logos-blockchain-pocs/cryptarchia/longest-chain-cryptarchia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a9e0b910-c633-4dbe-827c-4ddb804f7a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def phi(f, alpha):\n",
    "    return 1 - (1-f)**alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 503,
   "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",
    "    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",
    "\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",
    "        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, leader, slot):\n",
    "        assert type(leader) in [int, np.int64], type(leader)\n",
    "        assert isinstance(slot, int)\n",
    "        arrived_blocks = self.block_arrivals[leader] <= slot\n",
    "        return (self.block_heights * arrived_blocks).argmax()\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\"$B_{{{b}}}$\") 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), 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=\"black\", lw=0.5))\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",
    "        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 honest_chain(self):\n",
    "        chain_head = max(self.blocks, key=lambda b: b.height)\n",
    "        honest_chain = {chain_head.id}\n",
    "        \n",
    "        curr_block = chain_head\n",
    "        while curr_block.parent >= 0:\n",
    "            honest_chain.add(curr_block.parent)\n",
    "            curr_block = self.blocks[curr_block.parent]\n",
    "        return sorted(honest_chain, key=lambda b: self.blocks[b].height)\n",
    "\n",
    "    def visualize_chain(self):\n",
    "        honest_chain = self.honest_chain()\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},{block.slot}\")\n",
    "            if block.parent >= 0:\n",
    "                G.add_edge(int(block.id), int(block.parent), width=2, color=color)\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",
    "        # 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",
    "        for s in range(1, self.params.SLOTS):\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",
    "\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(leader, s)\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_height_by_slot = np.zeros(self.params.SLOTS, dtype=np.int64)\n",
    "        for block in self.blocks:\n",
    "            block_height = np.zeros(self.params.SLOTS, dtype=np.int64) + block.height\n",
    "            block_height[:block.slot] = 0\n",
    "            honest_height_by_slot = np.maximum(block_height, honest_height_by_slot)\n",
    "        \n",
    "        for slot in range(1, self.params.SLOTS):\n",
    "            if honest_height_by_slot[slot] == 0:\n",
    "                honest_height_by_slot[slot] = honest_height_by_slot[slot-1]\n",
    "\n",
    "        \n",
    "        honest_chain = self.honest_chain()\n",
    "        \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 b in range(len(self.blocks)):\n",
    "            if block.id > 0 and block.id % 1000 == 0:\n",
    "                print(\"Processing block\", block)\n",
    "            block = self.blocks[b]\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",
    "                nearest_honest_block = self.blocks[nearest_honest_block.parent]\n",
    "\n",
    "            remaining_slots = adversary_active_slots[block.slot+1:]\n",
    "            cumulative_rel_height = remaining_slots.cumsum()\n",
    "\n",
    "            adverserial_height_by_slot = block.height + cumulative_rel_height\n",
    "\n",
    "            honest_height_by_slot_lookahead = honest_height_by_slot[block.slot + 1:]\n",
    "        \n",
    "            adverserial_wins = adverserial_height_by_slot > honest_height_by_slot_lookahead\n",
    "        \n",
    "            reorg_events = adverserial_wins & all_active_slots[block.slot+1:]\n",
    "            reorg_depths = np.append(reorg_depths, honest_height_by_slot_lookahead[reorg_events] - nearest_honest_block.height)\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_height_by_slot[:last_slot-first_slot]-honest_height_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",
    "        \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",
    "            _ = 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",
    "        return reorg_depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d7eef71a-aa3c-49df-a711-9c9f7f5cb4a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg blocks per slot 0.04708\n",
      "Number of blocks 4708\n",
      "longest chain 2345\n",
      "CPU times: user 6.42 s, sys: 7.49 s, total: 13.9 s\n",
      "Wall time: 14.7 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": 125,
   "id": "aabccc4e-8f47-403e-b7f9-7508e93ec18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim.visualize_chain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0b5b4d8d-85af-4252-b12a-f27fef099d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.95 s, sys: 287 ms, total: 3.24 s\n",
      "Wall time: 3.28 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "reorgs = sim.adverserial_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "78567508-a1a3-4b89-abd3-9cd1b4bbb692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.08% of slots were reorged with depth >= 0\n",
      "1.41% of slots were reorged with depth >= 1\n",
      "0.68% of slots were reorged with depth >= 2\n",
      "0.29% of slots were reorged with depth >= 3\n",
      "0.13% of slots were reorged with depth >= 4\n",
      "0.06% of slots were reorged with depth >= 5\n",
      "0.03% of slots were reorged with depth >= 6\n",
      "0.01% 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": 128,
   "id": "76de5a72-cca5-4b00-9feb-7563cca9c03d",
   "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": 129,
   "id": "52ff4e83-c6f9-4933-9190-564124a479bc",
   "metadata": {},
   "outputs": [
    {
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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": 542,
   "id": "9cca2f57-1083-446c-b083-1dd158e0e7ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/5\n",
      "simulating 2/5\n",
      "simulating 3/5\n",
      "simulating 4/5\n",
      "simulating 5/5\n",
      "finished simulation, starting analysis\n",
      "Processing block Block(id=1000, slot=21002, height=953, parent=999, leader=20)\n",
      "Processing block Block(id=2000, slot=43475, height=1911, parent=1999, leader=72)\n",
      "Processing block Block(id=3000, slot=64765, height=2867, parent=2999, leader=23)\n",
      "Processing block Block(id=4000, slot=86142, height=3833, parent=3999, leader=70)\n",
      "Processing block Block(id=5000, slot=107452, height=4794, parent=4999, leader=52)\n",
      "Processing block Block(id=6000, slot=129648, height=5760, parent=5999, leader=41)\n",
      "Processing block Block(id=7000, slot=150919, height=6702, parent=6999, leader=70)\n",
      "Processing block Block(id=8000, slot=172689, height=7650, parent=7999, leader=66)\n",
      "Processing block Block(id=9000, slot=194671, height=8614, parent=8999, leader=27)\n",
      "Processing block Block(id=1000, slot=21002, height=724, parent=999, leader=20)\n",
      "Processing block Block(id=2000, slot=43475, height=1451, parent=1997, leader=72)\n",
      "Processing block Block(id=3000, slot=64765, height=2150, parent=2999, leader=23)\n",
      "Processing block Block(id=4000, slot=86142, height=2886, parent=3999, leader=70)\n",
      "Processing block Block(id=5000, slot=107452, height=3612, parent=4997, leader=52)\n",
      "Processing block Block(id=6000, slot=129648, height=4345, parent=5998, leader=41)\n",
      "Processing block Block(id=7000, slot=150919, height=5067, parent=6998, leader=70)\n",
      "Processing block Block(id=8000, slot=172689, height=5777, parent=7998, leader=66)\n",
      "Processing block Block(id=9000, slot=194671, height=6507, parent=8999, leader=27)\n",
      "Processing block Block(id=1000, slot=21002, height=584, parent=999, leader=20)\n",
      "Processing block Block(id=2000, slot=43475, height=1157, parent=1997, leader=72)\n",
      "Processing block Block(id=3000, slot=64765, height=1733, parent=2998, leader=23)\n",
      "Processing block Block(id=4000, slot=86142, height=2329, parent=3999, leader=70)\n",
      "Processing block Block(id=5000, slot=107452, height=2912, parent=4997, leader=52)\n",
      "Processing block Block(id=6000, slot=129648, height=3510, parent=5998, leader=41)\n",
      "Processing block Block(id=7000, slot=150919, height=4102, parent=6995, leader=70)\n",
      "Processing block Block(id=8000, slot=172689, height=4684, parent=7996, leader=66)\n",
      "Processing block Block(id=9000, slot=194671, height=5264, parent=8998, leader=27)\n",
      "Processing block Block(id=1000, slot=21002, height=476, parent=999, leader=20)\n",
      "Processing block Block(id=2000, slot=43475, height=953, parent=1996, leader=72)\n",
      "Processing block Block(id=3000, slot=64765, height=1429, parent=2998, leader=23)\n",
      "Processing block Block(id=4000, slot=86142, height=1936, parent=3996, leader=70)\n",
      "Processing block Block(id=5000, slot=107452, height=2429, parent=4998, leader=52)\n",
      "Processing block Block(id=6000, slot=129648, height=2928, parent=5998, leader=41)\n",
      "Processing block Block(id=7000, slot=150919, height=3419, parent=6996, leader=70)\n",
      "Processing block Block(id=8000, slot=172689, height=3905, parent=7996, leader=66)\n",
      "Processing block Block(id=9000, slot=194671, height=4406, parent=8998, leader=27)\n",
      "Processing block Block(id=1000, slot=21002, height=421, parent=999, leader=20)\n",
      "Processing block Block(id=2000, slot=43475, height=838, parent=1997, leader=72)\n",
      "Processing block Block(id=3000, slot=64765, height=1254, parent=2998, leader=23)\n",
      "Processing block Block(id=4000, slot=86142, height=1676, parent=3996, leader=70)\n",
      "Processing block Block(id=5000, slot=107452, height=2098, parent=4998, leader=52)\n",
      "Processing block Block(id=6000, slot=129648, height=2537, parent=5998, leader=41)\n",
      "Processing block Block(id=7000, slot=150919, height=2950, parent=6992, leader=70)\n",
      "Processing block Block(id=8000, slot=172689, height=3378, parent=7996, leader=66)\n",
      "Processing block Block(id=9000, slot=194671, height=3810, parent=8996, leader=27)\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": 543,
   "id": "1ff938f3-6bc4-4b8e-bd9a-492db140d7b9",
   "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": 492,
   "id": "8c9a369c-2d55-4c07-8bfe-9e270cfed90a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating 1/4\n",
      "simulating 2/4\n",
      "simulating 3/4\n",
      "simulating 4/4\n",
      "finished simulation, starting analysis\n",
      "Processing block Block(id=1000, slot=6363, height=320, parent=996, leader=23)\n",
      "Processing block Block(id=2000, slot=12555, height=639, parent=1993, leader=84)\n",
      "Processing block Block(id=3000, slot=18477, height=952, parent=2996, leader=17)\n",
      "Processing block Block(id=4000, slot=25149, height=1282, parent=3996, leader=20)\n",
      "Processing block Block(id=5000, slot=31501, height=1599, parent=4998, leader=56)\n",
      "Processing block Block(id=6000, slot=37990, height=1919, parent=5991, leader=8)\n",
      "Processing block Block(id=7000, slot=44481, height=2251, parent=6997, leader=27)\n",
      "Processing block Block(id=8000, slot=50868, height=2578, parent=7992, leader=36)\n",
      "Processing block Block(id=9000, slot=57212, height=2896, parent=8996, leader=8)\n",
      "Processing block Block(id=10000, slot=63774, height=3230, parent=9996, leader=52)\n",
      "Processing block Block(id=11000, slot=70157, height=3546, parent=10993, leader=0)\n",
      "Processing block Block(id=12000, slot=76603, height=3862, parent=11988, leader=23)\n",
      "Processing block Block(id=13000, slot=82834, height=4181, parent=12995, leader=85)\n",
      "Processing block Block(id=14000, slot=89666, height=4523, parent=13994, leader=62)\n",
      "Processing block Block(id=15000, slot=95882, height=4848, parent=14996, leader=7)\n",
      "Processing block Block(id=16000, slot=102488, height=5177, parent=15997, leader=8)\n",
      "Processing block Block(id=17000, slot=108773, height=5491, parent=16995, leader=5)\n",
      "Processing block Block(id=18000, slot=115521, height=5820, parent=17993, leader=89)\n",
      "Processing block Block(id=19000, slot=121882, height=6133, parent=18994, leader=68)\n",
      "Processing block Block(id=20000, slot=128075, height=6448, parent=19990, leader=35)\n",
      "Processing block Block(id=21000, slot=134265, height=6774, parent=20996, leader=10)\n",
      "Processing block Block(id=22000, slot=140656, height=7092, parent=21995, leader=98)\n",
      "Processing block Block(id=23000, slot=146896, height=7405, parent=22997, leader=50)\n",
      "Processing block Block(id=1000, slot=13599, height=507, parent=995, leader=38)\n",
      "Processing block Block(id=2000, slot=27183, height=999, parent=1999, leader=85)\n",
      "Processing block Block(id=3000, slot=40799, height=1491, parent=2995, leader=45)\n",
      "Processing block Block(id=4000, slot=53827, height=1978, parent=3995, leader=20)\n",
      "Processing block Block(id=5000, slot=66897, height=2473, parent=4994, leader=6)\n",
      "Processing block Block(id=6000, slot=80538, height=2985, parent=5999, leader=19)\n",
      "Processing block Block(id=7000, slot=94047, height=3485, parent=6996, leader=73)\n",
      "Processing block Block(id=8000, slot=107291, height=3990, parent=7999, leader=10)\n",
      "Processing block Block(id=9000, slot=120394, height=4502, parent=8998, leader=52)\n",
      "Processing block Block(id=10000, slot=134175, height=5014, parent=9999, leader=70)\n",
      "Processing block Block(id=11000, slot=147463, height=5517, parent=10997, leader=73)\n",
      "Processing block Block(id=12000, slot=160780, height=6014, parent=11998, leader=21)\n",
      "Processing block Block(id=13000, slot=174393, height=6517, parent=12999, leader=49)\n",
      "Processing block Block(id=14000, slot=187294, height=6991, parent=13999, leader=17)\n",
      "Processing block Block(id=15000, slot=201132, height=7503, parent=14999, leader=37)\n",
      "Processing block Block(id=16000, slot=214289, height=7998, parent=15997, leader=23)\n",
      "Processing block Block(id=17000, slot=227195, height=8491, parent=16999, leader=38)\n",
      "Processing block Block(id=18000, slot=240271, height=8990, parent=17997, leader=13)\n",
      "Processing block Block(id=19000, slot=254358, height=9507, parent=18998, leader=70)\n",
      "Processing block Block(id=20000, slot=267926, height=10010, parent=19996, leader=19)\n",
      "Processing block Block(id=21000, slot=281002, height=10497, parent=20999, leader=20)\n",
      "Processing block Block(id=22000, slot=294406, height=10999, parent=21999, leader=32)\n",
      "Processing block Block(id=1000, slot=27987, height=680, parent=999, leader=42)\n",
      "Processing block Block(id=2000, slot=54664, height=1341, parent=1999, leader=8)\n",
      "Processing block Block(id=3000, slot=81950, height=2006, parent=2999, leader=38)\n",
      "Processing block Block(id=4000, slot=108979, height=2670, parent=3995, leader=38)\n",
      "Processing block Block(id=5000, slot=136878, height=3343, parent=4999, leader=40)\n",
      "Processing block Block(id=6000, slot=163748, height=4015, parent=5998, leader=70)\n",
      "Processing block Block(id=7000, slot=192675, height=4698, parent=6999, leader=68)\n",
      "Processing block Block(id=8000, slot=221466, height=5369, parent=7999, leader=1)\n",
      "Processing block Block(id=9000, slot=248970, height=6030, parent=8999, leader=93)\n",
      "Processing block Block(id=10000, slot=277928, height=6714, parent=9998, leader=66)\n",
      "Processing block Block(id=11000, slot=305384, height=7372, parent=10999, leader=42)\n",
      "Processing block Block(id=12000, slot=332509, height=8051, parent=11997, leader=92)\n",
      "Processing block Block(id=13000, slot=359097, height=8708, parent=12998, leader=19)\n",
      "Processing block Block(id=14000, slot=387363, height=9366, parent=13997, leader=33)\n",
      "Processing block Block(id=15000, slot=414827, height=10023, parent=14995, leader=17)\n",
      "Processing block Block(id=16000, slot=442300, height=10693, parent=15999, leader=89)\n",
      "Processing block Block(id=17000, slot=470243, height=11365, parent=16999, leader=8)\n",
      "Processing block Block(id=18000, slot=499093, height=12056, parent=17998, leader=83)\n",
      "Processing block Block(id=19000, slot=526688, height=12721, parent=18999, leader=89)\n",
      "Processing block Block(id=20000, slot=555973, height=13410, parent=19998, leader=38)\n",
      "Processing block Block(id=21000, slot=583015, height=14057, parent=20998, leader=70)\n",
      "Processing block Block(id=1000, slot=42927, height=756, parent=999, leader=21)\n",
      "Processing block Block(id=2000, slot=84798, height=1524, parent=1999, leader=21)\n",
      "Processing block Block(id=3000, slot=126990, height=2279, parent=2999, leader=35)\n",
      "Processing block Block(id=4000, slot=168468, height=3022, parent=3999, leader=86)\n",
      "Processing block Block(id=5000, slot=211518, height=3781, parent=4998, leader=13)\n",
      "Processing block Block(id=6000, slot=254462, height=4515, parent=5999, leader=7)\n",
      "Processing block Block(id=7000, slot=297291, height=5284, parent=6997, leader=30)\n",
      "Processing block Block(id=8000, slot=337460, height=6021, parent=7999, leader=68)\n",
      "Processing block Block(id=9000, slot=379964, height=6780, parent=8999, leader=4)\n",
      "Processing block Block(id=10000, slot=421773, height=7525, parent=9997, leader=74)\n",
      "Processing block Block(id=11000, slot=463452, height=8279, parent=10999, leader=2)\n",
      "Processing block Block(id=12000, slot=508227, height=9044, parent=11999, leader=42)\n",
      "Processing block Block(id=13000, slot=551336, height=9812, parent=12998, leader=4)\n",
      "Processing block Block(id=14000, slot=593129, height=10575, parent=13997, leader=70)\n",
      "Processing block Block(id=15000, slot=633453, height=11328, parent=14999, leader=13)\n",
      "Processing block Block(id=16000, slot=674863, height=12062, parent=15999, leader=38)\n",
      "Processing block Block(id=17000, slot=714726, height=12815, parent=16999, leader=68)\n",
      "Processing block Block(id=18000, slot=757976, height=13567, parent=17998, leader=45)\n",
      "Processing block Block(id=19000, slot=799790, height=14311, parent=18998, leader=17)\n",
      "Processing block Block(id=20000, slot=841233, height=15063, parent=19997, leader=52)\n",
      "Processing block Block(id=21000, slot=881361, height=15808, parent=20998, leader=89)\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": 501,
   "id": "87c8d0b8-c8d2-4c49-a9c4-2eaefd41c254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAX=40\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 (per block)\")\n",
    "_ = plt.legend()\n",
    "_ = plt.yscale(\"log\")\n",
    "_ = plt.xlim(0, MAX)\n",
    "_ = plt.ylim(10**-4, 4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "af11b126-33b6-4b62-868e-b11a0090aa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(150000, 5.0), (300000, 10.0), (600000, 20.0), (900000, 30.0)]"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(s.params.SLOTS, 1 / s.params.f) for s in sims]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "23a226b5-b624-4ae9-86bd-2d7201bbb365",
   "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([s.network.mixnet_delay_mean for s in sims], [len(s.honest_chain()) / s.params.SLOTS for s in sims])\n",
    "_ = plt.scatter([1 / s.params.f for s in sims], [len(s.honest_chain()) / s.params.SLOTS for s in sims])\n",
    "\n",
    "_ = plt.title(\"chain growth vs. block time multiple of network delay\")\n",
    "_ = plt.ylabel(\"chain growth / slot\")\n",
    "_ = plt.xlabel(\"block time multiple of network delay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "317dd511-6fda-44c3-88ff-622460b36665",
   "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.scatter([s.params.f for s in sims], [len(s.honest_chain()) / 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",
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