nomos-pocs/cryptarchia/ghost-cryptarchia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad657d5a-bd36-4329-b134-6745daff7ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataclasses import dataclass\n",
    "from pyvis.network import Network\n",
    "from pyvis.options import Layout\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9e0b910-c633-4dbe-827c-4ddb804f7a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def phi(f, alpha):\n",
    "    return 1 - (1-f)**alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa0aadce-a0be-4873-ba23-293be74db313",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Block:\n",
    "    id: int\n",
    "    slot: int\n",
    "    height: int\n",
    "    parent: int\n",
    "    leader: int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a538cf45-d551-4603-b484-dbbc3f3d0a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class NetworkParams:\n",
    "    mixnet_delay_mean: int # seconds\n",
    "    mixnet_delay_var: int\n",
    "    broadcast_delay_mean: int # second\n",
    "    pol_proof_time: int # seconds\n",
    "    no_network_delay: bool = False\n",
    "\n",
    "    def sample_mixnet_delay(self):\n",
    "        scale = self.mixnet_delay_var / self.mixnet_delay_mean\n",
    "        shape = self.mixnet_delay_mean / scale\n",
    "        return np.random.gamma(shape=shape, scale=scale)\n",
    "    \n",
    "    def sample_broadcast_delay(self, blocks):\n",
    "        return np.random.exponential(self.broadcast_delay_mean, size=blocks.shape)\n",
    "\n",
    "    def block_arrival_slot(self, block_slot):\n",
    "        if self.no_network_delay:\n",
    "            return block_slot\n",
    "        return self.pol_proof_time + self.sample_mixnet_delay() + self.sample_broadcast_delay(block_slot) + block_slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24779de7-284f-4200-9e4a-d2aa6e1b823b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Params:\n",
    "    SLOTS: int\n",
    "    f: float\n",
    "    honest_stake: np.array\n",
    "    adversary_control: float\n",
    "\n",
    "    @property\n",
    "    def N(self):\n",
    "        return len(self.honest_stake) + 1\n",
    "\n",
    "    @property\n",
    "    def stake(self):\n",
    "        return np.append(self.honest_stake, self.honest_stake.sum() / (1/self.adversary_control - 1))\n",
    "    \n",
    "    @property\n",
    "    def relative_stake(self):\n",
    "        return self.stake / self.stake.sum()\n",
    "\n",
    "    def slot_prob(self):\n",
    "        return phi(self.f, self.relative_stake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "a90495a8-fcda-4e47-92b4-cc5ceaa9ff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sim:\n",
    "    def __init__(self, params: Params, network: NetworkParams):\n",
    "        self.params = params\n",
    "        self.network = network\n",
    "        self.leaders = np.zeros((params.N, params.SLOTS), dtype=np.int64)\n",
    "        self.blocks = []\n",
    "        self.block_slots = np.array([], dtype=np.int64)\n",
    "        self.block_heights = np.array([], dtype=np.int64)\n",
    "        self.block_arrivals = np.zeros(shape=(params.N, 0), dtype=np.int64) # arrival time to each leader for each block\n",
    "\n",
    "        # emit the genesis block\n",
    "        self.emit_block(\n",
    "            leader=0,\n",
    "            slot=0,\n",
    "            height=1,\n",
    "            parent=-1,\n",
    "        )\n",
    "        self.block_arrivals[:,:] = 0 # all nodes see the genesis block\n",
    "\n",
    "    def emit_block(self, leader, slot, height, parent):\n",
    "        assert type(leader) in [int, np.int64]\n",
    "        assert type(slot) in [int, np.int64]\n",
    "        assert type(height) in [int, np.int64]\n",
    "        assert type(parent) in [int, np.int64]\n",
    "\n",
    "        block = Block(\n",
    "            id=len(self.blocks),\n",
    "            slot=slot,\n",
    "            height=height,\n",
    "            parent=parent,\n",
    "            leader=leader,\n",
    "        )\n",
    "        self.blocks.append(block)\n",
    "        self.block_slots = np.append(self.block_slots, block.slot)\n",
    "        self.block_heights = np.append(self.block_heights, block.height)\n",
    "    \n",
    "        # decide when this block will arrive at each node\n",
    "        new_block_arrival_by_node = self.network.block_arrival_slot(np.repeat(block.slot, self.params.N))\n",
    "\n",
    "        if parent != -1:\n",
    "            # the new block cannot arrive before it's parent\n",
    "            parent_arrival_by_node = self.block_arrivals[:,parent]\n",
    "            new_block_arrival_by_node = np.maximum(new_block_arrival_by_node, parent_arrival_by_node)\n",
    "        \n",
    "        self.block_arrivals = np.append(self.block_arrivals, new_block_arrival_by_node.reshape((self.params.N, 1)), axis=1)\n",
    "        return block.id\n",
    "\n",
    "    def emit_leader_block(self, leader, slot):\n",
    "        assert type(leader) in [int, np.int64], type(leader)\n",
    "        assert isinstance(slot, int)\n",
    "\n",
    "        parent = self.fork_choice(leader, slot)\n",
    "\n",
    "        return self.emit_block(\n",
    "            leader,\n",
    "            slot,\n",
    "            height=self.blocks[parent].height + 1,\n",
    "            parent=parent,\n",
    "        )\n",
    "\n",
    "    def fork_choice(self, node, slot):\n",
    "        return self.honest_chain(node, slot)[-1]\n",
    "\n",
    "    def honest_chain(self, node, slot):\n",
    "        seen_blocks = self.block_arrivals[node,:] <= slot\n",
    "        block_ids = np.nonzero(seen_blocks)[0]\n",
    "\n",
    "        children = defaultdict(list)\n",
    "        for block in block_ids:\n",
    "            children[self.blocks[block].parent].append(block)\n",
    "\n",
    "        block_weight = self.block_weight(node, slot)\n",
    "\n",
    "        chain = [self.blocks[0].id]\n",
    "\n",
    "        while len(children[chain[-1]]) > 0:\n",
    "            next_block = max(children[chain[-1]], key=lambda c: block_weight[c])\n",
    "            chain.append(next_block)\n",
    "\n",
    "        return chain\n",
    "\n",
    "    def block_weight(self, node, slot, dbg=False):\n",
    "        seen_blocks = self.block_arrivals[node,:] <= slot\n",
    "        block_ids = np.nonzero(seen_blocks)[0]\n",
    "        \n",
    "        block_weight = {}\n",
    "\n",
    "        children = defaultdict(list)\n",
    "        if dbg:\n",
    "            print(\"seen\", seen_blocks)\n",
    "            print(\"block_ids\", block_ids)\n",
    "            \n",
    "        for b in sorted(block_ids, reverse=True):\n",
    "            if dbg:\n",
    "                print(f\"block={b} weights={block_weight} children={children}\")\n",
    "            weight = 1\n",
    "            for child in children[b]:\n",
    "                weight += block_weight[child]\n",
    "\n",
    "            block_weight[b] = weight\n",
    "            children[self.blocks[b].parent].append(b)\n",
    "            assert self.blocks[b].parent not in block_weight\n",
    "            # curr = b\n",
    "            # while self.blocks[curr].parent >= 0:\n",
    "            #     block_weight[self.blocks[curr].parent] += 1\n",
    "            #     curr = self.blocks[curr].parent\n",
    "\n",
    "        return block_weight\n",
    "        \n",
    "\n",
    "    def plot_spacetime_diagram(self, MAX_SLOT=1000):\n",
    "        alpha_index = sorted(range(self.params.N), key=lambda n: self.params.relative_stake[n])\n",
    "        nodes = [f\"$N_{n}$($\\\\alpha$={self.params.relative_stake[n]:.2f})\" for n in alpha_index]\n",
    "        messages = [(nodes[alpha_index.index(self.blocks[b].leader)], nodes[alpha_index.index(node)], self.blocks[b].slot, arrival_slot, f\"$B_{{{b}}}$\", b) for b, arrival_slots in enumerate(self.block_arrivals.T) for node, arrival_slot in enumerate(arrival_slots) if arrival_slot < MAX_SLOT]\n",
    "    \n",
    "        fig, ax = plt.subplots(figsize=(8,4))\n",
    "        \n",
    "        # Plot vertical lines for each node\n",
    "        max_slot = max(s for _,_,start_t, end_t,_,_ in messages for s in [start_t, end_t])\n",
    "        for i, node in enumerate(nodes):\n",
    "            ax.plot([i, i], [0, max_slot], 'k-', linewidth=0.1)\n",
    "            ax.text(i, max_slot + 30 * (0 if i % 2 == 0 else 1), node, ha='center', va='bottom')\n",
    "        \n",
    "        # Plot messages\n",
    "        colors = plt.cm.rainbow(np.linspace(0, 1, len(messages)))\n",
    "        for (start, end, start_time, end_time, label, b), color in zip(messages, colors):\n",
    "            start_idx = nodes.index(start)\n",
    "            end_idx = nodes.index(end)\n",
    "            ax.annotate('', xy=(end_idx, end_time), xytext=(start_idx, start_time),\n",
    "                        arrowprops=dict(arrowstyle='->', color=\"grey\", lw=0.1))\n",
    "            placement = 0\n",
    "            mid_x = start_idx * (1 - placement) + end_idx * placement\n",
    "            mid_y = start_time * (1 - placement) + end_time * placement\n",
    "            ax.text(mid_x, mid_y, label, ha='center', va='center', \n",
    "                    bbox=dict(facecolor='white', edgecolor='none', alpha=0.7))\n",
    "\n",
    "            # # draw parent pointers\n",
    "\n",
    "            # block = self.blocks[b]\n",
    "            # parent = self.blocks[block.parent]\n",
    "            # parent_t = parent.slot\n",
    "            # parent_idx = alpha_index.index(parent.leader)\n",
    "        \n",
    "            # ax.annotate('', xy=(parent_idx, parent_t), xytext=(end_idx, end_time),\n",
    "            #             arrowprops=dict(arrowstyle='->', color=\"black\", lw=2))\n",
    "\n",
    "        for block in self.blocks:\n",
    "            if block.parent == -1:\n",
    "                continue\n",
    "\n",
    "            parent = self.blocks[block.parent]\n",
    "            parent_t = parent.slot\n",
    "            parent_idx = alpha_index.index(parent.leader)\n",
    "\n",
    "            child_t = block.slot\n",
    "            child_idx = alpha_index.index(block.leader)\n",
    "        \n",
    "            ax.annotate('', xy=(parent_idx, parent_t), xytext=(child_idx, child_t),\n",
    "                        arrowprops=dict(arrowstyle='-', color=\"black\", lw=2))\n",
    "            \n",
    "        \n",
    "        ax.set_xlim(-1, len(nodes))\n",
    "        ax.set_ylim(0, max_slot + 70)\n",
    "        ax.set_xticks(range(len(nodes)))\n",
    "        ax.set_xticklabels([])\n",
    "        # ax.set_yticks([])\n",
    "        ax.set_title('Space-Time Diagram')\n",
    "        ax.set_ylabel('Slot')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    def visualize_chain(self):\n",
    "        honest_chain = self.honest_chain(0, self.block_arrivals.max())\n",
    "        print(\"Honest chain length\", len(honest_chain))\n",
    "        honest_chain_set = set(honest_chain)\n",
    "        \n",
    "        layout = Layout()\n",
    "        layout.hierachical = True\n",
    "        \n",
    "        G = Network(width=1600, height=800, notebook=True, directed=True, layout=layout, cdn_resources='in_line')\n",
    "\n",
    "        for block in self.blocks:\n",
    "            # level = slot\n",
    "            level = block.height\n",
    "            color = \"lightgrey\"\n",
    "            if block.id in honest_chain_set:\n",
    "                color = \"orange\"\n",
    "\n",
    "            G.add_node(int(block.id), level=level, color=color, label=f\"{block.id}:s={block.slot},h={block.height}\")\n",
    "            if block.parent >= 0:\n",
    "                G.add_edge(int(block.id), int(block.parent), width=1, color=color)\n",
    "\n",
    "        \n",
    "        return G.show(\"chain.html\")\n",
    "\n",
    "    def run(self, seed=None):\n",
    "        if seed is not None:\n",
    "            np.random.seed(seed)\n",
    "        \n",
    "        for s in range(1, self.params.SLOTS):\n",
    "            if s > 0 and s % 100000 == 0:\n",
    "                print(f\"SIM={s}/{self.params.SLOTS}, blocks={len(self.blocks)}\")\n",
    "    \n",
    "            # the adversary will not participate in the simulation\n",
    "            # (implemented by never delivering blocks to the adversary)\n",
    "            self.block_arrivals[-1,:] = self.params.SLOTS\n",
    "\n",
    "            self.leaders[:,s] = np.random.random(size=self.params.N) < self.params.slot_prob()\n",
    "            for leader in np.nonzero(self.leaders[:,s])[0]:\n",
    "                if self.params.adversary_control is not None and leader == self.params.N - 1:\n",
    "                    continue\n",
    "                self.emit_leader_block(\n",
    "                    leader,\n",
    "                    s,\n",
    "                )\n",
    "    \n",
    "    def adverserial_analysis(self, should_plot=True, seed=0):\n",
    "        np.random.seed(seed)\n",
    "\n",
    "        adversary = self.params.N-1 # adversary is always the last node in our simulations\n",
    "\n",
    "        self.block_arrivals[adversary,:] = self.block_slots # we will say the adversary receives the blocks immidiately\n",
    "\n",
    "        honest_chain = self.honest_chain(adversary, slot=self.params.SLOTS)\n",
    "        honest_weight_by_slot = np.zeros(self.params.SLOTS, dtype=np.int64)\n",
    "        honest_height_by_slot = np.zeros(self.params.SLOTS, dtype=np.int64)\n",
    "        for b in honest_chain:\n",
    "            temp_weight = np.zeros(self.params.SLOTS, dtype=np.int64) + self.block_weight(adversary, self.blocks[b].slot)[0]\n",
    "            temp_weight[:self.blocks[b].slot] = 0\n",
    "            honest_weight_by_slot = np.maximum(temp_weight, honest_weight_by_slot)\n",
    "\n",
    "            temp_height = np.zeros(self.params.SLOTS, dtype=np.int64) + self.blocks[b].height\n",
    "            temp_height[:self.blocks[b].slot] = 0\n",
    "            honest_height_by_slot = np.maximum(temp_height, honest_height_by_slot)\n",
    "\n",
    "        reorg_hist = np.zeros(self.params.SLOTS, dtype=np.int64)\n",
    "        reorg_depths = np.array([], dtype=np.int64)\n",
    "\n",
    "        if should_plot:\n",
    "            plt.figure(figsize=(20, 6))\n",
    "            ax = plt.subplot(121)\n",
    "        \n",
    "        adversary_active_slots = np.random.random(size=self.params.SLOTS) < phi(self.params.f, self.params.relative_stake[adversary])\n",
    "        all_active_slots = (self.leaders.sum(axis=0) + adversary_active_slots) > 0\n",
    "        \n",
    "        for block in self.blocks:\n",
    "            if block.id > 0 and block.id % 1000 == 0:\n",
    "                print(\"Processing block\", block)\n",
    "\n",
    "            # honest_chain = self.honest_chain(adversary, slot=block.slot)\n",
    "\n",
    "            nearest_honest_block = block\n",
    "            while nearest_honest_block.height > len(honest_chain) or honest_chain[nearest_honest_block.height-1] != nearest_honest_block.id:\n",
    "                assert nearest_honest_block.parent != -1\n",
    "                nearest_honest_block = self.blocks[nearest_honest_block.parent]\n",
    "\n",
    "            adv_init_fork_weight = self.block_weight(adversary, block.slot)[0]\n",
    "\n",
    "            cumulative_rel_height = adversary_active_slots[block.slot+1:].cumsum()\n",
    "            adverserial_weight_by_slot = adv_init_fork_weight + cumulative_rel_height\n",
    "        \n",
    "            adverserial_wins = adverserial_weight_by_slot > honest_weight_by_slot[block.slot + 1:]\n",
    "            \n",
    "            reorg_events = adverserial_wins & all_active_slots[block.slot+1:]\n",
    "            \n",
    "            reorg_depths = np.append(reorg_depths, honest_height_by_slot[block.slot+1:][reorg_events] - nearest_honest_block.height)\n",
    "            reorg_hist += np.append(np.zeros(block.slot, dtype=np.int64), adverserial_wins).sum(axis=0)\n",
    "\n",
    "            if should_plot:\n",
    "                if reorg_events.sum() > 0:\n",
    "                    first_slot = block.slot+1\n",
    "                    last_slot = first_slot + np.nonzero(reorg_events)[0].max() + 1\n",
    "\n",
    "                    ax.plot(np.arange(first_slot, last_slot), adverserial_weight_by_slot[:last_slot-first_slot]-honest_weight_by_slot[first_slot:last_slot], lw=\"1\")\n",
    "                    for event in np.nonzero(reorg_events)[0]:\n",
    "                        plt.axvline(x = event + block.slot + 1, ymin = 0, ymax = 1, color ='red', lw=0.01)\n",
    "\n",
    "        if should_plot:\n",
    "            ax.plot(np.zeros(self.params.SLOTS), color=\"k\", label=f\"honest chain\")\n",
    "            _ = ax.set_title(f\"max chain weight with adversery controlling {self.params.relative_stake[adversary] * 100:.0f}% of stake\")\n",
    "            _ = ax.set_ylabel(\"weight advantage\")\n",
    "            _ = ax.set_xlabel(\"slot\")\n",
    "            _ = ax.legend()\n",
    "\n",
    "            ax = plt.subplot(122)\n",
    "            _ = ax.grid(True)\n",
    "            bins = (reorg_depths.max() if reorg_depths.sum() > 0 else 0) + 1\n",
    "            _ = ax.hist(reorg_depths, density=False, bins=100)\n",
    "            _ = ax.set_title(f\"re-org depth with {self.params.relative_stake[adversary] * 100:.0f}% adversary\")\n",
    "            _ = ax.set_xlabel(\"re-org depth\")\n",
    "            _ = ax.set_ylabel(\"frequency\")\n",
    "\n",
    "        return reorg_depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "6625ba1b-0039-4dcc-a1ec-eea47ce7e476",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg blocks per slot 0.04709\n",
      "Number of blocks 4709\n",
      "longest chain 2346\n",
      "CPU times: user 16.5 s, sys: 7.51 s, total: 24 s\n",
      "Wall time: 24.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "np.random.seed(0)\n",
    "sim = Sim(\n",
    "    params=Params(\n",
    "        SLOTS=100000,\n",
    "        f=0.05,\n",
    "        adversary_control = 0.1,\n",
    "        honest_stake = np.random.pareto(10, 1000)\n",
    "    ),\n",
    "    network=NetworkParams(\n",
    "        mixnet_delay_mean=10, # seconds\n",
    "        mixnet_delay_var=4,\n",
    "        broadcast_delay_mean=2, # second\n",
    "        pol_proof_time=10, # seconds\n",
    "        no_network_delay=False\n",
    "    )\n",
    ")\n",
    "sim.run(seed=5)\n",
    "\n",
    "n_blocks_per_slot = len(sim.blocks) / sim.params.SLOTS\n",
    "print(\"avg blocks per slot\", n_blocks_per_slot)\n",
    "print(\"Number of blocks\", len(sim.blocks))\n",
    "print(\"longest chain\", max(b.height for b in sim.blocks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "aabccc4e-8f47-403e-b7f9-7508e93ec18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim.visualize_chain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "51c90b03-336f-4108-8560-27bd4465a5bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing block Block(id=1000, slot=21253, height=495, parent=998, leader=935)\n",
      "Processing block Block(id=2000, slot=42906, height=991, parent=1999, leader=589)\n",
      "Processing block Block(id=3000, slot=64559, height=1499, parent=2998, leader=781)\n",
      "Processing block Block(id=4000, slot=85787, height=1996, parent=3998, leader=421)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reorgs = sim.adverserial_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "67b6b368-9203-4af6-bab3-dfbc2ef4fb1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.71% of slots were reorged with depth >= 0\n",
      "2.04% of slots were reorged with depth >= 1\n",
      "0.22% of slots were reorged with depth >= 2\n",
      "0.04% of slots were reorged with depth >= 3\n",
      "0.01% of slots were reorged with depth >= 4\n",
      "0.00% of slots were reorged with depth >= 5\n",
      "0.00% of slots were reorged with depth >= 6\n",
      "0.00% of slots were reorged with depth >= 7\n",
      "0.00% of slots were reorged with depth >= 8\n",
      "0.00% of slots were reorged with depth >= 9\n",
      "0.00% of slots were reorged with depth >= 10\n",
      "0.00% of slots were reorged with depth >= 11\n",
      "0.00% of slots were reorged with depth >= 12\n",
      "0.00% of slots were reorged with depth >= 13\n",
      "0.00% of slots were reorged with depth >= 14\n",
      "0.00% of slots were reorged with depth >= 15\n",
      "0.00% of slots were reorged with depth >= 16\n",
      "0.00% of slots were reorged with depth >= 17\n",
      "0.00% of slots were reorged with depth >= 18\n",
      "0.00% of slots were reorged with depth >= 19\n"
     ]
    }
   ],
   "source": [
    "for DEPTH in range(20):\n",
    "    print(f\"{len(reorgs[reorgs >= DEPTH]) / sim.params.SLOTS*100:.2f}% of slots were reorged with depth >= {DEPTH}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "e29f1bfb-042d-4ffa-9981-b6a5dfc88557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/10\n",
      "simulating 2/10\n",
      "simulating 3/10\n",
      "simulating 4/10\n",
      "simulating 5/10\n",
      "simulating 6/10\n",
      "simulating 7/10\n",
      "simulating 8/10\n",
      "simulating 9/10\n",
      "simulating 10/10\n",
      "finished simulation, starting analysis\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "stake = np.random.pareto(10, 100)\n",
    "\n",
    "sims = [Sim(\n",
    "    params=Params(\n",
    "        SLOTS=10000,\n",
    "        f=0.05,\n",
    "        adversary_control = i,\n",
    "        honest_stake = stake\n",
    "    ),\n",
    "    network=NetworkParams(\n",
    "        mixnet_delay_mean=10, # seconds\n",
    "        mixnet_delay_var=4,\n",
    "        broadcast_delay_mean=2, # second\n",
    "        pol_proof_time=10, # seconds\n",
    "        no_network_delay=False\n",
    "    )\n",
    ") for i in np.linspace(1e-3, 0.3, 10)]\n",
    "\n",
    "for i, sim in enumerate(sims):\n",
    "    print(f\"simulating {i+1}/{len(sims)}\")\n",
    "    sim.run(seed=0)\n",
    "\n",
    "print(\"finished simulation, starting analysis\")\n",
    "advs = [sim.adverserial_analysis(should_plot=False) for sim in sims]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "dd417361-b315-4769-9c24-221e231c2458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_reorg_depth = max(a.max() if len(a) > 0 else 0 for a in advs)\n",
    "\n",
    "\n",
    "heatmap = np.zeros((len(advs), max_reorg_depth), dtype=np.int64)\n",
    "\n",
    "for i,  adv in enumerate(advs):\n",
    "    for depth in range(max_reorg_depth):\n",
    "        heatmap[i][depth] = (adv == depth).sum()\n",
    "\n",
    "plt.figure(figsize=(40,40))\n",
    "ax = plt.subplot(121)\n",
    "im = ax.imshow(heatmap)\n",
    "\n",
    "_ = ax.set_yticks(np.arange(len(sims)), labels=[f\"{s.params.adversary_control:.2f}\" if i % 2 == (len(sims) - 1) % 2 else None for i, s in enumerate(sims)])\n",
    "_ = ax.set_xticks(np.arange(max_reorg_depth), labels=[r if r % (max_reorg_depth // 10) == 0 else None for r in range(max_reorg_depth)])\n",
    "_ = ax.set_xlabel(\"reorg depth\")\n",
    "_ = ax.set_ylabel(\"adversary stake\")\n",
    "\n",
    "ax = plt.subplot(1,10,6)\n",
    "scale = heatmap.max()\n",
    "ax.imshow(np.arange(scale+1).reshape((1, scale+1)).T, extent=(1,0,1,0))\n",
    "_ = ax.set_yticks(np.arange(scale+1) / scale, labels = [r if r % (scale // 10) == 0 else None for r in range(scale+1)])\n",
    "_ = ax.set_xticks([], minor=False)\n",
    "_ = ax.set_ylabel(\"frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "6a232e82-2b79-4924-b6fe-b56564c797ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/5\n",
      "SIM=100000/200000, blocks=4637\n",
      "simulating 2/5\n",
      "SIM=100000/200000, blocks=4637\n",
      "simulating 3/5\n",
      "SIM=100000/200000, blocks=4637\n",
      "simulating 4/5\n",
      "SIM=100000/200000, blocks=4637\n",
      "simulating 5/5\n",
      "SIM=100000/200000, blocks=4637\n",
      "finished simulation, starting analysis\n",
      "Processing block Block(id=1000, slot=21112, height=947, parent=999, leader=70)\n",
      "Processing block Block(id=2000, slot=43487, height=1905, parent=1999, leader=27)\n",
      "Processing block Block(id=3000, slot=64768, height=2860, parent=2998, leader=74)\n",
      "Processing block Block(id=4000, slot=86152, height=3827, parent=3999, leader=4)\n",
      "Processing block Block(id=5000, slot=107463, height=4788, parent=4999, leader=53)\n",
      "Processing block Block(id=6000, slot=129674, height=5754, parent=5999, leader=8)\n",
      "Processing block Block(id=7000, slot=150939, height=6696, parent=6999, leader=57)\n",
      "Processing block Block(id=8000, slot=172707, height=7644, parent=7999, leader=27)\n",
      "Processing block Block(id=9000, slot=194724, height=8608, parent=8999, leader=66)\n",
      "Processing block Block(id=1000, slot=21112, height=719, parent=999, leader=70)\n",
      "Processing block Block(id=2000, slot=43487, height=1446, parent=1998, leader=27)\n",
      "Processing block Block(id=3000, slot=64768, height=2144, parent=2998, leader=74)\n",
      "Processing block Block(id=4000, slot=86152, height=2881, parent=3999, leader=4)\n",
      "Processing block Block(id=5000, slot=107463, height=3606, parent=4996, leader=53)\n",
      "Processing block Block(id=6000, slot=129674, height=4340, parent=5999, leader=8)\n",
      "Processing block Block(id=7000, slot=150939, height=5062, parent=6999, leader=57)\n",
      "Processing block Block(id=8000, slot=172707, height=5772, parent=7999, leader=27)\n",
      "Processing block Block(id=9000, slot=194724, height=6502, parent=8999, leader=66)\n",
      "Processing block Block(id=1000, slot=21112, height=579, parent=999, leader=70)\n",
      "Processing block Block(id=2000, slot=43487, height=1152, parent=1999, leader=27)\n",
      "Processing block Block(id=3000, slot=64768, height=1727, parent=2997, leader=74)\n",
      "Processing block Block(id=4000, slot=86152, height=2323, parent=3998, leader=4)\n",
      "Processing block Block(id=5000, slot=107463, height=2906, parent=4996, leader=53)\n",
      "Processing block Block(id=6000, slot=129674, height=3505, parent=5999, leader=8)\n",
      "Processing block Block(id=7000, slot=150939, height=4097, parent=6999, leader=57)\n",
      "Processing block Block(id=8000, slot=172707, height=4679, parent=7997, leader=27)\n",
      "Processing block Block(id=9000, slot=194724, height=5259, parent=8998, leader=66)\n",
      "Processing block Block(id=1000, slot=21112, height=471, parent=999, leader=70)\n",
      "Processing block Block(id=2000, slot=43487, height=947, parent=1995, leader=27)\n",
      "Processing block Block(id=3000, slot=64768, height=1423, parent=2997, leader=74)\n",
      "Processing block Block(id=4000, slot=86152, height=1931, parent=3998, leader=4)\n",
      "Processing block Block(id=5000, slot=107463, height=2423, parent=4996, leader=53)\n",
      "Processing block Block(id=6000, slot=129674, height=2922, parent=5997, leader=8)\n",
      "Processing block Block(id=7000, slot=150939, height=3412, parent=6994, leader=57)\n",
      "Processing block Block(id=8000, slot=172707, height=3898, parent=7997, leader=27)\n",
      "Processing block Block(id=9000, slot=194724, height=4399, parent=8998, leader=66)\n",
      "Processing block Block(id=1000, slot=21112, height=418, parent=999, leader=70)\n",
      "Processing block Block(id=2000, slot=43487, height=834, parent=1996, leader=27)\n",
      "Processing block Block(id=3000, slot=64768, height=1249, parent=2997, leader=74)\n",
      "Processing block Block(id=4000, slot=86152, height=1671, parent=3995, leader=4)\n",
      "Processing block Block(id=5000, slot=107463, height=2093, parent=4997, leader=53)\n",
      "Processing block Block(id=6000, slot=129674, height=2532, parent=5997, leader=8)\n",
      "Processing block Block(id=7000, slot=150939, height=2944, parent=6995, leader=57)\n",
      "Processing block Block(id=8000, slot=172707, height=3372, parent=7997, leader=27)\n",
      "Processing block Block(id=9000, slot=194724, height=3804, parent=8998, leader=66)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "stake = np.random.pareto(10, 100)\n",
    "\n",
    "sims = [Sim(\n",
    "    params=Params(\n",
    "        SLOTS=200000,\n",
    "        f=0.05,\n",
    "        adversary_control = 0.1,\n",
    "        honest_stake = stake\n",
    "    ),\n",
    "    network=NetworkParams(\n",
    "        mixnet_delay_mean=i, # seconds\n",
    "        mixnet_delay_var=(i / 5)**2,\n",
    "        broadcast_delay_mean=1e-6, # second\n",
    "        pol_proof_time=0, # seconds\n",
    "        no_network_delay=False\n",
    "    )\n",
    ") for i in np.linspace(1, 30, 5)]\n",
    "\n",
    "\n",
    "for i, sim in enumerate(sims):\n",
    "    print(f\"simulating {i+1}/{len(sims)}\")\n",
    "    sim.run(seed=0)\n",
    "\n",
    "print(\"finished simulation, starting analysis\")\n",
    "advs = [sim.adverserial_analysis(should_plot=False) for sim in sims]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "b49ce221-5754-4982-a2c1-9fc61e23d0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in range(len(sims)):\n",
    "    max_depth = advs[s].max()\n",
    "    count_by_depth = np.zeros(max_depth)\n",
    "    for d in range(max_depth):\n",
    "        count_by_depth[d] = (advs[s] == d).sum() / (sims[s].params.SLOTS * sims[s].params.f)\n",
    "    plt.plot(np.arange(max_depth), count_by_depth, label=f\"E[mixnet_delay]={sims[s].network.mixnet_delay_mean:.1f}s\")\n",
    "\n",
    "_ = plt.title(f\"reorg depth sensitivity to mixnet delay @ {1/sims[s].params.f:.0f}s block time\")\n",
    "_ = plt.xlabel(\"reorg depth\")\n",
    "_ = plt.ylabel(\"frequency\")\n",
    "_ = plt.legend()\n",
    "_ = plt.yscale(\"log\")\n",
    "_ = plt.xlim(0, 25)\n",
    "_ = plt.ylim(10**-3,10**0)\n",
    "# _ = plt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "518f75ce-58e1-466c-87b5-57bb59c6dd99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/5\n",
      "SIM=100000/600000, blocks=122\n",
      "SIM=200000/600000, blocks=225\n",
      "SIM=300000/600000, blocks=326\n",
      "SIM=400000/600000, blocks=442\n",
      "SIM=500000/600000, blocks=559\n",
      "simulating 2/5\n",
      "simulating 3/5\n",
      "simulating 4/5\n",
      "simulating 5/5\n",
      "finished simulation, starting analysis\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "stake = np.random.pareto(10, 100)\n",
    "\n",
    "sims = [Sim(\n",
    "    params=Params(\n",
    "        SLOTS=int(1000 / i),\n",
    "        f=i,\n",
    "        adversary_control = 0.3,\n",
    "        honest_stake = stake\n",
    "    ),\n",
    "    network=NetworkParams(\n",
    "        mixnet_delay_mean=10, # seconds\n",
    "        mixnet_delay_var=4,\n",
    "        broadcast_delay_mean=2, # second\n",
    "        pol_proof_time=2, # seconds\n",
    "        no_network_delay=False\n",
    "    )\n",
    ") for i in np.linspace(1 / 600, 0.05, 5)]\n",
    "\n",
    "\n",
    "for i, sim in enumerate(sims):\n",
    "    print(f\"simulating {i+1}/{len(sims)}\")\n",
    "    sim.run(seed=0)\n",
    "\n",
    "print(\"finished simulation, starting analysis\")\n",
    "advs = [sim.adverserial_analysis(should_plot=False) for sim in sims]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "070d2a09-a06e-4998-bd67-df3d80c2d482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in range(len(sims)):\n",
    "    max_depth = advs[s].max()\n",
    "    count_by_depth = np.zeros(max_depth)\n",
    "    for d in range(max_depth):\n",
    "        count_by_depth[d] = (advs[s] == d).sum() / (sims[s].params.SLOTS * sims[s].params.f)\n",
    "    plt.plot(np.arange(max_depth), count_by_depth, label=f\"E[block_time]={1 / sims[s].params.f:.0f}s\")\n",
    "\n",
    "_ = plt.title(\"reorg depth sensitivity to block time\")\n",
    "_ = plt.xlabel(\"reorg depth\")\n",
    "_ = plt.ylabel(\"frequency (per block)\")\n",
    "_ = plt.legend()\n",
    "_ = plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "e32a48f7-17c8-47f9-8177-e8ce2117e630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/4\n",
      "SIM=100000/150000, blocks=15539\n",
      "simulating 2/4\n",
      "SIM=100000/300000, blocks=7454\n",
      "SIM=200000/300000, blocks=14913\n",
      "simulating 3/4\n",
      "SIM=100000/600000, blocks=3648\n",
      "SIM=200000/600000, blocks=7251\n",
      "SIM=300000/600000, blocks=10815\n",
      "SIM=400000/600000, blocks=14462\n",
      "SIM=500000/600000, blocks=18038\n",
      "simulating 4/4\n",
      "SIM=100000/900000, blocks=2368\n",
      "SIM=200000/900000, blocks=4745\n",
      "SIM=300000/900000, blocks=7051\n",
      "SIM=400000/900000, blocks=9482\n",
      "SIM=500000/900000, blocks=11815\n",
      "SIM=600000/900000, blocks=14139\n",
      "SIM=700000/900000, blocks=16637\n",
      "SIM=800000/900000, blocks=19004\n",
      "finished simulation, starting analysis\n",
      "Processing block Block(id=1000, slot=6647, height=331, parent=993, leader=23)\n",
      "Processing block Block(id=2000, slot=12979, height=654, parent=1996, leader=72)\n",
      "Processing block Block(id=3000, slot=19147, height=970, parent=2996, leader=70)\n",
      "Processing block Block(id=4000, slot=25849, height=1301, parent=3994, leader=7)\n",
      "Processing block Block(id=5000, slot=32235, height=1620, parent=4995, leader=57)\n",
      "Processing block Block(id=6000, slot=38680, height=1938, parent=5996, leader=23)\n",
      "Processing block Block(id=7000, slot=45085, height=2265, parent=6997, leader=20)\n",
      "Processing block Block(id=8000, slot=51497, height=2591, parent=7999, leader=62)\n",
      "Processing block Block(id=9000, slot=57923, height=2912, parent=8997, leader=13)\n",
      "Processing block Block(id=10000, slot=64359, height=3240, parent=9995, leader=8)\n",
      "Processing block Block(id=11000, slot=70846, height=3554, parent=10996, leader=0)\n",
      "Processing block Block(id=12000, slot=77247, height=3868, parent=11997, leader=74)\n",
      "Processing block Block(id=13000, slot=83581, height=4187, parent=12996, leader=49)\n",
      "Processing block Block(id=14000, slot=90342, height=4523, parent=13997, leader=23)\n",
      "Processing block Block(id=15000, slot=96605, height=4847, parent=14995, leader=68)\n",
      "Processing block Block(id=16000, slot=103137, height=5175, parent=15994, leader=35)\n",
      "Processing block Block(id=17000, slot=109408, height=5485, parent=16996, leader=21)\n",
      "Processing block Block(id=18000, slot=116148, height=5814, parent=17994, leader=1)\n",
      "Processing block Block(id=19000, slot=122629, height=6133, parent=18996, leader=44)\n",
      "Processing block Block(id=20000, slot=128649, height=6436, parent=19992, leader=72)\n",
      "Processing block Block(id=21000, slot=134981, height=6766, parent=20992, leader=91)\n",
      "Processing block Block(id=22000, slot=141443, height=7084, parent=21993, leader=72)\n",
      "Processing block Block(id=23000, slot=147541, height=7391, parent=22996, leader=70)\n",
      "Processing block Block(id=1000, slot=13696, height=504, parent=998, leader=19)\n",
      "Processing block Block(id=2000, slot=27252, height=994, parent=1996, leader=40)\n",
      "Processing block Block(id=3000, slot=40875, height=1486, parent=2995, leader=17)\n",
      "Processing block Block(id=4000, slot=53942, height=1974, parent=3997, leader=52)\n",
      "Processing block Block(id=5000, slot=66966, height=2468, parent=4997, leader=52)\n",
      "Processing block Block(id=6000, slot=80689, height=2983, parent=5998, leader=52)\n",
      "Processing block Block(id=7000, slot=94116, height=3481, parent=6998, leader=2)\n",
      "Processing block Block(id=8000, slot=107469, height=3988, parent=7999, leader=54)\n",
      "Processing block Block(id=9000, slot=120483, height=4498, parent=8997, leader=10)\n",
      "Processing block Block(id=10000, slot=134285, height=5011, parent=9998, leader=13)\n",
      "Processing block Block(id=11000, slot=147596, height=5514, parent=10998, leader=28)\n",
      "Processing block Block(id=12000, slot=160875, height=6009, parent=11997, leader=45)\n",
      "Processing block Block(id=13000, slot=174501, height=6513, parent=12998, leader=38)\n",
      "Processing block Block(id=14000, slot=187371, height=6986, parent=13996, leader=37)\n",
      "Processing block Block(id=15000, slot=201237, height=7499, parent=14997, leader=38)\n",
      "Processing block Block(id=16000, slot=214376, height=7992, parent=15995, leader=19)\n",
      "Processing block Block(id=17000, slot=227251, height=8483, parent=16996, leader=83)\n",
      "Processing block Block(id=18000, slot=240344, height=8983, parent=17995, leader=28)\n",
      "Processing block Block(id=19000, slot=254516, height=9501, parent=18999, leader=65)\n",
      "Processing block Block(id=20000, slot=268027, height=10003, parent=19998, leader=72)\n",
      "Processing block Block(id=21000, slot=281073, height=10489, parent=20998, leader=62)\n",
      "Processing block Block(id=22000, slot=294487, height=10990, parent=21998, leader=89)\n",
      "Processing block Block(id=1000, slot=28018, height=678, parent=999, leader=13)\n",
      "Processing block Block(id=2000, slot=54681, height=1338, parent=1998, leader=37)\n",
      "Processing block Block(id=3000, slot=81956, height=2003, parent=2998, leader=2)\n",
      "Processing block Block(id=4000, slot=108982, height=2667, parent=3994, leader=68)\n",
      "Processing block Block(id=5000, slot=136909, height=3341, parent=4999, leader=18)\n",
      "Processing block Block(id=6000, slot=163753, height=4012, parent=5997, leader=13)\n",
      "Processing block Block(id=7000, slot=192695, height=4696, parent=6999, leader=5)\n",
      "Processing block Block(id=8000, slot=221475, height=5366, parent=7998, leader=6)\n",
      "Processing block Block(id=9000, slot=248974, height=6027, parent=8998, leader=17)\n",
      "Processing block Block(id=10000, slot=277968, height=6712, parent=9998, leader=42)\n",
      "Processing block Block(id=11000, slot=305410, height=7370, parent=10999, leader=18)\n",
      "Processing block Block(id=12000, slot=332514, height=8047, parent=11996, leader=13)\n",
      "Processing block Block(id=13000, slot=359107, height=8703, parent=12997, leader=93)\n",
      "Processing block Block(id=14000, slot=387380, height=9362, parent=13997, leader=28)\n",
      "Processing block Block(id=15000, slot=414889, height=10019, parent=14997, leader=5)\n",
      "Processing block Block(id=16000, slot=442318, height=10689, parent=15999, leader=31)\n",
      "Processing block Block(id=17000, slot=470263, height=11361, parent=16999, leader=0)\n",
      "Processing block Block(id=18000, slot=499107, height=12050, parent=17997, leader=56)\n",
      "Processing block Block(id=19000, slot=526745, height=12716, parent=18999, leader=44)\n",
      "Processing block Block(id=20000, slot=555994, height=13405, parent=19998, leader=60)\n",
      "Processing block Block(id=21000, slot=583060, height=14052, parent=20999, leader=48)\n",
      "Processing block Block(id=1000, slot=43147, height=756, parent=999, leader=52)\n",
      "Processing block Block(id=2000, slot=84935, height=1523, parent=1998, leader=58)\n",
      "Processing block Block(id=3000, slot=127256, height=2278, parent=2998, leader=13)\n",
      "Processing block Block(id=4000, slot=168543, height=3021, parent=3998, leader=17)\n",
      "Processing block Block(id=5000, slot=211612, height=3780, parent=4998, leader=40)\n",
      "Processing block Block(id=6000, slot=254555, height=4514, parent=5999, leader=37)\n",
      "Processing block Block(id=7000, slot=297433, height=5284, parent=6999, leader=36)\n",
      "Processing block Block(id=8000, slot=337608, height=6021, parent=7999, leader=66)\n",
      "Processing block Block(id=9000, slot=380081, height=6780, parent=8999, leader=37)\n",
      "Processing block Block(id=10000, slot=421828, height=7524, parent=9998, leader=10)\n",
      "Processing block Block(id=11000, slot=463502, height=8278, parent=10998, leader=52)\n",
      "Processing block Block(id=12000, slot=508372, height=9044, parent=11999, leader=36)\n",
      "Processing block Block(id=13000, slot=551404, height=9811, parent=12998, leader=41)\n",
      "Processing block Block(id=14000, slot=593238, height=10574, parent=13998, leader=83)\n",
      "Processing block Block(id=15000, slot=633488, height=11327, parent=14999, leader=27)\n",
      "Processing block Block(id=16000, slot=674922, height=12060, parent=15997, leader=51)\n",
      "Processing block Block(id=17000, slot=714860, height=12815, parent=16999, leader=89)\n",
      "Processing block Block(id=18000, slot=758063, height=13567, parent=17999, leader=70)\n",
      "Processing block Block(id=19000, slot=799852, height=14311, parent=18999, leader=59)\n",
      "Processing block Block(id=20000, slot=841279, height=15062, parent=19999, leader=20)\n",
      "Processing block Block(id=21000, slot=881497, height=15807, parent=20999, leader=7)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "stake = np.random.pareto(10, 100)\n",
    "\n",
    "mixnet_delay_mean = 10\n",
    "\n",
    "sims = [Sim(\n",
    "    params=Params(\n",
    "        SLOTS=int(30000 * 1 / (1/mixnet_delay_mean / i)),\n",
    "        f=1/mixnet_delay_mean / i,\n",
    "        adversary_control = 0.3,\n",
    "        honest_stake = stake\n",
    "    ),\n",
    "    network=NetworkParams(\n",
    "        mixnet_delay_mean=mixnet_delay_mean, # seconds\n",
    "        mixnet_delay_var=4,\n",
    "        broadcast_delay_mean=2, # second\n",
    "        pol_proof_time=2, # seconds\n",
    "        no_network_delay=False\n",
    "    )\n",
    ") for i in [1/2, 1, 2, 3]]\n",
    "\n",
    "\n",
    "for i, sim in enumerate(sims):\n",
    "    print(f\"simulating {i+1}/{len(sims)}\")\n",
    "    sim.run(seed=0)\n",
    "\n",
    "print(\"finished simulation, starting analysis\")\n",
    "advs = [sim.adverserial_analysis(should_plot=False) for sim in sims]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "8c716377-7a62-4b2c-9001-910fc07cbb1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAX=30\n",
    "\n",
    "for s in range(len(sims)):\n",
    "    \n",
    "    max_depth = advs[s].max() if advs[s].sum() > 0 else 0\n",
    "    max_depth = min(MAX, max_depth)\n",
    "    count_by_depth = np.zeros(max_depth)\n",
    "    for d in range(max_depth):\n",
    "        count_by_depth[d] = (advs[s] == d).sum() \n",
    "    block_time = 1 / sims[s].params.f\n",
    "    expected_blocks = sims[s].params.SLOTS / block_time\n",
    "    plt.plot(np.arange(max_depth), count_by_depth / expected_blocks, label=f\"{block_time:.0f}s ~ {block_time / sims[s].network.mixnet_delay_mean:.1f}x mix delay\")\n",
    "\n",
    "_ = plt.title(f\"reorg depth sensitivity to block time @ {mixnet_delay_mean}s mixnet delay\")\n",
    "_ = plt.xlabel(\"reorg depth\")\n",
    "_ = plt.ylabel(\"frequency\")\n",
    "_ = plt.legend()\n",
    "_ = plt.yscale(\"log\")\n",
    "_ = plt.xlim(0, MAX)\n",
    "_ = plt.ylim(10**-4, 4)\n",
    "# _ = plt.ylim(0,None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "3ba22221-45d6-4e4c-9cee-9e5cb4855dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# _ = plt.plot([(1 / s.params.f) / s.network.mixnet_delay_mean for s in sims], [len(s.honest_chain()) / s.params.SLOTS for s in sims])\n",
    "_ = plt.plot([s.params.f for s in sims], [len(s.honest_chain(-1, s.params.SLOTS)) / s.params.SLOTS for s in sims])\n",
    "\n",
    "_ = plt.title(\"chain growth vs. block time multiple of network delay\")\n",
    "_ = plt.ylabel(\"blocks/slot\")\n",
    "_ = plt.xlabel(\"block time multiple of network delay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "8bf09039-2bff-40a7-96b4-df55a514c060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# _ = plt.plot([(1 / s.params.f) / s.network.mixnet_delay_mean for s in sims], [len(s.honest_chain()) / s.params.SLOTS for s in sims])\n",
    "_ = plt.plot([s.params.f for s in sims], [len(s.honest_chain(-1, s.params.SLOTS)) / len(s.blocks) for s in sims])\n",
    "\n",
    "_ = plt.title(\"block efficiency\")\n",
    "_ = plt.ylabel(\"% of blocks member of honest chain\")\n",
    "_ = plt.xlabel(\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b360b74-c8f6-4694-b511-7ee4e50275cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}