Make visualize and distinguish notebooks kind of work with the new multidents.

5 files changed, 204 insertions, 578 deletions

diff --git a/analysis/scalarmults/common.py b/analysis/scalarmults/common.py
index f1299dd..d3a784c 100644
--- a/analysis/scalarmults/common.py
+++ b/analysis/scalarmults/common.py
@@ -1,4 +1,5 @@
 import itertools
+import hashlib
 from datetime import timedelta
 from enum import Enum
 from operator import itemgetter
@@ -249,7 +250,13 @@ class ProbMap:
         return self.probs.items()
 
     def narrow(self, divisors: set[int]):
-        self.probs = {k:v for k, v in self.probs.items() if k in divisors}
+        divisors_hash = hashlib.blake2b(str(sorted(divisors)).encode(), digest_size=8).digest()
+        if self.divisors_hash == divisors_hash:
+            # Already narrow.
+            return
+        for kdel in set(self.probs.keys()).difference(divisors):
+            del self.probs[kdel]
+        self.divisors_hash = divisors_hash
 
     def merge(self, other: "ProbMap") -> None:
         if self.divisors_hash != other.divisors_hash:
diff --git a/analysis/scalarmults/distinguish.ipynb b/analysis/scalarmults/distinguish.ipynb
index fdb3f6a..4165d87 100644
--- a/analysis/scalarmults/distinguish.ipynb
+++ b/analysis/scalarmults/distinguish.ipynb
@@ -41,6 +41,23 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "119b1e18-eeb0-4b70-bec3-ff277641403f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def silence():\n",
+    "    import warnings\n",
+    "    warnings.filterwarnings(\n",
+    "        \"ignore\",\n",
+    "        message=\"The number of unique classes is greater than 50% of the number of samples.*\",\n",
+    "        category=UserWarning\n",
+    "    )\n",
+    "silence()"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "id": "4868c083-8073-453d-b508-704fcb6d6f2a",
    "metadata": {},
@@ -57,7 +74,6 @@
    "outputs": [],
    "source": [
     "divisor_name = \"all\"\n",
-    "kind = \"all\"\n",
     "allfeats = list(filter(lambda feat: feat not in (1,2,3,4,5), divisor_map[divisor_name]))"
    ]
   },
@@ -69,14 +85,10 @@
    "outputs": [],
    "source": [
     "# Load\n",
-    "try:\n",
-    "    with open(f\"{divisor_name}_{kind}_distrs.pickle\", \"rb\") as f:\n",
-    "        distributions_mults = pickle.load(f)\n",
-    "except FileNotFoundError:\n",
-    "    with open(f\"all_{kind}_distrs.pickle\", \"rb\") as f:\n",
-    "        distributions_mults = pickle.load(f)\n",
-    "    for probmap in distributions_mults.values():\n",
-    "        probmap.narrow(allfeats)"
+    "with open(f\"merged.pickle\", \"rb\") as f:\n",
+    "    distributions_mults = pickle.load(f)\n",
+    "for probmap in tqdm(distributions_mults.values()):\n",
+    "    probmap.narrow(allfeats)"
    ]
   },
   {
@@ -127,7 +139,7 @@
    "source": [
     "# Now go over all divisors, cluster based on overlapping CI for given n?\n",
     "io_map = {mult:{} for mult in allmults}\n",
-    "for divisor in allfeats:\n",
+    "for divisor in tqdm(allfeats):\n",
     "    prev_ci_low = None\n",
     "    prev_ci_high = None\n",
     "    groups = {}\n",
@@ -250,7 +262,7 @@
    "source": [
     "simulations = 1000\n",
     "\n",
-    "for nattack in trange(100, 10000, 100):\n",
+    "for nattack in trange(100, 10100, 100):\n",
     "    successes = 0\n",
     "    pathiness = 0\n",
     "    for i in range(simulations):\n",
@@ -450,8 +462,19 @@
     "                        scorer):\n",
     "    classifier.set_params(nattack=nattack)\n",
     "    classifier.fit(X, y)\n",
+    "    #print(\"Make instance\")\n",
+    "    #print(X)\n",
+    "    #print(y)\n",
     "    X_samp, y_samp = make_instance(nattack, simulations, X, y)\n",
-    "    return scorer(classifier, X_samp, y_samp)\n",
+    "    #print(\"Here!\")\n",
+    "    #print(X_samp)\n",
+    "    #print(y_samp)\n",
+    "    #print(X_samp.shape, y_samp.shape)\n",
+    "    #print(classifier.X_.shape, classifier.y_.shape)\n",
+    "    #print(scorer)\n",
+    "    score = scorer(classifier, X_samp, y_samp)\n",
+    "    #print(score)\n",
+    "    return score\n",
     "\n",
     "\n",
     "def average_rank_score(y_true, y_pred, labels=None):\n",
@@ -514,6 +537,7 @@
     "    top_k_accuracy_score,\n",
     "    greater_is_better=True,\n",
     "    response_method=(\"decision_function\", \"predict_proba\"),\n",
+    "    labels=np.arange(nmults)\n",
     ")\n",
     "\n",
     "#accuracy_scorer.__str__ = lambda self: \"Accuracy\"\n",
@@ -522,7 +546,8 @@
     "    top_k_accuracy_score,\n",
     "    greater_is_better=True,\n",
     "    response_method=(\"decision_function\", \"predict_proba\"),\n",
-    "    k=5\n",
+    "    k=5,\n",
+    "    labels=np.arange(nmults)\n",
     ")\n",
     "\n",
     "#top_5_scorer.__str__ = lambda self: \"Top-5 accuracy\"\n",
@@ -531,7 +556,8 @@
     "    top_k_accuracy_score,\n",
     "    greater_is_better=True,\n",
     "    response_method=(\"decision_function\", \"predict_proba\"),\n",
-    "    k=10\n",
+    "    k=10,\n",
+    "    labels=np.arange(nmults)\n",
     ")\n",
     "\n",
     "#top_10_scorer.__str__ = lambda self: \"Top-10 accuracy\"\n",
@@ -540,6 +566,7 @@
     "    average_rank_score,\n",
     "    greater_is_better=False,\n",
     "    response_method=(\"decision_function\", \"predict_proba\"),\n",
+    "    labels=np.arange(nmults)\n",
     ")\n",
     "\n",
     "#avg_rank_scorer.__str__ = lambda self: \"Average rank\""
@@ -581,7 +608,6 @@
     "        self.scorer = scorer\n",
     "\n",
     "class RandomFeatures(FeaturesByClassification):\n",
-    "\n",
     "    def __init__(self,\n",
     "             allfeats: list[int],\n",
     "             mults: dict[MultIdent, ProbMap],\n",
@@ -607,7 +633,7 @@
     "            return startwith\n",
     "\n",
     "    def select(self, nfeats: int, startwith: list[int] = None) -> tuple[list[int], float]:\n",
-    "        with TaskExecutor(max_workers=self.num_workers) as pool:\n",
+    "        with TaskExecutor(max_workers=self.num_workers, initializer=silence) as pool:\n",
     "            feat_map = []\n",
     "            for i in range(self.retries):\n",
     "                feats = self._select_random(nfeats, startwith)\n",
@@ -638,7 +664,7 @@
     "            raise ValueError(\"No features to select.\")\n",
     "        available_feats = list(filter(lambda feat: feat not in startwith, self.allfeats))\n",
     "        current = list(startwith)\n",
-    "        with TaskExecutor(max_workers=self.num_workers) as pool:\n",
+    "        with TaskExecutor(max_workers=self.num_workers, initializer=silence) as pool:\n",
     "            while toselect > 0:\n",
     "                for feat in available_feats:\n",
     "                    feats = current + [feat]\n",
@@ -684,7 +710,7 @@
    "source": [
     "def plot_performance(classifier, scorer, simulations, feature_map, mults, num_workers = 30):\n",
     "    scores = {}\n",
-    "    with TaskExecutor(max_workers=num_workers) as pool:\n",
+    "    with TaskExecutor(max_workers=num_workers, initializer=silence) as pool:\n",
     "        for (nattack, nfeats), feats in feature_map.items():\n",
     "            X, y = to_sklearn(mults, feats)\n",
     "            pool.submit_task((nattack, nfeats),\n",
@@ -755,11 +781,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "simulations = 500\n",
+    "simulations = 15000\n",
     "retries = 500\n",
     "nattack = range(50, 350, 50)\n",
     "nfeats = range(1, 11)\n",
-    "num_workers = 30\n",
+    "num_workers = 20\n",
     "\n",
     "euclid_classifier = EuclidClassifier()\n",
     "tree_random_subsets = RandomFeatures(sorted(feats_in_tree), distributions_mults, num_workers,\n",
@@ -1191,7 +1217,7 @@
     "def compute_redundancy(feats: list[int], priors, probs, nattack: int, num_workers: int = 30):\n",
     "    nallfeats = len(feats)\n",
     "    redundancy = np.zeros((nallfeats, nallfeats), dtype=np.float64)\n",
-    "    with TaskExecutor(max_workers=num_workers) as pool:\n",
+    "    with TaskExecutor(max_workers=num_workers, initializer=silence) as pool:\n",
     "        for i in trange(nallfeats):\n",
     "            for j in range(nallfeats):\n",
     "                if i < j:\n",
@@ -1480,7 +1506,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c5598117-4c54-4721-9fc5-68432fb8e230",
+   "id": "b04fa91e-28cd-4cff-81d7-7db4359b04a1",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1502,7 +1528,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/analysis/scalarmults/simulate.ipynb b/analysis/scalarmults/simulate.ipynb
index dae8d06..9b450b1 100644
--- a/analysis/scalarmults/simulate.ipynb
+++ b/analysis/scalarmults/simulate.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "b4386513-cc14-434b-a748-2863f8657452",
    "metadata": {},
    "outputs": [],
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3463a7bd-34d8-458b-8ceb-dddf99de21dc",
    "metadata": {},
    "outputs": [],
@@ -79,21 +79,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "170c11fc-86cf-4eb1-bf4e-b2e44b2d7ac5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scalar multipliers considered:  65\n",
-      "Scalar multipliers (with a single countermeasure) considered:  390\n",
-      "Error models considered:  32\n",
-      "Total configurations considered:  12480\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nmults = len(all_mults)\n",
     "nmults_ctr = len(all_mults_with_ctr)\n",
@@ -116,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "4d5c7f10-618f-4612-b594-81d1607b0d1d",
    "metadata": {},
    "outputs": [],
@@ -135,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "07bc266d-35eb-4f6d-bdba-e9f6f66827f1",
    "metadata": {},
    "outputs": [],
@@ -166,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6f3e9ffa-19e8-46b2-a6ad-1d318d5c8e17",
    "metadata": {},
    "outputs": [],
@@ -201,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "64799c16-8113-4eff-81de-6a3e547eb5c5",
    "metadata": {},
    "outputs": [],
@@ -228,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "ac630a08-4120-41cf-b3bb-1827ef469542",
    "metadata": {},
    "outputs": [],
@@ -267,421 +256,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "84359084-4116-436c-92cd-d43fdfeca842",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b7c686d503d14bb69250747a881fe3f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Computing multiple graphs.:   0%|          | 0/390 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Got SlidingWindow_w:6,recode:LTR.\n",
-      "Got SlidingWindow_w:5,recode:LTR+bt.\n",
-      "Got SlidingWindow_w:4,recode:LTR.\n",
-      "Got SlidingWindow_w:3,recode:LTR+bt.\n",
-      "Got SlidingWindow_w:5,recode:LTR+multiplicative.\n",
-      "Got SlidingWindow_w:5,recode:LTR+gsr.\n",
-      "Got SlidingWindow_w:4,recode:LTR+multiplicative.\n",
-      "Got SlidingWindow_w:2,recode:LTR.\n",
-      "Got SlidingWindow_w:3,recode:LTR+multiplicative.\n",
-      "Got SlidingWindow_w:4,recode:LTR+gsr.\n",
-      "Got SlidingWindow_w:3,recode:LTR.\n",
-      "Got SlidingWindow_w:2,recode:LTR+bt.\n",
-      "Got SlidingWindow_w:3,recode:LTR+gsr.\n",
-      "Got SlidingWindow_w:6,recode:LTR+gsr.\n",
-      "Got SlidingWindow_w:5,recode:LTR.\n",
-      "Got SlidingWindow_w:4,recode:LTR+bt.\n",
-      "Got SlidingWindow_w:6,recode:LTR+bt.\n",
-      "Got SlidingWindow_w:4,recode:LTR+euclidean.\n",
-      "Got SlidingWindow_w:5,recode:LTR+euclidean.\n",
-      "Got SlidingWindow_w:3,recode:LTR+euclidean.\n",
-      "Got SlidingWindow_w:4,recode:LTR+additive.\n",
-      "Got SlidingWindow_w:2,recode:LTR+multiplicative.\n",
-      "Got SlidingWindow_w:6,recode:LTR+euclidean.\n",
-      "Got SlidingWindow_w:2,recode:LTR+gsr.\n",
-      "Got SlidingWindow_w:6,recode:LTR+multiplicative.\n",
-      "Got SlidingWindow_w:2,recode:RTL.\n",
-      "Got SlidingWindow_w:2,recode:LTR+euclidean.\n",
-      "Got SlidingWindow_w:6,recode:LTR+additive.\n",
-      "Got SlidingWindow_w:5,recode:LTR+additive.\n",
-      "Got SlidingWindow_w:3,recode:LTR+additive.\n",
-      "Got SlidingWindow_w:3,recode:RTL.\n",
-      "Got SlidingWindow_w:2,recode:RTL+gsr.\n",
-      "Got SlidingWindow_w:2,recode:RTL+bt.\n",
-      "Got SlidingWindow_w:3,recode:RTL+multiplicative.\n",
-      "Got SlidingWindow_w:3,recode:RTL+gsr.\n",
-      "Got SlidingWindow_w:3,recode:RTL+bt.\n",
-      "Got SlidingWindow_w:5,recode:RTL.\n",
-      "Got SlidingWindow_w:4,recode:RTL.\n",
-      "Got SlidingWindow_w:2,recode:LTR+additive.\n",
-      "Got SlidingWindow_w:2,recode:RTL+multiplicative.\n",
-      "Got SlidingWindow_w:4,recode:RTL+bt.\n",
-      "Got SlidingWindow_w:4,recode:RTL+gsr.\n",
-      "Got SlidingWindow_w:5,recode:RTL+gsr.\n",
-      "Got SlidingWindow_w:4,recode:RTL+multiplicative.\n",
-      "Got SlidingWindow_w:5,recode:RTL+multiplicative.\n",
-      "Got SlidingWindow_w:3,recode:RTL+euclidean.\n",
-      "Got SlidingWindow_w:6,recode:RTL.\n",
-      "Got SlidingWindow_w:5,recode:RTL+bt.\n",
-      "Got SlidingWindow_w:6,recode:RTL+gsr.\n",
-      "Got SlidingWindow_w:2,recode:RTL+euclidean.\n",
-      "Got SlidingWindow_w:5,recode:RTL+euclidean.\n",
-      "Got SlidingWindow_w:4,recode:RTL+euclidean.\n",
-      "Got SlidingWindow_w:3,recode:RTL+additive.\n",
-      "Got SlidingWindow_w:5,recode:RTL+additive.\n",
-      "Got SlidingWindow_w:6,recode:RTL+bt.\n",
-      "Got SlidingWindow_w:4,recode:RTL+additive.\n",
-      "Got FixedWindowLTR_m:2+gsr.\n",
-      "Got SlidingWindow_w:6,recode:RTL+multiplicative.\n",
-      "Got FixedWindowLTR_m:4.\n",
-      "Got SlidingWindow_w:2,recode:RTL+additive.\n",
-      "Got FixedWindowLTR_m:2.\n",
-      "Got FixedWindowLTR_m:2+bt.\n",
-      "Got SlidingWindow_w:6,recode:RTL+euclidean.\n",
-      "Got FixedWindowLTR_m:8.\n",
-      "Got FixedWindowLTR_m:2+multiplicative.\n",
-      "Got SlidingWindow_w:6,recode:RTL+additive.\n",
-      "Got FixedWindowLTR_m:4+bt.\n",
-      "Got FixedWindowLTR_m:8+bt.\n",
-      "Got FixedWindowLTR_m:4+gsr.\n",
-      "Got FixedWindowLTR_m:4+multiplicative.\n",
-      "Got FixedWindowLTR_m:16.\n",
-      "Got FixedWindowLTR_m:16+gsr.\n",
-      "Got FixedWindowLTR_m:8+gsr.\n",
-      "Got FixedWindowLTR_m:2+euclidean.\n",
-      "Got FixedWindowLTR_m:8+multiplicative.\n",
-      "Got FixedWindowLTR_m:16+multiplicative.\n",
-      "Got FixedWindowLTR_m:4+euclidean.\n",
-      "Got FixedWindowLTR_m:16+bt.\n",
-      "Got FixedWindowLTR_m:32+bt.\n",
-      "Got FixedWindowLTR_m:64.\n",
-      "Got FixedWindowLTR_m:32.\n",
-      "Got FixedWindowLTR_m:8+euclidean.\n",
-      "Got FixedWindowLTR_m:32+gsr.\n",
-      "Got FixedWindowLTR_m:2+additive.\n",
-      "Got FixedWindowLTR_m:32+multiplicative.\n",
-      "Got FixedWindowLTR_m:64+gsr.\n",
-      "Got FixedWindowLTR_m:8+additive.\n",
-      "Got FixedWindowLTR_m:4+additive.\n",
-      "Got FixedWindowLTR_m:16+additive.\n",
-      "Got FixedWindowLTR_m:64+bt.\n",
-      "Got FixedWindowLTR_m:16+euclidean.\n",
-      "Got FixedWindowLTR_m:32+euclidean.\n",
-      "Got FixedWindowLTR_m:64+multiplicative.\n",
-      "Got WindowBooth_w:3.\n",
-      "Got WindowBooth_w:2+gsr.\n",
-      "Got WindowBooth_w:2+bt.\n",
-      "Got WindowBooth_w:2.\n",
-      "Got FixedWindowLTR_m:32+additive.\n",
-      "Got WindowBooth_w:4.\n",
-      "Got WindowBooth_w:3+bt.\n",
-      "Got FixedWindowLTR_m:64+euclidean.\n",
-      "Got WindowBooth_w:3+gsr.\n",
-      "Got FixedWindowLTR_m:64+additive.\n",
-      "Got WindowBooth_w:4+gsr.\n",
-      "Got WindowBooth_w:5.\n",
-      "Got WindowBooth_w:5+gsr.\n",
-      "Got WindowBooth_w:2+multiplicative.\n",
-      "Got WindowBooth_w:6.\n",
-      "Got WindowBooth_w:3+multiplicative.\n",
-      "Got WindowBooth_w:5+bt.\n",
-      "Got WindowBooth_w:4+bt.\n",
-      "Got WindowBooth_w:6+gsr.\n",
-      "Got WindowBooth_w:6+bt.\n",
-      "Got WindowBooth_w:2+additive.\n",
-      "Got WindowNAF_w:2.\n",
-      "Got WindowBooth_w:4+multiplicative.\n",
-      "Got WindowBooth_w:5+multiplicative.\n",
-      "Got WindowBooth_w:3+euclidean.\n",
-      "Got WindowBooth_w:3+additive.\n",
-      "Got WindowBooth_w:4+additive.\n",
-      "Got WindowNAF_w:4.\n",
-      "Got WindowBooth_w:2+euclidean.\n",
-      "Got WindowNAF_w:2+gsr.\n",
-      "Got WindowNAF_w:3.\n",
-      "Got WindowNAF_w:2+multiplicative.\n",
-      "Got WindowBooth_w:5+additive.\n",
-      "Got WindowNAF_w:2+bt.\n",
-      "Got WindowNAF_w:3+bt.\n",
-      "Got WindowBooth_w:6+additive.\n",
-      "Got WindowNAF_w:3+gsr.\n",
-      "Got WindowNAF_w:3+multiplicative.\n",
-      "Got WindowBooth_w:6+multiplicative.\n",
-      "Got WindowNAF_w:4+gsr.\n",
-      "Got WindowNAF_w:4+bt.\n",
-      "Got WindowNAF_w:2+euclidean.\n",
-      "Got WindowBooth_w:4+euclidean.\n",
-      "Got WindowNAF_w:5.\n",
-      "Got WindowNAF_w:4+multiplicative.\n",
-      "Got WindowNAF_w:5+bt.\n",
-      "Got WindowNAF_w:6.\n",
-      "Got WindowNAF_w:6+bt.\n",
-      "Got WindowNAF_w:3+euclidean.\n",
-      "Got WindowNAF_w:5+gsr.\n",
-      "Got WindowBooth_w:5+euclidean.\n",
-      "Got WindowNAF_w:5+multiplicative.\n",
-      "Got WindowBooth_w:6+euclidean.\n",
-      "Got WindowNAF_w:6+gsr.\n",
-      "Got WindowNAF_w:3+additive.\n",
-      "Got WindowNAF_w:5+euclidean.\n",
-      "Got WindowNAF_w:2+additive.\n",
-      "Got BinaryNAF_always:False,dir:LTR+bt.\n",
-      "Got BinaryNAF_always:False,dir:LTR.\n",
-      "Got WindowNAF_w:4+euclidean.\n",
-      "Got BinaryNAF_always:False,dir:LTR+gsr.\n",
-      "Got WindowNAF_w:6+multiplicative.\n",
-      "Got WindowNAF_w:4+additive.\n",
-      "Got BinaryNAF_always:False,dir:RTL.\n",
-      "Got BinaryNAF_always:False,dir:LTR+multiplicative.\n",
-      "Got WindowNAF_w:6+euclidean.\n",
-      "Got WindowNAF_w:5+additive.\n",
-      "Got WindowNAF_w:6+additive.\n",
-      "Got BinaryNAF_always:False,dir:RTL+gsr.\n",
-      "Got BinaryNAF_always:False,dir:RTL+multiplicative.\n",
-      "Got BinaryNAF_always:True,dir:LTR.\n",
-      "Got BinaryNAF_always:False,dir:RTL+bt.\n",
-      "Got BinaryNAF_always:False,dir:LTR+euclidean.\n",
-      "Got BinaryNAF_always:True,dir:LTR+gsr.\n",
-      "Got BinaryNAF_always:False,dir:LTR+additive.\n",
-      "Got BinaryNAF_always:True,dir:LTR+multiplicative.\n",
-      "Got BinaryNAF_always:True,dir:RTL.\n",
-      "Got BinaryNAF_always:False,dir:RTL+euclidean.\n",
-      "Got LTR_always:False,complete:True+bt.\n",
-      "Got BinaryNAF_always:True,dir:LTR+bt.\n",
-      "Got LTR_always:False,complete:True+gsr.\n",
-      "Got LTR_always:False,complete:True.\n",
-      "Got BinaryNAF_always:True,dir:RTL+multiplicative.\n",
-      "Got BinaryNAF_always:True,dir:RTL+gsr.\n",
-      "Got BinaryNAF_always:True,dir:LTR+euclidean.\n",
-      "Got BinaryNAF_always:True,dir:RTL+bt.\n",
-      "Got BinaryNAF_always:False,dir:RTL+additive.\n",
-      "Got LTR_always:False,complete:False.\n",
-      "Got LTR_always:True,complete:True.\n",
-      "Got LTR_always:False,complete:False+gsr.\n",
-      "Got LTR_always:False,complete:True+multiplicative.\n",
-      "Got LTR_always:True,complete:True+gsr.\n",
-      "Got BinaryNAF_always:True,dir:LTR+additive.\n",
-      "Got LTR_always:False,complete:False+multiplicative.\n",
-      "Got LTR_always:False,complete:False+bt.\n",
-      "Got LTR_always:True,complete:True+bt.\n",
-      "Got LTR_always:False,complete:True+additive.\n",
-      "Got BinaryNAF_always:True,dir:RTL+euclidean.\n",
-      "Got BinaryNAF_always:True,dir:RTL+additive.\n",
-      "Got RTL_always:False,complete:True.\n",
-      "Got LTR_always:False,complete:False+euclidean.\n",
-      "Got LTR_always:True,complete:False.\n",
-      "Got LTR_always:True,complete:False+gsr.\n",
-      "Got LTR_always:False,complete:True+euclidean.\n",
-      "Got RTL_always:False,complete:True+bt.\n",
-      "Got RTL_always:False,complete:True+gsr.\n",
-      "Got LTR_always:True,complete:False+bt.\n",
-      "Got LTR_always:False,complete:False+additive.\n",
-      "Got LTR_always:True,complete:False+multiplicative.\n",
-      "Got RTL_always:False,complete:False.\n",
-      "Got LTR_always:True,complete:True+additive.\n",
-      "Got LTR_always:True,complete:True+multiplicative.\n",
-      "Got RTL_always:False,complete:False+gsr.\n",
-      "Got RTL_always:False,complete:True+multiplicative.\n",
-      "Got RTL_always:True,complete:True+gsr.\n",
-      "Got LTR_always:True,complete:False+euclidean.\n",
-      "Got RTL_always:True,complete:True.\n",
-      "Got RTL_always:True,complete:True+bt.\n",
-      "Got RTL_always:False,complete:False+bt.\n",
-      "Got RTL_always:False,complete:False+multiplicative.\n",
-      "Got RTL_always:True,complete:False.\n",
-      "Got RTL_always:False,complete:True+additive.\n",
-      "Got Coron.\n",
-      "Got RTL_always:True,complete:False+gsr.\n",
-      "Got RTL_always:False,complete:False+euclidean.\n",
-      "Got FullPrecomp_always:False,complete:True.\n",
-      "Got RTL_always:True,complete:False+multiplicative.\n",
-      "Got LTR_always:True,complete:False+additive.\n",
-      "Got RTL_always:True,complete:False+bt.\n",
-      "Got RTL_always:False,complete:False+additive.\n",
-      "Got RTL_always:False,complete:True+euclidean.\n",
-      "Got Coron+bt.\n",
-      "Got Coron+gsr.\n",
-      "Got FullPrecomp_always:False,complete:True+bt.\n",
-      "Got FullPrecomp_always:False,complete:True+gsr.\n",
-      "Got Coron+multiplicative.\n",
-      "Got RTL_always:True,complete:False+euclidean.\n",
-      "Got FullPrecomp_always:False,complete:True+additive.\n",
-      "Got FullPrecomp_always:True,complete:True.\n",
-      "Got FullPrecomp_always:True,complete:True+gsr.\n",
-      "Got RTL_always:True,complete:True+multiplicative.\n",
-      "Got RTL_always:True,complete:True+additive.\n",
-      "Got FullPrecomp_always:False,complete:False.\n",
-      "Got FullPrecomp_always:False,complete:False+gsr.\n",
-      "Got FullPrecomp_always:False,complete:True+multiplicative.\n",
-      "Got Coron+euclidean.\n",
-      "Got LTR_always:True,complete:True+euclidean.\n",
-      "Got FullPrecomp_always:False,complete:True+euclidean.\n",
-      "Got FullPrecomp_always:False,complete:False+additive.\n",
-      "Got FullPrecomp_always:True,complete:True+bt.\n",
-      "Got FullPrecomp_always:False,complete:False+bt.\n",
-      "Got RTL_always:True,complete:False+additive.\n",
-      "Got FullPrecomp_always:True,complete:False.\n",
-      "Got RTL_always:True,complete:True+euclidean.\n",
-      "Got Coron+additive.\n",
-      "Got FullPrecomp_always:True,complete:True+additive.\n",
-      "Got FullPrecomp_always:True,complete:False+bt.\n",
-      "Got FullPrecomp_always:False,complete:False+multiplicative.\n",
-      "Got FullPrecomp_always:True,complete:False+gsr.\n",
-      "Got SimpleLadder_complete:True.\n",
-      "Got FullPrecomp_always:False,complete:False+euclidean.\n",
-      "Got SimpleLadder_complete:True+gsr.\n",
-      "Got FullPrecomp_always:True,complete:False+multiplicative.\n",
-      "Got SimpleLadder_complete:False.\n",
-      "Got Comb_w:2,always:True.\n",
-      "Got SimpleLadder_complete:True+bt.\n",
-      "Got FullPrecomp_always:True,complete:False+additive.\n",
-      "Got SimpleLadder_complete:False+gsr.\n",
-      "Got SimpleLadder_complete:False+bt.\n",
-      "Got FullPrecomp_always:True,complete:True+multiplicative.\n",
-      "Got SimpleLadder_complete:False+multiplicative.\n",
-      "Got Comb_w:3,always:True.\n",
-      "Got FullPrecomp_always:True,complete:False+euclidean.\n",
-      "Got Comb_w:2,always:True+gsr.\n",
-      "Got Comb_w:3,always:True+bt.\n",
-      "Got Comb_w:2,always:True+bt.\n",
-      "Got Comb_w:3,always:True+gsr.\n",
-      "Got FullPrecomp_always:True,complete:True+euclidean.\n",
-      "Got Comb_w:4,always:True+gsr.\n",
-      "Got Comb_w:2,always:True+additive.\n",
-      "Got Comb_w:4,always:True.\n",
-      "Got SimpleLadder_complete:True+multiplicative.\n",
-      "Got SimpleLadder_complete:False+euclidean.\n",
-      "Got Comb_w:3,always:True+additive.\n",
-      "Got SimpleLadder_complete:True+additive.\n",
-      "Got Comb_w:4,always:True+bt.\n",
-      "Got Comb_w:2,always:True+multiplicative.\n",
-      "Got Comb_w:5,always:True.\n",
-      "Got Comb_w:5,always:True+gsr.\n",
-      "Got SimpleLadder_complete:False+additive.\n",
-      "Got Comb_w:5,always:True+additive.\n",
-      "Got Comb_w:4,always:True+additive.\n",
-      "Got Comb_w:5,always:True+bt.\n",
-      "Got Comb_w:6,always:True.\n",
-      "Got Comb_w:6,always:True+bt.\n",
-      "Got Comb_w:3,always:True+multiplicative.\n",
-      "Got Comb_w:6,always:True+gsr.\n",
-      "Got Comb_w:4,always:True+multiplicative.\n",
-      "Got Comb_w:2,always:False.\n",
-      "Got Comb_w:6,always:True+additive.\n",
-      "Got Comb_w:3,always:True+euclidean.\n",
-      "Got SimpleLadder_complete:True+euclidean.\n",
-      "Got Comb_w:3,always:False.\n",
-      "Got Comb_w:2,always:False+bt.\n",
-      "Got Comb_w:2,always:False+gsr.\n",
-      "Got Comb_w:3,always:False+gsr.\n",
-      "Got Comb_w:2,always:True+euclidean.\n",
-      "Got Comb_w:3,always:False+bt.\n",
-      "Got Comb_w:5,always:True+multiplicative.\n",
-      "Got Comb_w:4,always:False.\n",
-      "Got Comb_w:3,always:False+additive.\n",
-      "Got Comb_w:4,always:True+euclidean.\n",
-      "Got Comb_w:4,always:False+gsr.\n",
-      "Got Comb_w:5,always:True+euclidean.\n",
-      "Got Comb_w:2,always:False+additive.\n",
-      "Got Comb_w:4,always:False+bt.\n",
-      "Got Comb_w:4,always:False+additive.\n",
-      "Got Comb_w:6,always:True+multiplicative.\n",
-      "Got Comb_w:6,always:True+euclidean.\n",
-      "Got Comb_w:5,always:False+gsr.\n",
-      "Got Comb_w:5,always:False.\n",
-      "Got Comb_w:3,always:False+multiplicative.\n",
-      "Got Comb_w:2,always:False+multiplicative.\n",
-      "Got Comb_w:5,always:False+additive.\n",
-      "Got Comb_w:4,always:False+multiplicative.\n",
-      "Got Comb_w:5,always:False+bt.\n",
-      "Got BGMW_w:2,dir:LTR.\n",
-      "Got Comb_w:2,always:False+euclidean.\n",
-      "Got Comb_w:3,always:False+euclidean.\n",
-      "Got Comb_w:6,always:False+bt.\n",
-      "Got Comb_w:6,always:False.\n",
-      "Got Comb_w:6,always:False+additive.\n",
-      "Got BGMW_w:3,dir:LTR+gsr.\n",
-      "Got BGMW_w:2,dir:LTR+bt.\n",
-      "Got Comb_w:6,always:False+gsr.\n",
-      "Got BGMW_w:3,dir:LTR.\n",
-      "Got Comb_w:4,always:False+euclidean.\n",
-      "Got BGMW_w:2,dir:LTR+gsr.\n",
-      "Got BGMW_w:2,dir:LTR+additive.\n",
-      "Got BGMW_w:3,dir:LTR+bt.\n",
-      "Got Comb_w:5,always:False+multiplicative.\n",
-      "Got BGMW_w:3,dir:LTR+additive.\n",
-      "Got BGMW_w:4,dir:LTR.\n",
-      "Got BGMW_w:4,dir:LTR+gsr.\n",
-      "Got BGMW_w:4,dir:LTR+bt.\n",
-      "Got BGMW_w:5,dir:LTR+gsr.\n",
-      "Got BGMW_w:4,dir:LTR+additive.\n",
-      "Got BGMW_w:2,dir:LTR+multiplicative.\n",
-      "Got BGMW_w:5,dir:LTR.\n",
-      "Got BGMW_w:5,dir:LTR+bt.\n",
-      "Got BGMW_w:3,dir:LTR+multiplicative.\n",
-      "Got BGMW_w:3,dir:LTR+euclidean.\n",
-      "Got Comb_w:5,always:False+euclidean.\n",
-      "Got BGMW_w:2,dir:LTR+euclidean.\n",
-      "Got BGMW_w:6,dir:LTR.\n",
-      "Got BGMW_w:5,dir:LTR+additive.\n",
-      "Got BGMW_w:2,dir:RTL.\n",
-      "Got BGMW_w:4,dir:LTR+multiplicative.\n",
-      "Got BGMW_w:6,dir:LTR+gsr.\n",
-      "Got BGMW_w:2,dir:RTL+gsr.\n",
-      "Got BGMW_w:6,dir:LTR+bt.\n",
-      "Got BGMW_w:6,dir:LTR+additive.\n",
-      "Got Comb_w:6,always:False+multiplicative.\n",
-      "Got Comb_w:6,always:False+euclidean.\n",
-      "Got BGMW_w:2,dir:RTL+bt.\n",
-      "Got BGMW_w:2,dir:RTL+additive.\n",
-      "Got BGMW_w:3,dir:RTL.\n",
-      "Got BGMW_w:5,dir:LTR+multiplicative.\n",
-      "Got BGMW_w:3,dir:RTL+bt.\n",
-      "Got BGMW_w:4,dir:LTR+euclidean.\n",
-      "Got BGMW_w:3,dir:RTL+gsr.\n",
-      "Got BGMW_w:4,dir:RTL.\n",
-      "Got BGMW_w:6,dir:LTR+multiplicative.\n",
-      "Got BGMW_w:2,dir:RTL+multiplicative.\n",
-      "Got BGMW_w:4,dir:RTL+gsr.\n",
-      "Got BGMW_w:5,dir:LTR+euclidean.\n",
-      "Got BGMW_w:4,dir:RTL+bt.\n",
-      "Got BGMW_w:5,dir:RTL.\n",
-      "Got BGMW_w:2,dir:RTL+euclidean.\n",
-      "Got BGMW_w:5,dir:RTL+gsr.\n",
-      "Got BGMW_w:5,dir:RTL+bt.\n",
-      "Got BGMW_w:4,dir:RTL+additive.\n",
-      "Got BGMW_w:6,dir:LTR+euclidean.\n",
-      "Got BGMW_w:3,dir:RTL+additive.\n",
-      "Got BGMW_w:6,dir:RTL.\n",
-      "Got BGMW_w:3,dir:RTL+euclidean.\n",
-      "Got BGMW_w:5,dir:RTL+additive.\n",
-      "Got BGMW_w:4,dir:RTL+euclidean.\n",
-      "Got BGMW_w:3,dir:RTL+multiplicative.\n",
-      "Got BGMW_w:6,dir:RTL+gsr.\n",
-      "Got BGMW_w:4,dir:RTL+multiplicative.\n",
-      "Got BGMW_w:6,dir:RTL+bt.\n",
-      "Got BGMW_w:6,dir:RTL+additive.\n",
-      "Got BGMW_w:5,dir:RTL+euclidean.\n",
-      "Got BGMW_w:5,dir:RTL+multiplicative.\n",
-      "Got BGMW_w:6,dir:RTL+multiplicative.\n",
-      "Got BGMW_w:6,dir:RTL+euclidean.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "chunk_id = randbytes(4).hex()\n",
     "with TaskExecutor(max_workers=num_workers, initializer=silence) as pool, tempfile.TemporaryDirectory() as tmp_dirname:\n",
@@ -715,69 +293,15 @@
    "execution_count": null,
    "id": "fbab8333-b8f1-4890-b38a-7bb34f5ffb02",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e682673036ab4053a6a2a5fc60194691",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Processing chunks:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing chunk 66897617, no probmaps found.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d0ac8b447b942f1bb6e31ee9efc9694",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading chunk 66897617.:   0%|          | 0/390 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1dfe709e6f37433aaa4dd6d0c4823373",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Processing 66897617.:   0%|          | 0/12480 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "with TaskExecutor(max_workers=num_workers, initializer=silence) as pool:\n",
     "    for in_fname in tqdm(glob.glob(f\"multiples_{bits}_{'init' if use_init else 'noinit'}_{'mult' if use_multiply else 'nomult'}_chunk*.pickle\"), desc=\"Processing chunks\", smoothing=0):\n",
     "        \n",
     "        match = re.match(\"multiples_(?P<bits>[0-9]+)_(?P<init>(?:no)?init)_(?P<mult>(?:no)?mult)_chunk(?P<id>[0-9a-f]+).pickle\", in_fname)\n",
-    "        bits = match.group(\"bits\")\n",
-    "        use_init = match.group(\"init\")\n",
-    "        use_multiply = match.group(\"mult\")\n",
     "        chunk_id = match.group(\"id\")\n",
     "        out_fname = f\"probs_{bits}_{use_init}_{use_multiply}_chunk{chunk_id}.pickle\"\n",
     "\n",
-    "\n",
     "        in_file = Path(in_fname)\n",
     "        out_file = Path(out_fname)\n",
     "\n",
@@ -847,6 +371,59 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a1bb9459-aa2b-4a07-970e-6e981ab3f97e",
+   "metadata": {},
+   "source": [
+    "## Merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f8daeda-f289-42c0-9157-19c0f688c6aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probmaps = {}\n",
+    "for in_fname in tqdm(glob.glob(f\"probs_{bits}_{'init' if use_init else 'noinit'}_{'mult' if use_multiply else 'nomult'}_chunk*.pickle\"), desc=\"Processing chunks\", smoothing=0):\n",
+    "    \n",
+    "    match = re.match(\"probs_(?P<bits>[0-9]+)_(?P<init>(?:no)?init)_(?P<mult>(?:no)?mult)_chunk(?P<id>[0-9a-f]+).pickle\", in_fname)\n",
+    "    chunk_id = match.group(\"id\")    \n",
+    "    \n",
+    "    with open(in_fname, \"rb\") as f:\n",
+    "        loading_bar = tqdm(total=ncfgs, desc=f\"Loading chunk {chunk_id}.\", smoothing=0)\n",
+    "        while True:\n",
+    "            try:\n",
+    "                mult, probmap = pickle.load(f)\n",
+    "                if mult in probmaps:\n",
+    "                    current = probmaps[mult]\n",
+    "                    current.merge(probmap)\n",
+    "                else:\n",
+    "                    probmaps[mult] = probmap\n",
+    "                loading_bar.update(1)\n",
+    "            except EOFError:\n",
+    "                break\n",
+    "            except pickle.UnpicklingError:\n",
+    "                print(\"Bad unpickling, the probs file is likely truncated.\")\n",
+    "                break\n",
+    "        loading_bar.close()\n",
+    "\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bc15256-8228-4498-9bbf-73e76eea3c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"merged.pickle\", \"wb\") as f:\n",
+    "    pickle.dump(probmaps, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "id": "228922dc-67bf-481a-9f08-4084695e2059",
    "metadata": {},
    "source": [
@@ -864,38 +441,18 @@
     "mult = next(iter(multiples_mults))\n",
     "res = multiples_mults[mult]\n",
     "\n",
-    "\n",
     "for checks in powerset(checks_add):\n",
     "    for precomp_to_affine in (True, False):\n",
     "        for check_condition in (\"all\", \"necessary\"):\n",
     "            error_model = ErrorModel(checks, check_condition=check_condition, precomp_to_affine=precomp_to_affine)\n",
     "            full = mult.with_error_model(error_model)\n",
     "            print(full)\n",
-    "            #with PyProfiler() as prof:\n",
-    "            probmap = evaluate_multiples(full, res, divisor_map[\"all\"])\n",
-    "            #print(prof.output_text(unicode=True, color=True))\n",
-    "            #print(probmap)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d291832-b0c7-4c3a-9989-22079e4e0f53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "multiples_mults = {}\n",
-    "for fname in glob.glob(f\"multiples_{bits}_{'init' if use_init else 'noinit'}_{'mult' if use_multiply else 'nomult'}_chunk*.pickle\"):\n",
-    "    with open(fname, \"rb\") as f:\n",
-    "        while True:\n",
-    "            try:\n",
-    "                mult, vals = pickle.load(f)\n",
-    "                if mult not in multiples_mults:\n",
-    "                    multiples_mults[mult] = vals\n",
-    "                else:\n",
-    "                    multiples_mults[mult].merge(vals)\n",
-    "            except EOFError:\n",
-    "                break"
+    "            with PyProfiler() as prof:\n",
+    "                probmap = evaluate_multiples(full, res, divisor_map[\"all\"])\n",
+    "            print(prof.output_text(unicode=True, color=True))\n",
+    "            break\n",
+    "        break\n",
+    "    break"
    ]
   },
   {
@@ -923,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/analysis/scalarmults/visualize.ipynb b/analysis/scalarmults/visualize.ipynb
index 709e566..d2d6b0f 100644
--- a/analysis/scalarmults/visualize.ipynb
+++ b/analysis/scalarmults/visualize.ipynb
@@ -19,6 +19,7 @@
     "import itertools\n",
     "import glob\n",
     "import gc\n",
+    "import random\n",
     "\n",
     "import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
@@ -126,9 +127,9 @@
    "source": [
     "selected_mults = all_mults\n",
     "divisor_name = \"all\"\n",
-    "kind = \"precomp+necessary\"\n",
     "showci = False\n",
-    "selected_divisors = divisor_map[divisor_name]"
+    "selected_divisors = divisor_map[divisor_name]\n",
+    "show_error_model = ErrorModel({}, \"all\", True)"
    ]
   },
   {
@@ -138,8 +139,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Optionally, load\n",
-    "with open(f\"{divisor_name}_{kind}_distrs.pickle\", \"rb\") as f:\n",
+    "with open(f\"merged.pickle\", \"rb\") as f:\n",
     "    distributions_mults = pickle.load(f)"
    ]
   },
@@ -152,21 +152,6 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ccc28f6-3994-4a0d-8639-2f6df4dddd26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for mult, probmap in distributions_mults.items():\n",
-    "    for divisor in sorted(divisor_map[divisor_name]):\n",
-    "        if divisor not in probmap.probs:\n",
-    "            print(f\"Missing {mult}, {divisor}\")\n",
-    "    if probmap.kind is not None and probmap.kind != kind:\n",
-    "        print(\"Bad kind! Did you forget to load?\")"
-   ]
-  },
-  {
    "cell_type": "markdown",
    "id": "9b6f169b-07b3-4b27-ba36-8b90418cd072",
    "metadata": {},
@@ -183,7 +168,7 @@
    "outputs": [],
    "source": [
     "for divisor_name in divisor_map:\n",
-    "    plot_mults = list(filter(lambda mult: mult in distributions_mults and mult.klass not in (CombMultiplier, BGMWMultiplier), all_mults_with_ctr))\n",
+    "    plot_mults = list(filter(lambda mult: mult in distributions_mults and mult.klass not in (CombMultiplier, BGMWMultiplier), distributions_mults))\n",
     "    print(divisor_name, \"nocomb\")\n",
     "    plot_divisors = sorted(divisor_map[divisor_name])\n",
     "    L = len(plot_divisors)\n",
@@ -196,14 +181,15 @@
     "    vals = np.zeros((N, L))\n",
     "    n_samples = 0\n",
     "    for i, mult in enumerate(plot_mults):\n",
+    "        clear_mult = mult.with_error_model(None)\n",
     "        probmap = distributions_mults[mult]\n",
     "        y_values = [probmap[l] for l in plot_divisors]\n",
     "        vals[i,] = y_values\n",
     "        ax.plot(x, y_values,\n",
-    "                color=colors[mult],\n",
-    "                linestyle=styles[mult],\n",
-    "                marker=markers[mult],\n",
-    "                label=str(mult) if mult.countermeasure is None else \"_nolegend_\")\n",
+    "                color=colors[clear_mult],\n",
+    "                linestyle=styles[clear_mult],\n",
+    "                marker=markers[clear_mult],\n",
+    "                label=str(mult) if mult.countermeasure is None and mult.error_model == show_error_model else \"_nolegend_\")\n",
     "        if showci:\n",
     "            cis = [conf_interval(p, probmap.samples) for p in y_values]\n",
     "            ci_low = [ci[0] for ci in cis]\n",
@@ -211,7 +197,7 @@
     "            ax.fill_between(x, ci_low, ci_high, color=\"black\", alpha=0.1)\n",
     "        n_samples += probmap.samples\n",
     "    \n",
-    "    ax.set_title(f\"{divisor_name} ({kind})\\nSamples: \" + str(n_samples//N))\n",
+    "    ax.set_title(f\"{divisor_name}\\nSamples: \" + str(n_samples//N))\n",
     "    \n",
     "    #var = np.var(vals, axis=0)\n",
     "    #ax.plot(x, var / np.max(var), label=\"cross-mult variance (normalized)\", ls=\"--\", lw=2, color=\"black\")\n",
@@ -231,7 +217,7 @@
     "    \n",
     "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
     "\n",
-    "    fig.savefig(f\"graphs/{kind}-kind/{divisor_name}-nocomb{'+ci' if showci else ''}.pdf\");\n",
+    "    fig.savefig(f\"graphs/{divisor_name}-nocomb{'+ci' if showci else ''}.pdf\");\n",
     "    plt.close()"
    ]
   },
@@ -321,28 +307,39 @@
    "outputs": [],
    "source": [
     "#divisor_name = \"powers_of_2_large\"\n",
-    "divisor_name = \"feature\"\n",
-    "plot_mults = list(filter(lambda mult: mult in distributions_mults, all_mults_with_ctr))\n",
+    "single_mult = random.choice(list(distributions_mults.keys())).with_error_model(None)\n",
+    "divisor_name = \"all_integers\"\n",
+    "plot_mults = list(filter(lambda mult: mult.with_error_model(None) == single_mult, distributions_mults))\n",
+    "plot_divisors = divisor_map[divisor_name]\n",
     "#plot_divisors = (61, 65, 111, 165, 1536, 12288) \n",
-    "plot_divisors = (55, 65, 165, 248, 3072)\n",
+    "#plot_divisors = (55, 65, 165, 248, 3072)\n",
     "L = len(plot_divisors)\n",
     "N = len(plot_mults)\n",
     "x = list(range(L))\n",
     "\n",
+    "colors = plt.get_cmap('tab20').colors +plt.get_cmap('tab20b').colors[:12]\n",
+    "\n",
     "fig = plt.figure(figsize=(L/4+15, 24))\n",
     "ax = plt.subplot(111)\n",
     "\n",
     "vals = np.zeros((N, L))\n",
     "n_samples = 0\n",
+    "groups = {}\n",
     "for i, mult in enumerate(plot_mults):\n",
+    "\n",
+    "    clear_mult = mult.with_error_model(None)\n",
     "    probmap = distributions_mults[mult]\n",
     "    y_values = [probmap[l] for l in plot_divisors]\n",
+    "    y_tup = tuple(y_values)\n",
+    "    group = groups.setdefault(y_tup, set())\n",
+    "    group.add(mult)\n",
     "    vals[i,] = y_values\n",
-    "    ax.plot(x, y_values,\n",
-    "            color=colors[mult],\n",
-    "            linestyle=styles[mult],\n",
-    "            marker=markers[mult],\n",
-    "            label=str(mult) if mult.countermeasure is None else \"_nolegend_\")\n",
+    "    offset = (i - N/2) * 0.0001\n",
+    "    ax.plot(x,[v + offset for v in y_values],\n",
+    "            color=colors[i],\n",
+    "            linestyle=styles[clear_mult],\n",
+    "            marker=markers[clear_mult],\n",
+    "            label=str(mult))\n",
     "    if showci:\n",
     "        cis = [conf_interval(p, probmap.samples) for p in y_values]\n",
     "        ci_low = [ci[0] for ci in cis]\n",
@@ -350,7 +347,7 @@
     "        ax.fill_between(x, ci_low, ci_high, color=\"black\", alpha=0.1)\n",
     "    n_samples += probmap.samples\n",
     "\n",
-    "ax.set_title(f\"{divisor_name} ({kind})\\nSamples(avg): \" + str(n_samples//N))\n",
+    "ax.set_title(f\"{divisor_name}\\nSamples(avg): \" + str(n_samples//N))\n",
     "\n",
     "#var = np.var(vals, axis=0)\n",
     "#ax.plot(x, var / np.max(var), label=\"cross-mult variance (normalized)\", ls=\"--\", lw=2, color=\"black\")\n",
@@ -369,14 +366,52 @@
     "ax.set_position([box.x0, box.y0, box.width * 0.7, box.height])\n",
     "\n",
     "# Put a legend to the right of the current axis\n",
-    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
-    "plt.show()"
+    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5));\n",
+    "for _, group in groups.items():\n",
+    "    print(group)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf6a5acb-2836-445b-a877-c29ed0f03bf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "divisor_name = \"all\"\n",
+    "plot_divisors = divisor_map[divisor_name]\n",
+    "ngroups = []\n",
+    "for mult in all_mults_with_ctr:\n",
+    "    groups = {}\n",
+    "    for error_model in all_error_models:\n",
+    "        full = mult.with_error_model(error_model)\n",
+    "        probmap = distributions_mults[full]\n",
+    "        y_values = [probmap[l] for l in plot_divisors]\n",
+    "        y_tup = tuple(y_values)\n",
+    "        group = groups.setdefault(y_tup, set())\n",
+    "        group.add(mult)\n",
+    "    ngroups.append(len(groups))\n",
+    "print(np.min(ngroups))\n",
+    "print(np.mean(ngroups))\n",
+    "print(np.median(ngroups))\n",
+    "print(np.max(ngroups))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c41722a-45b1-40f0-afc4-1aadc882fcc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.close(\"all\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d68f0bfc-cdf1-4891-b0e5-0b6d1b02ded7",
+   "id": "e7d7e614-467d-4e7d-874b-0495ef4dcf27",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -398,7 +433,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index e28315b..595b20a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,3 +8,4 @@ tabulate
 tqdm
 statsmodels
 anytree
+scikit-learn