(const char *filename, size_t *num_steps_ret,
-    int *num_demes_ret, int *tot_pop_ret, double *migration_rate_ret,
-    int **final_mut_pop_ret, int **mut_pop_ret, double **allele_frequency_mut_ret,
-    double **t_of_forward_ev_ret, int **ev_type_ret, int **start_deme_ret,
-    int **end_deme_ret)
-{
-    int ret = 0;
-    size_t num_steps;
-    int num_demes;
-    double *allele_frequency_mut = NULL;
-    double *t_of_forward_ev = NULL;
-    size_t j = 0;
-    int i = 0;
-    double migration_rate = 0.0;
-    int tot_pop = 0;
-    int *mut_pop = NULL, *final_mut_pop = NULL;
-    int *ev_type = NULL, *start_deme = NULL, *end_deme = NULL;
-    FILE *file = fopen(filename, "r");
-
-    fread(&num_steps, sizeof(size_t), 1, file);
-    fread(&num_demes, sizeof(int), 1, file);
-    fread(&tot_pop, sizeof(int), 1, file);
-    fread(&migration_rate, sizeof(double), 1, file);
-
-    mut_pop = (int *) malloc(sizeof(*mut_pop) * (size_t) num_demes);
-    allele_frequency_mut
-        = (double *) malloc(sizeof(*allele_frequency_mut) * (size_t) num_demes);
-    final_mut_pop = (int *) malloc(sizeof(*final_mut_pop) * (size_t) num_demes);
-
-    for (i = 0; i < num_demes; i++) {
-        fread(&final_mut_pop[i], sizeof(int), 1, file);
-    }
-
-    for (i = 0; i < num_demes; i++) {
-        fread(&mut_pop[i], sizeof(int), 1, file);
-        allele_frequency_mut[i] = 1.0 * mut_pop[i] / tot_pop;
-    }
-
-    t_of_forward_ev = (double *) malloc(sizeof(*t_of_forward_ev) * num_steps);
-    ev_type = (int *) malloc(sizeof(*ev_type) * num_steps);
-    start_deme = (int *) malloc(sizeof(*start_deme) * num_steps);
-    end_deme = (int *) malloc(sizeof(*end_deme) * num_steps);
-    for (j = 1; j <= num_steps; j++) {
-        fread(&t_of_forward_ev[num_steps - j], sizeof(double), 1, file);
-        fread(&ev_type[num_steps - j], sizeof(int), 1, file);
-        fread(&start_deme[num_steps - j], sizeof(int), 1, file);
-        fread(&end_deme[num_steps - j], sizeof(int), 1, file);
-    }
-
-    fclose(file);
-    *num_demes_ret = num_demes;
-    *num_steps_ret = num_steps;
-    *tot_pop_ret = tot_pop;
-    *migration_rate_ret = migration_rate;
-    *final_mut_pop_ret = final_mut_pop;
-    *mut_pop_ret = mut_pop;
-    *allele_frequency_mut_ret = allele_frequency_mut;
-    *t_of_forward_ev_ret = t_of_forward_ev;
-    *ev_type_ret = ev_type;
-    *start_deme_ret = start_deme;
-    *end_deme_ret = end_deme;
-    return ret;
-}
-
 static int
 sweep_forward_event(msp_t *self, double t_of_next_forward_ev, int *ev_type,
     size_t *curr_step, int *start_deme, int *end_deme, double **ancestral_bg_num_ind,
@@ -6090,30 +6020,30 @@ sweep_forward_event(msp_t *self, double t_of_next_forward_ev, int *ev_type,
 
     if (start_deme_index == end_deme[*curr_step]) {
         if (curr_ev_type == 0) { // mutant replaced with wildtype, mutant coalascence
-            p_forward_ev
-                = (float) ancestral_bg_num_ind[start_deme_index][1]
-                  * (ancestral_bg_num_ind[start_deme_index][1] - 1.0)
-                  / ((*mut_pop[start_deme_index]) * (*mut_pop[start_deme_index] - 1.0));
+            p_forward_ev = (float) ancestral_bg_num_ind[start_deme_index][1]
+                           * (ancestral_bg_num_ind[start_deme_index][1] - 1.0)
+                           / (((*mut_pop)[start_deme_index])
+                               * ((*mut_pop)[start_deme_index] - 1.0));
             tmp_rand = gsl_rng_uniform(self->rng);
             if (tmp_rand <= p_forward_ev) {
                 ret = self->common_ancestor_event(self, start_deme_index, 1);
             }
-            (*mut_pop[start_deme_index])--;
-            *allele_frequency_mut[start_deme_index]
-                = 1.0 * *mut_pop[start_deme_index] / tot_pop;
+            ((*mut_pop)[start_deme_index])--;
+            (*allele_frequency_mut)[start_deme_index]
+                = 1.0 * (*mut_pop)[start_deme_index] / tot_pop;
             (*curr_step)++;
 
         } else if (curr_ev_type == 1) { // wildtype replaced with mutant, wt coalescence
             p_forward_ev = (float) ancestral_bg_num_ind[start_deme_index][0]
                            * (ancestral_bg_num_ind[start_deme_index][0] - 1.0)
-                           / ((tot_pop - *mut_pop[start_deme_index])
-                                 * ((tot_pop - *mut_pop[start_deme_index]) - 1.0));
+                           / ((tot_pop - (*mut_pop)[start_deme_index])
+                               * ((tot_pop - (*mut_pop)[start_deme_index]) - 1.0));
             tmp_rand = gsl_rng_uniform(self->rng);
             if (tmp_rand <= p_forward_ev) {
                 ret = self->common_ancestor_event(self, start_deme_index, 0);
             }
-            (*mut_pop[start_deme_index])++;
-            *allele_frequency_mut[start_deme_index]
+            ((*mut_pop)[start_deme_index])++;
+            (*allele_frequency_mut)[start_deme_index]
                 = 1.0 * *mut_pop[start_deme_index] / tot_pop;
             (*curr_step)++;
         }
@@ -6123,40 +6053,40 @@ sweep_forward_event(msp_t *self, double t_of_next_forward_ev, int *ev_type,
         if (curr_ev_type == 2) { // wildtype replaced by mutant, mutant migration
                                  // + coalascence
             p_forward_ev = 1.0 * ancestral_bg_num_ind[start_deme_index][1]
-                           / *mut_pop[start_deme_index];
+                           / (*mut_pop)[start_deme_index];
             tmp_rand = gsl_rng_uniform(self->rng);
             if (tmp_rand <= p_forward_ev) {
                 ret = msp_migration_event_in_background(
                     self, start_deme_index, end_deme[*curr_step], 1);
                 p_forward_ev = 1.0 * ancestral_bg_num_ind[end_deme[*curr_step]][1]
-                               / *mut_pop[end_deme[*curr_step]];
+                               / (*mut_pop)[end_deme[*curr_step]];
                 tmp_rand = gsl_rng_uniform(self->rng);
                 if (tmp_rand <= p_forward_ev) {
                     ret = self->common_ancestor_event(self, end_deme[*curr_step], 1);
                 }
             }
-            (*mut_pop[start_deme[*curr_step]])--;
-            *allele_frequency_mut[start_deme[*curr_step]]
-                = 1.0 * *mut_pop[start_deme[*curr_step]] / tot_pop;
+            ((*mut_pop)[start_deme[*curr_step]])--;
+            (*allele_frequency_mut)[start_deme[*curr_step]]
+                = 1.0 * (*mut_pop)[start_deme[*curr_step]] / tot_pop;
             (*curr_step)++;
 
         } else if (curr_ev_type == 3) { // mutant replaced by wildtype, wildtype
                                         // migration + coalascence
             p_forward_ev = 1.0 * ancestral_bg_num_ind[start_deme_index][0]
-                           / (tot_pop - *mut_pop[start_deme_index]);
+                           / (tot_pop - (*mut_pop)[start_deme_index]);
             tmp_rand = gsl_rng_uniform(self->rng);
             if (tmp_rand <= p_forward_ev) {
                 ret = msp_migration_event_in_background(
                     self, start_deme_index, end_deme[*curr_step], 0);
                 p_forward_ev = 1.0 * ancestral_bg_num_ind[end_deme[*curr_step]][0]
-                               / (tot_pop - *mut_pop[end_deme[*curr_step]]);
+                               / (tot_pop - (*mut_pop)[end_deme[*curr_step]]);
                 tmp_rand = gsl_rng_uniform(self->rng);
                 if (tmp_rand <= p_forward_ev) {
                     ret = self->common_ancestor_event(self, end_deme[*curr_step], 0);
                 }
             }
-            (*mut_pop[start_deme[*curr_step]])++;
-            *allele_frequency_mut[start_deme[*curr_step]]
+            ((*mut_pop)[start_deme[*curr_step]])++;
+            (*allele_frequency_mut)[start_deme[*curr_step]]
                 = 1.0 * *mut_pop[start_deme[*curr_step]] / tot_pop;
             (*curr_step)++;
         }
@@ -6354,11 +6284,13 @@ msp_run_sweep_reverse(msp_t *self)
     int ret = 0;
     simulation_model_t *model = &self->model;
     size_t curr_step = 0;
-    size_t num_steps = 0;
-    double *allele_frequency_mut = NULL, *t_of_forward_ev = NULL;
-    int *ev_type = NULL, *start_deme = NULL, *end_deme = NULL;
+    size_t num_steps = model->params.sweep_reverse.num_steps;
+    double *allele_frequency_mut = NULL;
+    double *t_of_forward_ev = model->params.sweep_reverse.t_of_forward_ev;
+    int *ev_type = model->params.sweep_reverse.ev_type;
+    tsk_id_t *start_deme = model->params.sweep_reverse.start_deme;
+    tsk_id_t *end_deme = model->params.sweep_reverse.end_deme;
     double sweep_locus = model->params.sweep_reverse.position;
-    const char *filename = model->params.sweep_reverse.filename;
     size_t j = 0;
     int i = 0;
     double recomb_mass = 0.0;
@@ -6368,12 +6300,14 @@ msp_run_sweep_reverse(msp_t *self)
     double *p_coal_wild = NULL, *p_coal_mut = NULL, *p_mig_wild_right = NULL,
            *p_mig_mut_right = NULL, *p_mig_wild_left = NULL, *p_mig_mut_left = NULL;
     double p_rec_wild = 0.0, p_rec_mut = 0.0;
-    double tmp_rand = 0.0, p_any_ev = 0.0, p_cum_ev = 0.0, t_of_next_forward_ev = 0.0;
+    double tmp_rand = 0.0, p_any_ev = 0.0, p_cum_ev = 0.0, p_coal_after_mig = 0.0,
+           t_of_next_forward_ev = 0.0;
     double t_start = 0.0, t_end = 0.0, t_of_next_ev = 0.0, t_current = 0.0;
-    int num_demes = 0;
-    double migration_rate = 0.0;
-    int tot_pop = 0;
-    int *mut_pop = NULL, *final_mut_pop = NULL;
+    int num_demes = model->params.sweep_reverse.num_demes;
+    double migration_rate = model->params.sweep_reverse.migration_rate;
+    int tot_pop = model->params.sweep_reverse.tot_pop;
+    tsk_id_t *mut_pop = model->params.sweep_reverse.mut_pop;
+    // tsk_id_t *final_mut_pop = model->params.sweep_reverse.mut_pop_final;
     bool no_event_yet = true;
 
     if (rate_map_get_total_mass(&self->gc_map) != 0.0) { // arrow?+
@@ -6382,13 +6316,10 @@ msp_run_sweep_reverse(msp_t *self)
         goto out;
     }
 
-    ret = genic_selection_read_trajectory(filename, &num_steps, &num_demes, &tot_pop,
-        &migration_rate, &final_mut_pop, &mut_pop, &allele_frequency_mut,
-        &t_of_forward_ev, &ev_type, &start_deme, &end_deme);
-
     tsk_bug_assert(num_demes > 0);
     tsk_bug_assert(tot_pop > 0);
 
+    allele_frequency_mut = (double *) malloc(sizeof(double) * (size_t) num_demes);
     p_coal_wild = (double *) malloc(sizeof(double) * (size_t) num_demes);
     p_coal_mut = (double *) malloc(sizeof(double) * (size_t) num_demes);
     if (num_demes > 1) {
@@ -6405,6 +6336,10 @@ msp_run_sweep_reverse(msp_t *self)
 
     curr_step = 0;
 
+    for (i = 0; i < num_demes; i++) {
+        allele_frequency_mut[i] = (double) mut_pop[i] / tot_pop;
+    }
+
     ret = msp_sweep_reverse_initialise(self, allele_frequency_mut);
     if (ret != 0) {
         goto out;
@@ -6432,12 +6367,18 @@ msp_run_sweep_reverse(msp_t *self)
                               : fenwick_get_total(&self->recomb_mass_index[label]);
             for (i = 0; i < num_demes; i++) {
                 ancestral_bg_num_ind[i][j]
-                    = avl_count(&self->populations[i].ancestors[label]);
+                    = (double) avl_count(&self->populations[i].ancestors[label]);
                 /* We can get small negative rates by numerical jitter which causes
                  * problems in later calculations and leads to assertion trips.
                  * See https://github.com/tskit-dev/msprime/issues/1966
                  */
+                if (j == 1) {
+                    tsk_bug_assert(ancestral_bg_num_ind[i][j] <= mut_pop[i]);
+                } else {
+                    tsk_bug_assert(ancestral_bg_num_ind[i][j] <= (tot_pop - mut_pop[i]));
+                }
             }
+
             rec_rates[j] = TSK_MAX(0, (recomb_mass));
         }
 
@@ -6541,6 +6482,12 @@ msp_run_sweep_reverse(msp_t *self)
                         p_cum_ev += p_mig_wild_left[i];
                         if (tmp_rand < p_cum_ev / p_any_ev) {
                             ret = msp_migration_event_in_background(self, i + 1, i, 0);
+                            p_coal_after_mig = 1.0 * ancestral_bg_num_ind[i][0]
+                                               / (tot_pop - mut_pop[i]);
+                            tmp_rand = gsl_rng_uniform(self->rng);
+                            if (tmp_rand <= p_coal_after_mig) {
+                                ret = self->common_ancestor_event(self, i, 0);
+                            }
                             no_event_yet = false;
                             break;
                         } else {
@@ -6548,6 +6495,12 @@ msp_run_sweep_reverse(msp_t *self)
                             if (tmp_rand < p_cum_ev / p_any_ev) {
                                 ret = msp_migration_event_in_background(
                                     self, i + 1, i, 1);
+                                p_coal_after_mig
+                                    = 1.0 * ancestral_bg_num_ind[i][1] / mut_pop[i];
+                                tmp_rand = gsl_rng_uniform(self->rng);
+                                if (tmp_rand <= p_coal_after_mig) {
+                                    ret = self->common_ancestor_event(self, i, 1);
+                                }
                                 no_event_yet = false;
                                 break;
                             } else {
@@ -6555,6 +6508,14 @@ msp_run_sweep_reverse(msp_t *self)
                                 if (tmp_rand < p_cum_ev / p_any_ev) {
                                     ret = msp_migration_event_in_background(
                                         self, i, i + 1, 0);
+                                    p_coal_after_mig = 1.0
+                                                       * ancestral_bg_num_ind[i + 1][0]
+                                                       / (tot_pop - mut_pop[i + 1]);
+                                    tmp_rand = gsl_rng_uniform(self->rng);
+                                    if (tmp_rand <= p_coal_after_mig) {
+                                        ret = self->common_ancestor_event(
+                                            self, i + 1, 0);
+                                    }
                                     no_event_yet = false;
                                     break;
                                 } else {
@@ -6562,6 +6523,14 @@ msp_run_sweep_reverse(msp_t *self)
                                     if (tmp_rand < p_cum_ev / p_any_ev) {
                                         ret = msp_migration_event_in_background(
                                             self, i, i + 1, 1);
+                                        p_coal_after_mig
+                                            = 1.0 * ancestral_bg_num_ind[i + 1][1]
+                                              / mut_pop[i + 1];
+                                        tmp_rand = gsl_rng_uniform(self->rng);
+                                        if (tmp_rand <= p_coal_after_mig) {
+                                            ret = self->common_ancestor_event(
+                                                self, i + 1, 1);
+                                        }
                                         no_event_yet = false;
                                         break;
                                     }
@@ -6634,13 +6603,7 @@ msp_run_sweep_reverse(msp_t *self)
         msp_safe_free(ancestral_bg_num_ind[i]);
     }
     msp_safe_free(ancestral_bg_num_ind);
-    msp_safe_free(mut_pop);
     msp_safe_free(allele_frequency_mut);
-    msp_safe_free(final_mut_pop);
-    msp_safe_free(t_of_forward_ev);
-    msp_safe_free(ev_type);
-    msp_safe_free(start_deme);
-    msp_safe_free(end_deme);
     return ret;
 }
 
@@ -8705,7 +8668,7 @@ genic_selection_generate_trajectory(sweep_t *self, msp_t *simulator,
         alpha = 2 * pop_size * trajectory.s;
         x = 1.0
             - genic_selection_stochastic_forwards(
-                  trajectory.dt, 1.0 - x, alpha, gsl_rng_uniform(rng));
+                trajectory.dt, 1.0 - x, alpha, gsl_rng_uniform(rng));
         /* need our recored traj to stay in bounds */
         t += trajectory.dt;
         sim_time += trajectory.dt * pop_size * simulator->ploidy;
@@ -9042,8 +9005,10 @@ msp_set_simulation_model_sweep_genic_selection(msp_t *self, double position,
 }
 
 int
-msp_set_simulation_model_sweep_genic_selection_reverse(
-    msp_t *self, double position, const char *filename)
+msp_set_simulation_model_sweep_genic_selection_reverse(msp_t *self, double position,
+    size_t num_steps, int num_demes, int tot_pop, double migration_rate,
+    tsk_id_t *mut_pop, tsk_id_t *mut_pop_final, double *t_of_forward_ev,
+    tsk_id_t *ev_type, tsk_id_t *start_deme, tsk_id_t *end_deme)
 {
     int ret = 0;
     simulation_model_t *model = &self->model;
@@ -9055,13 +9020,28 @@ msp_set_simulation_model_sweep_genic_selection_reverse(
         goto out;
     }
 
+    if (num_demes == 1) {
+        tsk_bug_assert(migration_rate == 0);
+    } else {
+        tsk_bug_assert(migration_rate > 0);
+    }
+
     ret = msp_set_simulation_model(self, MSP_MODEL_SWEEP_REVERSE);
     if (ret != 0) {
         goto out;
     }
 
     model->params.sweep_reverse.position = position;
-    model->params.sweep_reverse.filename = filename;
+    model->params.sweep_reverse.num_steps = num_steps;
+    model->params.sweep_reverse.num_demes = num_demes;
+    model->params.sweep_reverse.tot_pop = tot_pop;
+    model->params.sweep_reverse.migration_rate = migration_rate;
+    model->params.sweep_reverse.mut_pop = mut_pop;
+    model->params.sweep_reverse.mut_pop_final = mut_pop_final;
+    model->params.sweep_reverse.t_of_forward_ev = t_of_forward_ev;
+    model->params.sweep_reverse.ev_type = ev_type;
+    model->params.sweep_reverse.start_deme = start_deme;
+    model->params.sweep_reverse.end_deme = end_deme;
 
 out:
     return ret;
diff --git a/lib/msprime.h b/lib/msprime.h
index e84799371..189f123a1 100644
--- a/lib/msprime.h
+++ b/lib/msprime.h
@@ -234,7 +234,16 @@ typedef struct _sweep_t {
 
 typedef struct _sweep_reverse_t {
     double position;
-    const char *filename;
+    size_t num_steps;
+    int num_demes;
+    int tot_pop;
+    double migration_rate;
+    tsk_id_t *mut_pop;
+    tsk_id_t *mut_pop_final;
+    double *t_of_forward_ev;
+    tsk_id_t *ev_type;
+    tsk_id_t *start_deme;
+    tsk_id_t *end_deme;
 } sweep_reverse_t;
 
 typedef struct _simulation_model_t {
@@ -359,7 +368,7 @@ typedef struct {
 /* Arbitrary limit, saves us having to put in complex malloc/free
  * logic in the demographic_events. Can easily be changed if
  * needs be. */
-#define MSP_MAX_EVENT_POPULATIONS 100
+#define MSP_MAX_EVENT_POPULATIONS 500000
 
 typedef struct {
     population_id_t derived[MSP_MAX_EVENT_POPULATIONS];
@@ -498,9 +507,10 @@ int msp_set_simulation_model_dirac(msp_t *self, double psi, double c);
 int msp_set_simulation_model_beta(msp_t *self, double alpha, double truncation_point);
 int msp_set_simulation_model_sweep_genic_selection(msp_t *self, double position,
     double start_frequency, double end_frequency, double s, double dt);
-int msp_set_simulation_model_sweep_genic_selection_reverse(
-    msp_t *self, double position, const char *filename);
-
+int msp_set_simulation_model_sweep_genic_selection_reverse(msp_t *self, double position,
+    size_t num_steps, int num_demes, int tot_pop, double migration_rate,
+    tsk_id_t *mut_pop, tsk_id_t *mut_pop_final, double *t_of_forward_ev,
+    tsk_id_t *ev_type, tsk_id_t *start_deme, tsk_id_t *end_deme);
 int msp_set_start_time(msp_t *self, double start_time);
 int msp_set_store_migrations(msp_t *self, bool store_migrations);
 int msp_set_store_full_arg(msp_t *self, bool store_full_arg);
diff --git a/lib/tests/test_sweeps.c b/lib/tests/test_sweeps.c
index b070bfc3c..2b3d8b9f2 100644
--- a/lib/tests/test_sweeps.c
+++ b/lib/tests/test_sweeps.c
@@ -329,8 +329,8 @@ sweep_genic_selection_mimic_msms_single_run(unsigned long int seed)
     double position = num_loci / 2;
     double s = 10000;
     double recom_rate = 0.0004;
-    double start_frequency = 0.5 / 10000;
-    double end_frequency = 1 - 0.5 / 10000;
+    double start_frequency = 0.5 / 1000;
+    double end_frequency = 1 - 0.5 / 1000;
     double dt = 1.0 / 400000;
     msp_t msp;
     gsl_rng *rng = safe_rng_alloc();
@@ -371,28 +371,146 @@ sweep_genic_selection_mimic_msms_single_run(unsigned long int seed)
 }
 
 static void
-sweep_genic_selection_reverse_single_run(unsigned long int seed)
+test_sweep_genic_selection_mimic_msms(void)
 {
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 1; i++) {
+        sweep_genic_selection_mimic_msms_single_run(i + 1);
+    }
+}
 
-    /* Try to mimic the msms parameters used in verification.py
-           "100 300 -t 200 -r 200 500000"
-           " -SF 0 0.9 -Sp 0.5 -SaA 5000 -SAA 10000 -N 10000"
-     */
+static void
+verify_sweep_reverse_wm_no_recomb(unsigned long int seed)
+{
+    int ret;
+    uint32_t n = 10;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+    int num_demes = 1;
+    tsk_id_t mut_pop[] = { 1001 };
+    int num_events = mut_pop[0] - 1;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+
+    tsk_id_t final_mut_pop[] = { 1 };
+
+    gsl_rng_set(rng, seed);
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = 1000 - p;
+    ev_type[0] = 0;
+    start_deme[0] = 0;
+    end_deme[0] = 0;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p;
+        ev_type[i] = 0;
+        start_deme[i] = 0;
+        end_deme[i] = 0;
+    }
+
+    ret = build_sim(&msp, &tables, rng, 1.0, num_demes, NULL, n);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+    ret = msp_set_num_labels(&msp, 2);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, 0.5, num_events,
+        num_demes, num_events + 1, 0.0, mut_pop, final_mut_pop, time_of_ev, ev_type,
+        start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
+    msp_free(&msp);
+    tsk_table_collection_free(&tables);
+    gsl_rng_free(rng);
+}
+
+static void
+test_sweep_reverse_wm_no_recomb(void)
+{
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 300; i++) {
+        verify_sweep_reverse_wm_no_recomb(i + 256329651209);
+    }
+}
+
+static void
+verify_sweep_reverse_wm(unsigned long int seed)
+{
     int ret;
-    const char *filename
-        = "/home/aalhadbhatt/Desktop/msprime/notebooks/PyNBmsprime/events.bin";
     uint32_t n = 10;
-    double num_loci = 500001;
-    double position = num_loci / 2;
-    size_t num_demes = 1;
-    double recom_rate = 0.0004;
     msp_t msp;
     gsl_rng *rng = safe_rng_alloc();
     tsk_table_collection_t tables;
+    tsk_id_t mut_pop[] = { 10001 };
+    int num_events = mut_pop[0] - 1;
+    int num_demes = 1;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+    double num_loci = 5001;
+    double position = num_loci / 2;
+    double recom_rate = 0.00004;
+    tsk_id_t final_mut_pop[] = { 1 };
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
 
     // Test over differnt seeds
     gsl_rng_set(rng, seed);
 
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = num_events - p;
+    ev_type[0] = 0;
+    start_deme[0] = 0;
+    end_deme[0] = 0;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p;
+        ev_type[i] = 0;
+        start_deme[i] = 0;
+        end_deme[i] = 0;
+    }
+
     ret = build_sim(&msp, &tables, rng, num_loci, num_demes, NULL, n);
     CU_ASSERT_EQUAL(ret, 0);
     CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
@@ -407,39 +525,431 @@ sweep_genic_selection_reverse_single_run(unsigned long int seed)
     msp_set_node_mapping_block_size(&msp, 65536);
     msp_set_segment_block_size(&msp, 65536);
 
-    ret = msp_set_simulation_model_sweep_genic_selection_reverse(
-        &msp, position, filename);
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, position,
+        num_events, num_demes, num_events + 1, 0.0, mut_pop, final_mut_pop, time_of_ev,
+        ev_type, start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
+    msp_free(&msp);
+    tsk_table_collection_free(&tables);
+    gsl_rng_free(rng);
+}
+
+static void
+test_sweep_reverse_wm(void)
+{
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 300; i++) {
+        verify_sweep_reverse_wm(i + 6584367292);
+    }
+}
+
+static void
+verify_sweep_reverse_wm_back_and_forth(unsigned long int seed)
+{
+    int ret;
+    uint32_t n = 10;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+    tsk_id_t mut_pop[] = { 101 };
+    int num_events = 3 * mut_pop[0] - 5;
+    int num_demes = 1;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+    double num_loci = 5001;
+    double position = num_loci / 2;
+    double recom_rate = 0.000004;
+    tsk_id_t final_mut_pop[] = { 1 };
+
+    gsl_rng_set(rng, seed);
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = num_events - p;
+    ev_type[0] = 0;
+    start_deme[0] = 0;
+    end_deme[0] = 0;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p;
+        if (i % 3 == 2) {
+            ev_type[i] = 1;
+        } else {
+            ev_type[i] = 0;
+        }
+        start_deme[i] = 0;
+        end_deme[i] = 0;
+    }
+
+    ret = build_sim(&msp, &tables, rng, num_loci, num_demes, NULL, n);
+    CU_ASSERT_EQUAL(ret, 0);
+    CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
+    ret = msp_set_num_labels(&msp, 2);
     CU_ASSERT_EQUAL(ret, 0);
 
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, position,
+        num_events, num_demes, mut_pop[0], 0.0, mut_pop, final_mut_pop, time_of_ev,
+        ev_type, start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
     ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
+    msp_free(&msp);
+    tsk_table_collection_free(&tables);
+    gsl_rng_free(rng);
+}
+
+static void
+test_sweep_reverse_wm_back_and_forth(void)
+{
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 300; i++) {
+        verify_sweep_reverse_wm_back_and_forth(i + 462523296209);
+    }
+}
+
+static void
+verify_sweep_reverse_wm_oscillating(unsigned long int seed)
+{
+    int ret;
+    uint32_t n = 10;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+    tsk_id_t mut_pop[] = { 1001 };
+    int num_events = 3 * mut_pop[0] - 9;
+    int num_demes = 1;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+    double num_loci = 5001;
+    double position = num_loci / 2;
+    double recom_rate = 0.00004;
+    tsk_id_t final_mut_pop[] = { 1 };
+
+    gsl_rng_set(rng, seed);
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = num_events - p;
+    ev_type[0] = 0;
+    start_deme[0] = 0;
+    end_deme[0] = 0;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p;
+        if ((i < 2 * mut_pop[0] - 6) && (i > mut_pop[0] - 3)) {
+            ev_type[i] = 1;
+        } else {
+            ev_type[i] = 0;
+        }
+        start_deme[i] = 0;
+        end_deme[i] = 0;
+    }
+
+    ret = build_sim(&msp, &tables, rng, num_loci, num_demes, NULL, n);
+    CU_ASSERT_EQUAL(ret, 0);
+    CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
+    ret = msp_set_num_labels(&msp, 2);
     CU_ASSERT_EQUAL(ret, 0);
 
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, position,
+        num_events, num_demes, mut_pop[0], 0.0, mut_pop, final_mut_pop, time_of_ev,
+        ev_type, start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
     ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
     CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
 
     msp_verify(&msp, 0);
 
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
     msp_free(&msp);
+    tsk_table_collection_free(&tables);
     gsl_rng_free(rng);
-    // free(samples);
+}
+
+static void
+test_sweep_reverse_wm_oscillating(void)
+{
+    /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
+    for (int i = 0; i < 300; i++) {
+        verify_sweep_reverse_wm_oscillating(i + 462941996216);
+    }
+}
+
+static void
+verify_sweep_reverse_wm_sudden_drop(unsigned long int seed)
+{
+    int ret;
+    uint32_t n = 10;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+    tsk_id_t mut_pop[] = { 10001 };
+    int num_events = mut_pop[0] - 1;
+    int num_demes = 1;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+    double num_loci = 5001;
+    double position = num_loci / 2;
+    double recom_rate = 0.0004;
+    tsk_id_t final_mut_pop[] = { 1 };
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+
+    // Test over differnt seeds
+    gsl_rng_set(rng, seed);
+
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = num_events - p / 10000;
+    ev_type[0] = 0;
+    start_deme[0] = 0;
+    end_deme[0] = 0;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p / 10000;
+        ev_type[i] = 0;
+        start_deme[i] = 0;
+        end_deme[i] = 0;
+    }
+
+    ret = build_sim(&msp, &tables, rng, num_loci, num_demes, NULL, n);
+    CU_ASSERT_EQUAL(ret, 0);
+    CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
+    ret = msp_set_num_labels(&msp, 2);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    // To mimic the verfication.py call
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, position,
+        num_events, num_demes, num_events + 1, 0.0, mut_pop, final_mut_pop, time_of_ev,
+        ev_type, start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
+    msp_free(&msp);
     tsk_table_collection_free(&tables);
+    gsl_rng_free(rng);
 }
 
 static void
-test_sweep_genic_selection_mimic_msms(void)
+test_sweep_reverse_wm_sudden_drop(void)
 {
     /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
     for (int i = 0; i < 300; i++) {
-        sweep_genic_selection_mimic_msms_single_run(i + 1);
+        verify_sweep_reverse_wm_sudden_drop(i + 6584367292);
     }
 }
 
 static void
-test_sweep_genic_selection_reverse(void)
+verify_sweep_reverse_1D_deme_wise(unsigned long int seed)
+{
+    int ret;
+    uint32_t n = 10;
+    msp_t msp;
+    gsl_rng *rng = safe_rng_alloc();
+    tsk_table_collection_t tables;
+    tsk_id_t *mut_pop;
+    tsk_id_t tot_pop = 100;
+    int num_demes = 10;
+    sample_t *samples = malloc(n * sizeof(sample_t));
+    int num_events = num_demes * tot_pop - 1;
+    double migration_rate = 0.25;
+    size_t deme_index;
+    double *time_of_ev;
+    int *ev_type;
+    int *start_deme;
+    int *end_deme;
+    int r, i;
+    double p;
+    double num_loci = 5001;
+    double position = num_loci / 2;
+    double recom_rate = 0.00004;
+    tsk_id_t *final_mut_pop;
+
+    time_of_ev = (double *) malloc(sizeof(double) * num_events);
+    ev_type = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    end_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    start_deme = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_events);
+    mut_pop = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_demes);
+    final_mut_pop = (tsk_id_t *) malloc(sizeof(tsk_id_t) * num_demes);
+
+    for (i = 0; i < n; i++) {
+        samples[i].time = 0;
+        samples[i].population = i % num_demes;
+    }
+
+    for (i = 0; i < num_demes; i++) {
+        mut_pop[i] = tot_pop;
+        final_mut_pop[i] = 0;
+    }
+    final_mut_pop[0] = 1;
+
+    // Test over differnt seeds
+    gsl_rng_set(rng, seed);
+
+    r = rand() % RAND_MAX;
+    p = (double) r / RAND_MAX;
+    time_of_ev[0] = num_events - p;
+    ev_type[0] = 0;
+    deme_index = num_demes - 1;
+    start_deme[0] = deme_index;
+    end_deme[0] = deme_index;
+
+    for (i = 1; i < num_events; i++) {
+        r = rand() % RAND_MAX;
+        p = (double) r / RAND_MAX;
+        time_of_ev[i] = time_of_ev[i - 1] - p;
+        if (i % tot_pop == (tot_pop - 1)) {
+            ev_type[i] = 2;
+            end_deme[i] = deme_index - 1;
+            start_deme[i] = deme_index;
+            deme_index -= 1;
+        } else {
+            ev_type[i] = 0;
+            start_deme[i] = deme_index;
+            end_deme[i] = deme_index;
+        }
+    }
+
+    ret = build_sim(&msp, &tables, rng, num_loci, num_demes, samples, n);
+    CU_ASSERT_EQUAL(ret, 0);
+    CU_ASSERT_EQUAL_FATAL(msp_set_recombination_rate(&msp, recom_rate), 0);
+    ret = msp_set_num_labels(&msp, 2);
+    CU_ASSERT_EQUAL(ret, 0);
+
+    // To mimic the verfication.py call
+    msp_set_discrete_genome(&msp, 0);
+    msp_set_gene_conversion_rate(&msp, 0);
+    msp_set_gene_conversion_tract_length(&msp, 1);
+    msp_set_avl_node_block_size(&msp, 65536);
+    msp_set_node_mapping_block_size(&msp, 65536);
+    msp_set_segment_block_size(&msp, 65536);
+
+    ret = msp_set_simulation_model_sweep_genic_selection_reverse(&msp, position,
+        num_events, num_demes, num_events + 1, migration_rate, mut_pop, final_mut_pop,
+        time_of_ev, ev_type, start_deme, end_deme);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_initialise(&msp);
+    CU_ASSERT_EQUAL_FATAL(ret, 0);
+
+    ret = msp_run(&msp, DBL_MAX, UINT32_MAX);
+    CU_ASSERT_EQUAL(ret, MSP_EXIT_MODEL_COMPLETE);
+
+    msp_verify(&msp, 0);
+
+    free(time_of_ev);
+    free(ev_type);
+    free(start_deme);
+    free(end_deme);
+    free(mut_pop);
+    free(final_mut_pop);
+    free(samples);
+    msp_free(&msp);
+    tsk_table_collection_free(&tables);
+    gsl_rng_free(rng);
+}
+
+static void
+test_sweep_reverse_1D_deme_wise(void)
 {
     /* To mimic the nrepeats = 300  parameter in msms cmdline arguments*/
-    for (int i = 0; i < 300; i++)
-        sweep_genic_selection_reverse_single_run(i + 1);
+    for (int i = 0; i < 300; i++) {
+        verify_sweep_reverse_1D_deme_wise(i + 6584367292);
+    }
 }
 
 int
@@ -448,17 +958,27 @@ main(int argc, char **argv)
     CU_TestInfo tests[] = {
         { "test_genic_selection_trajectory", test_genic_selection_trajectory },
         { "test_sweep_genic_selection_bad_parameters",
-            test_sweep_genic_selection_bad_parameters },
+         test_sweep_genic_selection_bad_parameters },
         { "test_sweep_genic_selection_events", test_sweep_genic_selection_events },
         { "test_sweep_genic_selection_single_locus",
-            test_sweep_genic_selection_single_locus },
+         test_sweep_genic_selection_single_locus },
         { "test_sweep_genic_selection_recomb", test_sweep_genic_selection_recomb },
         { "test_sweep_genic_selection_gc", test_sweep_genic_selection_gc },
         { "test_sweep_genic_selection_time_change",
-            test_sweep_genic_selection_time_change },
+         test_sweep_genic_selection_time_change },
         { "test_sweep_genic_selection_mimic_msms",
             test_sweep_genic_selection_mimic_msms },
-        { "test_sweep_genic_selection_reverse", test_sweep_genic_selection_reverse },
+        { "test_sweep_reverse_wm_no_recomb",
+            test_sweep_reverse_wm_no_recomb },
+        { "test_sweep_reverse_wm",
+            test_sweep_reverse_wm },
+        { "test_sweep_reverse_wm_back_and_forth",
+            test_sweep_reverse_wm_back_and_forth },
+        { "test_sweep_reverse_wm_oscillating",
+            test_sweep_reverse_wm_oscillating },
+        { "test_sweep_reverse_wm_sudden_drop",
+            test_sweep_reverse_wm_sudden_drop },
+        { "test_sweep_reverse_1D_deme_wise", test_sweep_reverse_1D_deme_wise },
         CU_TEST_INFO_NULL,
     };
 
diff --git a/msprime/_msprimemodule.c b/msprime/_msprimemodule.c
index a3c794e05..f894075a3 100644
--- a/msprime/_msprimemodule.c
+++ b/msprime/_msprimemodule.c
@@ -151,20 +151,6 @@ get_dict_number(PyObject *dict, const char *key_str)
     return ret;
 }
 
-static PyObject *
-get_dict_string(PyObject *dict, const char *key_str)
-{
-    PyObject *ret = NULL;
-    PyObject *value;
-    value = get_required_dict_value(dict, key_str);
-    if (value == NULL) {
-        goto out;
-    }
-    ret = value;
-out:
-    return ret;
-}
-
 static int
 parse_rate_map(PyObject *py_rate_map, size_t *ret_size,
         PyArrayObject **ret_position, PyArrayObject **ret_rate)
@@ -1077,34 +1063,33 @@ Simulator_parse_sweep_genic_selection_model(Simulator *self, PyObject *py_model)
     return ret;
 }
 
-static int
-Simulator_parse_sweep_genic_selection_reverse_model(Simulator *self, PyObject *py_model)
-{
-    int ret = -1;
-    int err;
-    double position;
-    const char *filename;
-    PyObject *value;
-    value = get_dict_number(py_model, "position");
-    if (value == NULL) {
-        goto out;
-    }
-    position = PyFloat_AsDouble(value);
-    value = get_dict_string(py_model, "filename");
-    if (value == NULL) {
-        goto out;
-    }
-    filename = PyUnicode_AsUTF8(value);
-    err = msp_set_simulation_model_sweep_genic_selection_reverse(self->sim,
-            position, filename);
-    if (err != 0) {
-        handle_input_error("sweep genic selection reverse", err);
-        goto out;
-    }
-    ret = 0;
-out:
-    return ret;
-}
+//static int
+//Simulator_parse_sweep_genic_selection_reverse_model(Simulator *self, PyObject *py_model)
+//{
+//    int ret = -1;
+//    int err;
+//    double position;
+//    const char *filename;
+//    PyObject *value;
+//    value = get_dict_number(py_model, "position");
+//    if (value == NULL) {
+//        goto out;
+//    }
+//    position = PyFloat_AsDouble(value);
+//    value = get_dict_string(py_model, "filename");
+//    if (value == NULL) {
+//        goto out;
+//    }
+//    filename = PyUnicode_AsUTF8(value);
+//    err = msp_set_simulation_model_sweep_genic_selection_reverse(self->sim, double position, size_t num_steps, int num_demes, int tot_pop, double migration_rate, tsk_id_t *mut_pop, tsk_id_t *mut_pop_final, double *t_of_forward_ev, tsk_id_t *ev_type, tsk_id_t *start_deme, tsk_id_t *end_deme);
+//    if (err != 0) {
+//        handle_input_error("sweep genic selection reverse", err);
+//        goto out;
+//    }
+//    ret = 0;
+//out:
+//    return ret;
+//}
 
 static int
 Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
@@ -1296,12 +1281,12 @@ Simulator_parse_simulation_model(Simulator *self, PyObject *py_model)
     if (is_sweep_genic_selection_reverse == -1) {
         goto out;
     }
-    if (is_sweep_genic_selection_reverse) {
-        ret = Simulator_parse_sweep_genic_selection_reverse_model(self, py_model);
-        if (ret != 0) {
-            goto out;
-        }
-    }
+    //if (is_sweep_genic_selection_reverse) {
+    //    ret = Simulator_parse_sweep_genic_selection_reverse_model(self, py_model);
+    //    if (ret != 0) {
+    //        goto out;
+    //    }
+    //}
 
     if (! (is_hudson || is_dtwf || is_smc || is_smc_prime || is_smc_k || is_dirac
                 || is_beta || is_sweep_genic_selection || is_sweep_genic_selection_reverse || is_fixed_pedigree)) {

From 690c7cbb8ebc92cd3f4ac72844bb151a46c65462 Mon Sep 17 00:00:00 2001
From: Aalhad-Bhatt <aalhadbhatt@gmail.com>
Date: Wed, 3 Dec 2025 16:03:50 -0500
Subject: [PATCH 4/4] Fixed test params

Fixed the parameters for the tests to be consistent with each other.
---
 lib/tests/test_sweeps.c | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/lib/tests/test_sweeps.c b/lib/tests/test_sweeps.c
index 2b3d8b9f2..ab0b30172 100644
--- a/lib/tests/test_sweeps.c
+++ b/lib/tests/test_sweeps.c
@@ -388,7 +388,7 @@ verify_sweep_reverse_wm_no_recomb(unsigned long int seed)
     gsl_rng *rng = safe_rng_alloc();
     tsk_table_collection_t tables;
     int num_demes = 1;
-    tsk_id_t mut_pop[] = { 1001 };
+    tsk_id_t mut_pop[] = { 10001 };
     int num_events = mut_pop[0] - 1;
     double *time_of_ev;
     int *ev_type;
@@ -564,7 +564,7 @@ verify_sweep_reverse_wm_back_and_forth(unsigned long int seed)
     msp_t msp;
     gsl_rng *rng = safe_rng_alloc();
     tsk_table_collection_t tables;
-    tsk_id_t mut_pop[] = { 101 };
+    tsk_id_t mut_pop[] = { 1001 };
     int num_events = 3 * mut_pop[0] - 5;
     int num_demes = 1;
     double *time_of_ev;
@@ -575,7 +575,7 @@ verify_sweep_reverse_wm_back_and_forth(unsigned long int seed)
     double p;
     double num_loci = 5001;
     double position = num_loci / 2;
-    double recom_rate = 0.000004;
+    double recom_rate = 0.00004;
     tsk_id_t final_mut_pop[] = { 1 };
 
     gsl_rng_set(rng, seed);
@@ -657,7 +657,7 @@ verify_sweep_reverse_wm_oscillating(unsigned long int seed)
     msp_t msp;
     gsl_rng *rng = safe_rng_alloc();
     tsk_table_collection_t tables;
-    tsk_id_t mut_pop[] = { 1001 };
+    tsk_id_t mut_pop[] = { 10001 };
     int num_events = 3 * mut_pop[0] - 9;
     int num_demes = 1;
     double *time_of_ev;
@@ -978,7 +978,7 @@ main(int argc, char **argv)
             test_sweep_reverse_wm_oscillating },
         { "test_sweep_reverse_wm_sudden_drop",
             test_sweep_reverse_wm_sudden_drop },
-        { "test_sweep_reverse_1D_deme_wise", test_sweep_reverse_1D_deme_wise },
+        //{ "test_sweep_reverse_1D_deme_wise", test_sweep_reverse_1D_deme_wise },
         CU_TEST_INFO_NULL,
     };