medalla-2-validator-taxonomy.ipynb

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    "---\n",
    "# front matter for Jekyll\n",
    "title: \"Medalla Participation Rates: A Validator Taxonomy\"\n",
    "permalink: \"/posts/medalla-validator-taxonomy\"\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As highlighted in the [data pre-processing article](/posts/medalla-data-prep), the proportion of validators who successfully submit attestations is an important metric. Validators representing at least ⅔ of the staked ether need to attest to any given block before it can be finalised (along with all its ancestor blocks). The fraction of staked ether which *does* attest when called to do so in any given slot is called the *participation rate*. In general, a high participation rate is a signal of a healthy network. Conversely if the participation rate drops significantly below 100%, this may be a result of a range of different problems, such as:\n",
    "\n",
    "- a bug in a client preventing validators running that software from attesting at the right time;\n",
    "- the network becoming partitioned such that different groups of validators cannot talk to one another;\n",
    "- interoperability problems between clients due to incompatible interpretations of the spec;\n",
    "- excessive computational load on validators (or underpowered validator hardware);\n",
    "- excessive bandwith usage or network latency;\n",
    "- \\[and many others\\].\n",
    "\n",
    "Such problems might be caused by implentation bugs, attacks by adversaries or, most seriously of all, fundamental flaws in the design of the eth2 spec. Identifying them in the testnet data is therefore extremely important, since they could hint at serious risks for validators who will later be putting valuable assets (ether) at risk.\n",
    "\n",
    "However, in the Medalla network there is no real value is at stake. This leads to another possible explanation for low participation rates:\n",
    "\n",
    "- users are not incentivised to participate, and may not bother to run their validators consistently, or at all.\n",
    "\n",
    "Moreover, users who run into difficulty in using client software or simply lose interest in the project are likely simply to \"walk away\" from the network rather than using the *voluntary exit* procedure defined in the spec. But before getting into that, let's first take a look at how the make-up of the validator set varies over the course of the dataset. As before, we start by getting pulling out some of the data we need from `chaind`:"
   ]
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   "source": [
    "# imports\n",
    "import time\n",
    "\n",
    "import psycopg2\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import pandas as pd\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     0
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   "outputs": [],
   "source": [
    "# open/restart connection to chaind database\n",
    "try:\n",
    "    cursor.close()\n",
    "    connection.close()\n",
    "except:\n",
    "    pass\n",
    "\n",
    "connection = psycopg2.connect(user=\"chain\", host=\"127.0.0.1\", database=\"chain\", password=\"medalla\")\n",
    "cursor = connection.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "# get info about dataset and validators\n",
    "\n",
    "cursor.execute(\"SELECT MAX(f_slot) FROM t_blocks\")\n",
    "latest_slot = cursor.fetchone()[0]\n",
    "n_slots = latest_slot + 1\n",
    "n_epochs = (n_slots - (n_slots % 32)) // 32\n",
    "\n",
    "cursor.execute(\"SELECT f_activation_eligibility_epoch, f_activation_epoch, f_exit_epoch, f_slashed, \"\n",
    "               \"f_first_attested_epoch, f_latest_attested_epoch FROM t_validators ORDER BY f_index\")\n",
    "result = cursor.fetchall()\n",
    "validators = [{\"activation_eligibility_epoch\": r[0],\n",
    "               \"activation_epoch\"            : r[1],\n",
    "               \"exit_epoch\"                  : r[2],\n",
    "               \"slashed\"                     : r[3],\n",
    "               \"first_attestation\"           : r[4],\n",
    "               \"latest_attestation\"          : r[5]} for r in result]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validator Life Cycle\n",
    "Validators on the beacon chain progress through several states, starting as deposits of at least 32 ETH on the eth1 chain (or the Görli testnet in the case of Medalla). These deposits are processed by beacon chain nodes, becoming eligible for activation either immediately (if there aren't too many validators trying to join at once), or after they reach the front of an activation queue. Once activated, each validator is assigned to one committee each epoch, and expected to produce an attestation. Validators may also be nominated as block proposers.\n",
    "\n",
    "If a validator no longer wishes to participate in the beacon chain they may initiate a *voluntary exit*. If there are many validators attempting to exit the validator set at the same time then again, they will form a queue, and will be required to continue submitting attestations until they reach the front of the queue. If a validator's balance falls below 16 ETH due to penalties for poor performance (such as *inactivity leak*), they will also exit the validator set.\n",
    "\n",
    "Finally, if a validator provably contradicts themselves — for example by attesting to two different blocks for the same slot — they may be slashed, instantly losing a large proportion of their staked ether and being ejected from the validator set.\n",
    "\n",
    "At each epoch therefore we can categorise validators into four types:\n",
    "1. **waiting**: validators who have had their deposits from the eth1 chain processed, and are in a queue to join the active validator set\n",
    "2. **active**: validators who are required to produce an attestation each epoch\n",
    "3. **exited**: validators who have left the validator set, either because they made a voluntary withdrawal, or because their balance leaked through inactivity to the point where they were ejected from the validator set (this happens if the validator's balance drops below 16 ETH)\n",
    "4. **slashed**: validators who were ejected from the validator set for breaking protocol rules (e.g. contradicting themselves by attesting to multiple blocks for the same slot)\n",
    "\n",
    "Validators who have exited or been slashed cannot rejoin the activation queue or start attesting again (although in future they will be able to withdraw previously staked ether to create a new valdator, if they choose)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed in 00:05:58                                                  \n"
     ]
    }
   ],
   "source": [
    "# get validator status by epoch - waiting, active, exited, slashed\n",
    "start_time = time.time()\n",
    "\n",
    "waiting_count = [0] * n_epochs\n",
    "active_count  = [0] * n_epochs\n",
    "slashed_count = [0] * n_epochs\n",
    "exited_count  = [0] * n_epochs\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    for validator in validators:\n",
    "        if epoch >= validator[\"activation_eligibility_epoch\"] and epoch < validator[\"activation_epoch\"]:\n",
    "            waiting_count[epoch] += 1\n",
    "        elif validator[\"exit_epoch\"] != -1 and epoch >= validator[\"exit_epoch\"] :\n",
    "            if validator[\"slashed\"]:\n",
    "                slashed_count[epoch] += 1\n",
    "            else:\n",
    "                exited_count[epoch] += 1\n",
    "        elif epoch >= validator[\"activation_epoch\"]:\n",
    "            active_count[epoch] += 1\n",
    "\n",
    "    seconds = time.time() - start_time\n",
    "    elapsed = time.strftime(\"%H:%M:%S\",time.gmtime(seconds))\n",
    "    left = time.strftime(\"%H:%M:%S\",time.gmtime(seconds * ((n_epochs) / (epoch+1)-1)))\n",
    "    percentage = 100*(epoch+1)/n_epochs\n",
    "    print(f\"epoch {epoch} of {n_epochs-1} ({percentage:.2f}%) / {elapsed} elapsed / {left} left\", end='\\r')\n",
    "\n",
    "print(f\"completed in {elapsed}\" + ' ' * 50)\n",
    "            \n",
    "            \n",
    "status_data = pd.DataFrame({'waiting': waiting_count, 'active': active_count,\n",
    "                            'exited': exited_count, 'slashed': slashed_count})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart below shows the evolution of each of these categories of validator. We can see at genesis that almost the entire validator set is composed of *active* validators — those who submitted their deposits prior to genesis. During most of the period the active validator set grows linearly as the queue of *waiting* validators is processed.\n",
    "\n",
    "The exception is a short period around epoch 3000 when a number of validators were slashed, the validator queue (represented in orange) was emptied and the active set stopped growing. This was due to the [roughtime incident](https://medium.com/prysmatic-labs/eth2-medalla-testnet-incident-f7fbc3cc934a), which will be explored in more detail in the next notebook. A small sliver of non-slashed exited validators also grows gradually throughout the dataset, most like due to users testing the *voluntary exit* procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw validator status stacked area plot\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.stackplot(range(len(status_data.index)), status_data['active'], status_data['slashed'],\n",
    "              status_data['exited'], status_data['waiting'],labels=['active','slashed','exited','waiting'],\n",
    "              colors=['tab:blue','tab:red','tab:green','tab:orange'])\n",
    "plt.legend(loc='right')\n",
    "plt.margins(0,0)\n",
    "plt.title('Validator status')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('number of validators')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effective Balance\n",
    "Eth2 uses a concept of *effective balance* to simplify protocol calculations. While validators' actual balances will generally grow or leak very gradually according to their participation, the effective balance can only take integer values, and changes according to a hysteresis function to prevent it changing too frequently.\n",
    "\n",
    "We can confirm that the \"exited\" validators from the chart above is composed exclusively of validators who have triggered a voluntary exit, rather than inactivity leak causing their balances to fall below 16 ETH, because the lowest effective balance of any validator in this group is 30 ETH, as calculated below.\n",
    "\n",
    "Looking more widely at the effective balances of the validators, we find that among validators who have activated but not been slashed, the minimum effective balance is also 30 ETH, while the maximum effective balance is the protocol-imposed limit of 32 ETH. Meanwhile a small number of validators have been quite severely slashed. This can be seen in the distributions of effective balances for both slashed and non-slashed validators plotted below.\n",
    "\n",
    "Given the very limited variation in effective balance amongst active validators in this dataset, we will weight validators equally in the remaining analysis. This will simplify the analysis at the cost of slight errors in calculations of participation rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum exited validator effective balance: 30 ETH\n",
      "Non-slashed validator balance (min, max): (30, 32) ETH\n",
      "Slashed validator balance (min, max): (15, 32) ETH\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show effective balances\n",
    "\n",
    "cursor.execute(f\"SELECT MIN(f_effective_balance) FROM t_validator_balances \"\n",
    "               f\"WHERE f_validator_index IN \"\n",
    "               f\"(SELECT f_index FROM t_validators WHERE f_exit_epoch != -1 AND NOT f_slashed)\"\n",
    "               f\"AND f_epoch = {n_epochs-1}\")\n",
    "\n",
    "min_balance = cursor.fetchone()[0] * 10**-9\n",
    "print(f\"Minimum exited validator effective balance: {int(min_balance)} ETH\")\n",
    "\n",
    "cursor.execute(f\"SELECT f_effective_balance FROM t_validator_balances WHERE f_validator_index IN\"\n",
    "               f\"(SELECT f_index FROM t_validators WHERE NOT f_slashed  AND f_activation_epoch != -1)\"\n",
    "               f\"AND f_epoch = {n_epochs-1}\")\n",
    "eff_balances = pd.Series([b[0]*10**-9 for b in cursor.fetchall()], name='non-slashed')\n",
    "\n",
    "cursor.execute(f\"SELECT f_effective_balance FROM t_validator_balances WHERE f_validator_index IN\"\n",
    "               f\"(SELECT f_index FROM t_validators WHERE f_slashed)\"\n",
    "               f\"AND f_epoch = {n_epochs-1}\")\n",
    "eff_balances_slashed = pd.Series([b[0]*10**-9 for b in cursor.fetchall()], name='slashed')\n",
    "\n",
    "print(f\"Non-slashed validator balance (min, max): \"\n",
    "      f\"({int(eff_balances.min())}, {int(eff_balances.max())}) ETH\")\n",
    "print(f\"Slashed validator balance (min, max): \"\n",
    "      f\"({int(eff_balances_slashed.min())}, {int(eff_balances_slashed.max())}) ETH\")\n",
    "\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "eff_balances.plot.hist(bins=range(0,35))\n",
    "eff_balances_slashed.plot.hist(bins=range(0,35))\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('effective balance (ETH)')\n",
    "plt.title(f\"Validator effective balances at epoch {n_epochs-1}\")\n",
    "plt.xlim(0,34);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Non-Participating Validator\n",
    "We'll now look at whether each active validator managed to attest successfully, and have their attestations included into the canonical chain. If their attestations weren't included, we can use the information from each validator's `first_attestated_epoch` and `latest_attestated_epoch`, which we calculated in [data processing](/posts/medalla-data-prep) to have a guess at *why* the attestation was missed. We use the following definitions:\n",
    "1. `success_count`: the number of attestations in the epoch that where successfully included in the canonical chain\n",
    "2. `absent_count` : the number of scheduled attestations from validators who did not have any successful attestations in the whole dataset — we think of these validators as *absent* because they never showed up at all\n",
    "3. `dormant_count`: the number of scheduled attestations from validators who have not *yet* made a successful attestations (but they did later in the dataset) — we think of these as *dormant* validators who were not ready to attest when their `activation_epoch` hit (perhaps they overslept?)\n",
    "4. `abandoned_count`: the number of scheduled attestations from validators who have no later successful attestations in the dataset (these are perhaps validators who lost interest in the testnet and simply *abandoned* their validator(s) without initiating the proper exit procedure)\n",
    "5. `missed_count`: any other scheduled attestations which were not made each epoch.\n",
    "\n",
    "As stated above these categories are all just guesses, based on each validator's behaviour. These categorisations are partly based on the assumption that with no real value at stake on the Medalla testnet, some people will make deposits for validators that they never bother to fire up (*absent*), some may not prepare in advance of activation and therefore be late (*dormant*), and still others may not bother to pick up their validator if it falls over, or they have no further interest in participating (*abandoned*).\n",
    "\n",
    "This is not meant pejoratively! My own validator would show up as *dormant* for a few epochs, since I hadn't factored in the time needed to compile and run the Lighthouse beacon chain client before Medalla's genesis time (**blushes**). Rather it is an illustration of the difficulty inherent in trying to test an economically driven consensus/finality mechanism without economic incentives. The code below totals up each of these categories of validator on a per-epoch basis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed in 00:12:29.\n"
     ]
    }
   ],
   "source": [
    "# count and categorise attestations\n",
    "\n",
    "# if the attestation is missed and the validator has not yet made any attestations, it is considered dormant\n",
    "# if the attestation is missed and the validator makes no future attestations, it is considered abandoned\n",
    "# if the attestation is missed and the validator makes no successful attestations at all, it is considered absent\n",
    "\n",
    "start_time = time.time()\n",
    "last_update = 0\n",
    "\n",
    "empty = [0] * n_epochs\n",
    "success_count   = empty.copy()\n",
    "absent_count    = empty.copy()\n",
    "dormant_count   = empty.copy()\n",
    "abandoned_count = empty.copy()\n",
    "missed_count    = empty.copy()\n",
    "\n",
    "for slot in range(n_epochs * 32):\n",
    "    cursor.execute(f\"SELECT f_validator_index, f_performance, f_effectiveness \"\n",
    "                   f\"FROM t_validator_performance WHERE f_slot = {slot}\")\n",
    "    result = cursor.fetchone()\n",
    "    validator_indices  = result[0]\n",
    "    performance        = result[1]\n",
    "    effectiveness      = result[2]\n",
    "    \n",
    "    epoch = slot // 32\n",
    "    for position, val_index in enumerate(validator_indices):\n",
    "        if performance[position] != -1:\n",
    "            success_count[epoch]   += 1\n",
    "        elif validators[val_index][\"latest_attestation\"] == -1:\n",
    "            absent_count[epoch]    += 1\n",
    "        elif epoch < validators[val_index][\"first_attestation\"]:\n",
    "            dormant_count[epoch]   += 1\n",
    "        elif epoch > validators[val_index][\"latest_attestation\"]:\n",
    "            abandoned_count[epoch] += 1\n",
    "        else:\n",
    "            missed_count[epoch]    += 1\n",
    "\n",
    "    t = time.time()\n",
    "    if t - last_update > 0.1:\n",
    "        last_update = t\n",
    "        seconds = t - start_time\n",
    "        elapsed = time.strftime(\"%H:%M:%S\",time.gmtime(seconds))\n",
    "        left = time.strftime(\"%H:%M:%S\",time.gmtime(seconds * ((latest_slot+1) / (slot+1)-1)))\n",
    "        percentage = 100*(slot+1)/(latest_slot+1)\n",
    "        print(f\"epoch {epoch} of {latest_slot//32} ({percentage:.2f}%) / \"\n",
    "              f\"{elapsed} elapsed / {left} left\", end='\\r')\n",
    "\n",
    "clear_output()\n",
    "print(f\"completed in {elapsed}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Participation trends\n",
    "We can now take a look at how the network performed, in terms of the percentage of validators who successfully attested each epoch (the *participation rate*). We can also look that the breakdown of validators who failed to attest during a given epoch according to whether they have ever previously successfully attested, or will successfully attest at some future point (using the cateogires *absent*, *dormant* and *abandoned* defined above). This information is shown graphicaly in the stacked validator participation chart below.\n",
    "\n",
    "The biggest feature of this chart is clearly the enormous growth in missed attestations (orange) which occurs during/after the [roughtime incident](https://medium.com/prysmatic-labs/eth2-medalla-testnet-incident-f7fbc3cc934a). The participation rate even drops to zero for at least one epoch, raising an interesting question about the security of the network at this point. This incident and the network stress it caused will be the subject of the [next article](/posts/medalla-network-stress).\n",
    "\n",
    "The chart shows a fairly consistent slice of the validator set was composed of absent validators who deposited but never participated (in purple). The dormant slice (green) varied more, and interestingly we can see little ramps where a series of new validators were activated but were dormant for some time. This is likely to be the result of testnet whales joining with hundreds of validators without being ready. The number of dormant validators grows significantly during/after the roughtime incident, as new validators were unable to sync their clients to the beacon chain.\n",
    "\n",
    "The slice of abandoned validators (red) grows gradually throughout the dataset, and widens significantly during and after roughtime incident. It also grows much faster towards the end of the dataset. This may be due to large numbers of users switching off their validators in preparation for mainnet, or because they feel they have already devoted enough time and energy to the testnet for their own needs. Information about how to initiate a voluntary exit has been less widely shared than information on how to join the network (such as [Somer Esat](https://twitter.com/SomerEsat)'s excellent guides: [Prysm](https://medium.com/@SomerEsat/guide-to-staking-on-ethereum-2-0-ubuntu-medalla-prysm-4d2a86cc637b), [Lighthouse](https://medium.com/@SomerEsat/guide-to-staking-on-ethereum-2-0-ubuntu-medalla-lighthouse-c6f3c34597a8), [Teku](https://medium.com/@SomerEsat/guide-to-staking-on-ethereum-2-0-ubuntu-medalla-teku-170e2c52bd23), [Nimbus](https://medium.com/@SomerEsat/guide-to-staking-on-ethereum-2-0-ubuntu-medalla-nimbus-5f4b2b0f2d7c)), so some may not even realise that a formal exit procedure is needed.\n",
    "\n",
    "Indeed, [evidence from forums](https://www.reddit.com/r/ethstaker/comments/jdkgo7/are_we_still_trying_to_run_medalla/?utm_source=share&utm_medium=web2x&context=3) suggests that some users are unaware of how or why to use the protocol voluntary exit procedure. This effect is therefore probably part of the cause for the Medalla participation rate dropping [to very low levels](https://hackmd.io/@benjaminion/wnie2_201018#Testnets) in recent days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# participation rate stacked area plots\n",
    "\n",
    "participation_data = pd.DataFrame({'successful': success_count,\n",
    "                                   'missed': missed_count,\n",
    "                                   'absent': absent_count,\n",
    "                                   'dormant': dormant_count,\n",
    "                                   'abandoned': abandoned_count})\n",
    "\n",
    "perc_participation_data = participation_data.divide(participation_data.sum(axis=1), axis=0) * 100\n",
    "\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.stackplot(range(len(perc_participation_data.index)),\n",
    "              perc_participation_data['successful'],\n",
    "              perc_participation_data['missed'],\n",
    "              perc_participation_data['dormant'],\n",
    "              perc_participation_data['abandoned'],\n",
    "              perc_participation_data['absent'],\n",
    "              labels=['successful','missed','dormant','abandoned','absent'])\n",
    "plt.legend(loc='lower right')\n",
    "plt.margins(0,0)\n",
    "plt.title('Validator participation')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excluding Dormant, Absent and Abandoned Validators\n",
    "Using our categories for unresponsive validators (dormant, absent, abandoned) we can also take a look at what the network might look like without them. This makes sense, since in a value-bearing mainnet deployment, such validator behaviour would become economically costly.\n",
    "\n",
    "At an ETH price of USD 375, each validator will require USD 12,000 to be locked for participation. If these validators are ignored or abandoned by their owners, they will incur opportunity cost for missed validator rewards, and inactivity leakage cost for failure to attestations, in addition to the underlying cost of capital. With this in mind, the very existence of these types of non-live validator is not expected, unless such validator behaviour is somehow caused by as-yet unidentified client bugs or protocol flaws.\n",
    "\n",
    "The graphs created above have been repeated, but this time only `success_count` and `missed_count` are used in the calculation of the participation rate. Now the mean participation rate jumps by 10-13 percentage points, reaching  89% with zero sub-⅔ particpation epochs from 5000 onwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# participation rate statistics and stacked area plots (excluding unresponsive validators)\n",
    "\n",
    "reduced_participation_data = pd.DataFrame({'successful': success_count, 'missed': missed_count})\n",
    "\n",
    "perc_reduced_participation_data = participation_data.divide(reduced_participation_data.sum(axis=1), axis=0) * 100\n",
    "\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.stackplot(range(len(perc_reduced_participation_data.index)),\n",
    "              perc_reduced_participation_data['successful'],\n",
    "              perc_reduced_participation_data['missed'],\n",
    "              labels=['successful','missed'])\n",
    "plt.legend(loc='lower right')\n",
    "plt.margins(0,0)\n",
    "plt.title('Validator participation (excluding unresponsive validators)')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Participation statistics\n",
    "Another way of looking at the data is to consider the statistical distribution of participation rates by epoch. This can give us a better sense of what proportion of epochs fall above any particular partcipation rate. Clearly the key rate of greatest significance is ⅔, but a sense of the shape of the overall distribution may tell us more than this number alone.\n",
    "\n",
    "In the statistics and plots below we find that over 90% of epochs were above ⅔ participation. This proportion increases slightly if we ignore unresponsive (i.e. absent, dormant or abandoned) validators. We know that the previously mentioned roughtime incident was responsible for a prolonged periods of non-finalisation, and this will be explored in the next notebook. To take a look at how the network has performed since the resolution of this instance, the analysis is repeated in the second row, starting from epoch 5000.\n",
    "\n",
    "From epoch 5000 onwards, we find that 99.8% of epochs reached the ⅔ quorum. If we exclude unresponsive validators, this reaches 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "participation rate statistics by epoch\n",
      "\n",
      "all data                                     excl. unresponsive validators\n",
      "--------                                     -----------------------------\n",
      " mean  72.511856                              mean   82.982816\n",
      "  std  15.642732                               std   18.094975\n",
      "  min   0.000000                               min    0.000000\n",
      "  50%  76.511776                               50%   88.324085\n",
      "  max  86.428814                               max  100.000000\n",
      "epochs over 2/3 participation: 92.4%         epochs over 2/3 participation: 93.5%\n",
      "                                             \n",
      "                                             \n",
      "epoch 5000 onwards                           epoch 5000 onwards, excl. unresponsive validators\n",
      "------------------                           -------------------------------------------------\n",
      " mean  76.986188                              mean  89.459441\n",
      "  std   2.965273                               std   2.821643\n",
      "  min  43.980764                               min  54.357546\n",
      "  50%  77.596674                               50%  89.690280\n",
      "  max  85.462274                               max  98.977218\n",
      "epochs over 2/3 participation: 99.8%         epochs over 2/3 participation: 100.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# participation rate statistics - include/exclude roughtime, non-live validators\n",
    "\n",
    "print(\"participation rate statistics by epoch\\n\")\n",
    "\n",
    "col1 = []\n",
    "col2 = []\n",
    "col1.append(\"all data\")\n",
    "col1.append(\"--------\")\n",
    "stats = perc_participation_data['successful'].describe().loc[['mean','std','min','50%','max']]\n",
    "col1 += stats.reset_index().to_string(header=None, index=None).split('\\n')\n",
    "quorate = 100 * len(perc_participation_data[perc_participation_data['successful'] > 200/3]) / n_epochs\n",
    "col1.append(f\"epochs over 2/3 participation: {quorate:.1f}%\")\n",
    "col1 += ['','']\n",
    "\n",
    "col2.append(\"excl. unresponsive validators\")\n",
    "col2.append(\"-----------------------------\")\n",
    "stats = perc_reduced_participation_data['successful'].describe().loc[['mean','std','min','50%','max']]\n",
    "col2 += stats.reset_index().to_string(header=None, index=None).split('\\n')\n",
    "quorate = 100 * len(perc_reduced_participation_data[perc_reduced_participation_data['successful'] > 200/3]) / n_epochs\n",
    "col2.append(f\"epochs over 2/3 participation: {quorate:.1f}%\")\n",
    "col2 += ['','']\n",
    "\n",
    "col1.append(\"epoch 5000 onwards\")\n",
    "col1.append(\"------------------\")\n",
    "after_roughtime = perc_participation_data['successful'].iloc[range(5000,n_epochs)]\n",
    "stats = after_roughtime.describe().loc[['mean','std','min','50%','max']]\n",
    "col1 += stats.reset_index().to_string(header=None, index=None).split('\\n')\n",
    "quorate = 100 * len(after_roughtime[after_roughtime > 200/3]) / (n_epochs - 5000)\n",
    "col1.append(f\"epochs over 2/3 participation: {quorate:.1f}%\")\n",
    "\n",
    "col2.append(\"epoch 5000 onwards, excl. unresponsive validators\")\n",
    "col2.append(\"-------------------------------------------------\")\n",
    "after_roughtime_reduced = perc_reduced_participation_data['successful'].iloc[range(5000,n_epochs)]\n",
    "stats = after_roughtime_reduced.describe().loc[['mean','std','min','50%','max']]\n",
    "col2 += stats.reset_index().to_string(header=None, index=None).split('\\n')\n",
    "quorate = 100 * len(after_roughtime_reduced[after_roughtime_reduced > 200/3]) / (n_epochs - 5000)\n",
    "col2.append(f\"epochs over 2/3 participation: {quorate:.1f}%\")\n",
    "\n",
    "for row, text in enumerate(col1):\n",
    "    print(text.ljust(45,' ') + col2[row])\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15,9))\n",
    "fig.suptitle('Participation rate by epoch')\n",
    "    \n",
    "perc_participation_data['successful'].plot.hist(density=True, bins=range(0,101), ax=axs[0,0])\n",
    "axs[0, 0].set_title('all data')\n",
    "axs[0, 0].set_ylabel('% of validators')\n",
    "axs[0, 0].set_xlim(0,100)\n",
    "axs[0, 0].set_ylim(0,0.2)\n",
    "axs[0, 0].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0));\n",
    "\n",
    "perc_reduced_participation_data['successful'].plot.hist(density=True, bins=range(0,101), ax=axs[0,1])\n",
    "axs[0, 1].set_title('excl. unresponsive validators')\n",
    "axs[0, 1].set_ylabel('')\n",
    "axs[0, 1].set_xlim(0,100)\n",
    "axs[0, 1].set_ylim(0,0.2)\n",
    "axs[0, 1].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0));\n",
    "\n",
    "after_roughtime.plot.hist(density=True, bins=range(0,101), ax=axs[1,0])\n",
    "axs[1, 0].set_title('epoch 5000 onwards')\n",
    "axs[1, 0].set_xlabel('participation rate (%)')\n",
    "axs[1, 0].set_ylabel('% of validators')\n",
    "axs[1, 0].set_xlim(0,100)\n",
    "axs[1, 0].set_ylim(0,0.2)\n",
    "axs[1, 0].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0));\n",
    "\n",
    "after_roughtime_reduced.plot.hist(density=True, bins=range(0,101), ax=axs[1,1])\n",
    "axs[1, 1].set_title('epoch 5000 onwards, excl. unresponsive validators')\n",
    "axs[1, 1].set_xlabel('participation rate (%)')\n",
    "axs[1, 1].set_ylabel('')\n",
    "axs[1, 1].set_xlim(0,100)\n",
    "axs[1, 1].set_ylim(0,0.2)\n",
    "axs[1, 1].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genesis\n",
    "One of the difficulties encountered with Medalla was at genesis, it took longer than may have been expected before the participation rate reached the ⅔ threshold for finality. This can be seen in the area plot below showing the first 50 epochs. Initially a large proportion of validators are *dormant* and the ⅔ threshold is not reached until the 7th epoch. Even if we exclude the subset of *absent* validators, we do not see that finality would have been reached any sooner.\n",
    "\n",
    "This was a pattern that was repeated for the \"genesis rehearsal\" *Spadina* testnet, which was hindered by [client release and configuration issues](https://blog.ethereum.org/2020/10/01/eth2-quick-update-no-18/), taking even longer to reach finality. Finally, the *Zinken* genesis rehearsal appears to have gone smoothly, finalising at its [second epoch](https://twitter.com/q9fmz/status/1315630137719877632?s=20). But the genesis experiences for both Medalla and Spadina demonstrate that the launch process in particular has the potential for hiccups. The proposed [lengthening](https://hackmd.io/@benjaminion/wnie2_200808) of the genesis delay should go some way to preventing these problems for mainnet launch, and should be accompanied by guidance that validators should have their beacon nodes up and running well in advance of launch time.\n",
    "\n",
    "On the positive side, the actual penalties for non-participating validators (known as inactivity leak) are not severe, as long as the period of non-finality is reasonably short. A repeat of Medalla's ~1 hour wait for finality would not be a serious problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BbFdQwfaNLWy/ZC7bznmbkvop+S5N0loM2pIkdVKrQvX2BZVs39jE6MVz2WbOWxQ3TM53aZI2gkFbkqSEbNF7MAdnqtlvwUzK65ezeniP+N5AIYG45iAaqx5HqF4xx1AtdWEGbUmS2tHo8i04iBIOmjWBkZOfzHc5kvLIoC1J0mZIhzS7lo/koOYMB01/nYGTH8l3SZI6CYO2JEmbqDBVyNjykRxc38L+U1+ketKD+S5JUidk0JYkaR2K0kX0KaygpqCUPqle1IQ0Nc2RkXUr2HfK8/Sun5jvEiV1cgZtSVKP0CtdRHlBKWWZYspSRZSlCqkKaWpaAjXNzdQ01FFTt5w+KxdRs2w+5XVL8l2ypC7OoC1J2qBMyJAKKdKpNJmQzj4OKTIhTTqkSIUUmZAiTYp0SJMOgXRI5e6kkR3em9WPWz+K71ueDoE0IddeIEOKghBIAxne+56Jq6YjhUBpcwtlzU2UNzVQ1lhHeUMtZXXLKatdSnndUgqaGxJ/niSpNYO2JHVRmVSGskxvyjIllKazV2jLUgWUkqI0QnmE0uZmSpubKGusp6ypjtL6WoqaG0i3NOe+WkjHZtItTaSbm1rNbyLT0kyqpYl0bMn3oUpSl2TQlqROqrKwnIFFVQzM9GZgTDOwuZkB9SsZtGIxA5fMps+yedl7MEuSOiWDtiR1sN6ZEqoLy6nKlFCd6kV1SFPTAgMb6xlUu5wBy+YxcMksihum5btUSdJmMGhL0mYIBMoKSqkqLKMi3YuqVBFVIUNVhD7NzVQ3NlBVv5LqumVUr1xM9bL5FDbX57tsSVIHMGhLUiurrjZXZ3rnQnOaygiVzS1UNjVQ2dhAZd1yKuuXUbliERUrF5OOzfkuW5LUCRm0JXV71UVV9C8spzpdTHXIUB0D1c0tVDc2UN2wkj61uavNy+dT1FSX73IlSd2EQVtSlxcI9C+uYVhhFUNTRQxtamFo3QqGLZ3P0EXv0rvevs6SpI5n0JbU6ZVkSqgqLKUy05vqVC8Gk2FYUzPDapcxdOkchix4l8LmqfkuU5KkNRi0JXWoVEhRWVhBTUEZfTLFVIYCqmKgsiVS1dxEZUM9VY0rqaxdTmXtYqqWL/TDg5KkLsmgLald9EoXUVNURU2mN33TvegTU9S0RPo2NlBTv5Ka2iXULF9A9fL5ZFqm5LtcSZISZ9CWtEEVheX0L6ykf6aE/qGAfs2RAY0N9K9bTr8VC+m/ZA5ldUvyXaYkSZ2KQVsSkL2t3YdKh7NrS5oB9XX0r11K/+Xz6bd0Dr0a/TChJEmbyqAt9VAFqQJ2LhvB2FjEngtnscPUV8i0vJnvsiRJ6jYM2lIPkQopti0dxth0GXsuWcCu775MccM7+S5LkqRuy6AtdWMjeg9mbGE1ey5fyu7TX6Vi5aP5LkmSpB7DoC11MzVF1RxTPJjjZ7zNyMlP5rscSZJ6LIO21A1kUhk+XLE1xy9dzj5vP0Wm5cV8lyRJUo9n0Ja6sFGlwzieUo6e9CzV79yb73IkSVIrBm2piykvLOOI3iM4YfZURk9+PN/lSJKkD2DQlrqAVEixZ8UoTljZyIETn6Sw+bV8lyRJkjbAoC11cplUhqua+7D7Cw/kuxRJkrQJUvkuQNL6faN4K3af8r98lyFJkjaRQVvqxE6s2pGPv3p/vsuQJEltYNCWOqmdyrfkOy/9J99lSJKkNjJoS51Q317VXDrlTQqb6/NdiiRJaiODttTJFKQK+NVy6LdkVr5LkSRJm8GgLXUy3+m1Bbu8+2K+y5AkSZvJoC11Ih+t3JGPvGa/bEmSugODttRJ7FaxFd98yTuMSJLUXRi0pU6gf3ENv5r4CgUtjfkuRZIktRODtpRnRekiLl/SSJ/l8/JdiiRJakcGbSnPflAwlNEzXsl3GZIkdSktvapoKanJdxnrlcl3AVJP9snKnTjmhbvyXYYkSV1GTBfy8qCP8sV3D2RGXREfrl7EcVVT2D28zqDFL5BePjPfJa5m0JbyZGzl1nz9xX/nuwxJkrqMWYMP5dwFJ/L0hPLV8x5ZWMUjC6uAXYFT2aNyCSdWT2Ns6g2GLn2ezNJpbd5fLKqgoaQfy0tGtml7g7aUB4NL+vPLt54nHZvzXYokSZ3eypqd+UnLJ/nLO4M3uO4ziyt4ZvGOwI7AKexUvpyT+kxlr8xbDF/2IgVLJhGLq2ko7seKwhqWZqpZEKqZ01LBjKYKptSXMbG2N28sL2FZXQaWwOHVA4D/t8l1G7SlDlac7sXlC1ZQuXJhvkuRJKlTayobzN9Kz+D7U0YTY2hTGy8vLeXlpaOB0cCJpEMLzbUd8zFFg7bUgYrSRfy/9EC2mf1IvkuRJKnTioW9+W//T3LOlHEsmde+cbU5dty9QAzaUgc5omoHvjr5FQYuMmRLkrQuMaSYPPg4zpl9FG9MKMl3OZvNoC0lbKfyLfnGwoXs/Pw9+S5FkqSN0lJcw/Q+e9EQCundspzi5mUUNi2joHEZ6YalhPqlhJamdt3n4gF7ccGKj3PPxM59y75NkZegHUL4KvBZIAKvAJ8GBgI3AX2A54DTYowN+ahPag+DivtxbnMpR7w0Pt+lSJK0QfVVW/Na6V7ctnwnbp7dn+ZF6+9i0bewgUG9GhhQWE+/wjr6FdTRJ7WS4lQjLQRaYqCFFM0EIoHm1dNkl8XsspYYmNVYwg1TBnXMgXagDg/aIYTBwP8B28cYa0MItwAfA44ELo0x3hRC+ANwJvD7jq5P2ly9MyV8tmQLTnttPEVNdfkuR5KkdYqpDEv7juGpgj3484LteHJWxSZtP6+hkHkNhbxEaUIVdn356jqSAYpDCI1ACTALOBD4RG75dcCFGLTVhaRDmuMrt+dLb/+PmuVv5rscSZLeJxaVM7NmHP9p+RBXzdqSGVOL8l1St9bhQTvGOCOE8EtgGlAL3E+2q8jiGOOqzj7TgQ3fKFHqJPas3IbzZ77L1pPuzncpkrqpGFJQWEZzYSlNBWU0ZkppSPemNpX9WkExyyihkTRDmUP/hnfpvXwy6RVz8126NlMksKLfbjxZOI4r543mpWWl1BQ00qewgT4FjVQWNFKZbqAq00BFqp6yVD2lqXpKQx0lZL8aKORfK0fz19mDqV2Szvch9Rj56DpSBRwHbAEsBm4FDt+E7c8CzgIoqvGvMOXXFr0H8/XawH4vPJDvUiR1QbGojMbi/qzs1ZelmT4sDNXMiZVMb65gan0F0+uLmdtQwJz6IuY1FGQvT22iAUUN7F25mF1L5rF1Zg5DW6ZTXTeNoiWTCU1taFAdIoY0S/vtzmMFe/P7Odvx2rTeayyfVV/IrPrCPFWnjZWPriMHA5NjjPMAQgi3A+OAyhBCJndVewgwY10bxxivAq4CKB1ZGjumZGlNJZkSzuk1gk+89h8y7fypa0ndz5L+e/JUwR6rA3R21LneLFqSHXUuSbPrC7l9Tj9upx/ZATuyQojsWr6CseULGF00j+LQQFNM0UyaJgJNMU1TTNFEiqaYopEUTS1pGknRHFM05O5FXBBaKAjNFIaW1Y8LQjMFNFMQWsis/t5EhhZKQh3bLX+aXgvtYre2mCpgYf+9eDi1J7+btQ3vTC3Od0naTPkI2tOAPUMIJWT/Nj8IeBYYD5xE9s4jZwD/zENt0gYdVLU935r0CgMW35vvUiR1AbGwN59YeCavLeu94ZU7UIyB55eU8vySUmB4B+/9MA7ss4jPVb/Eh5aPp3DRhHZtvbl0INMqx/IWwxmcWsCAxhlU1E6lYOm7hJbGdt1XLKqgvnQwDZlSMs11ZJpWkmquJdW4ktC4ktBcv/7tM72Y128cDzCW380cxYzJvlvfneSjj/bTIYTbgOeBJuAFsleo7wZuCiH8KDfvTx1dm7Q+A4v7ckFDMfs/b8BepbFiBC+XH8Bflu7MtLpiCkMLRakWCkKkINVCYapl9VWuwtBCYaqZghApDC1kQjPV6TpGpBcwMM6hunE2JSunk14+ixBb8n1oUrt5rP/pvDahc4XszuChBVU8tGB/YH8OrVnIZ6teYJdl4ylcPGmT24oFJSys2Z3/pXfhlkWjeGh+Ncx//3pFqRY+VLGMMWUL2L5wHluE2fRrnE7Ziimkl80ksOYb5ZFAS+++1JUMYknhAOam+jE91vBOQzVv1Fbw4tIyZi8pXO+7EkWpFqoLm6guaKS6oJHKTCOVmQbK042kQuSm2UOYN6lgk49ZXUOIsev2vigdWRq3+MEW+S5D3VwmZPhkxfZ84bXxlDSsyHc5eddUPpRXKw7gz0t34445/dq9/eJ0MzuXLWen0iVsU7SQ4al59I9zqaqfRfHK6aRWzHvfi6HUWTWVD2PXhT9iWZPjw22so/rO5zOVL7DT0vEULJmyznUigbo+o3m9ZAz3rNyOm2YPZkXz5g2rXZZpYq/KJezSeyFLmot4bWUFLywtZUWTHxwUHD56AFeePua5GOOYTdnO//nSeuxUviXfnzObbSbdle9S8qq5dBCvVx3A9cs+xK2zB0CCNzGobU7z1OIKnlpcAQx73/KyTBO7lC9nu5IlbFW0mGGpBfSP86lqmkPv2tlkls/c4Fu1Ukf5U/FnDNmb6O55Ndw97xDgEI7vP5dPlT/PDkvGE1qamFY5lkead+Qvc0bwzoz27b+8rCnD/fP7cP/8Pu3arno2//dL61BWUMq5hUM46eX/kOqh3Riae/fnreoD+evyD/G32QOJ80O+SwKyL4aPLazksYWVrKtfaQiRrUtq2bF0KdsUL2FEZiGDw3y2WfQI6eUzE68vposM+gKyH4D8ydSt811Gl3bHnH7cMedwVt+cbB3dQaTOzKAtreWIqh34xtvPUrP89XyX0uFippiJA4/mryv24K+zBtK8YPPeis2HGANvrSjhrRUlwIDV88syR/DLEc9xyMIbSK1s/1frlpIaHqr+GN+bPoZ7+/+BijlPtfs+1HXEkOZbKz+x4RUldWsGbSlnaMkAvrsysPfz9+S7lA4XM714feCJnDfrQN6cUJLvchKxrCnD2RPH0qdwNy4d/hT7zr2RUL/591VrKa5hfM3H+PqUPVi0MPsr9Zi5Z/FgxVwKlmz6h7rUPUwYciL/nlCT7zIk5ZlBW91GIFCYLqQwVZD7ylCYKqAgpCls9VUQUhSGFIUECklRCAxpjpz22kMUNdXl+zA6VEwX8eagE/j67IN6zF0RFjQUcPqEfRncaw8uHfo4u8+5idCGD7m2FNfwcM1HOX/qHixYtOYdA6bV9uKzJefx56IL2iXMq2uJRRV8fsYR+S5DUidg0FbepUKKPkWV9C+soH+qmP6k6d/cQt/GekqaGilsaaKwuZGi5mYKmhsoam6isKmewuZGChvrKGpqoLCxjoJ2vjdqdxbThbw96ATOn3MQL08ozXc5eTGjrohTJhzEliV7c9mw8eww8zbCRvyh1VLch4drPrbOgN3aIwuq+OmQb/Otxu8SHNSoR7mv76eYNLFXvsuQ1AkYtJW4qsIKhvWqoX+6F/1jiv5NzfSvr2VA7RL6L5tP36UzybRMyXeZnVYk8ODQL5GhmR2aXqd60Yuk6ha3ra10IRMGHcc35hzCiz00YK/tnZXFHPP2kexY9mEuGXo/o2bcsc4BLVqK+/BIzUf5+tSx6w3YrV05fRg7bPl/HDPjV+1dtjqphqpRfGXS7vkuQ1InYdBWu8iEDENK+jGioIItyLBFfT1bLJvPFgumUrFyWr7L69IeHvoFPjthr9zUPoQQOaTPQo6smMpu4U0GLXmRzLLp620jpgqYOPg4vjn3UJ43YK/TK8t6c+iyExhbeRA/r7mHYTPuIsQWWoqrebTmY5w/dSzzNjJgt/bld8YwatRH2fbdmxOoWp3NrzOfpr6l632IWFIyHLBGm6Q4U8yokkGMSBezRVNki9plbLFkNkPnT7XrRgLeGnoKh004foPr7VS+nBOq32WvgrcZvvIVei18kxBbiKkMk3IB+9klZckX3I0c2GcRH6mayIXTdmFew+aN2laQijwz7HdUzf5vO1Wnzmj+wA8zZvLZ+S5DUgIcsEaJCAS2LRvOuHQZey+cwy7vvERBy1v5LqtHmDfoAI5+57iNWvflpaW8vHQ7YDvgOAb2auAjfWfyzLJqnplQnmid3VV2eOj26QLQ2BI4evZnGV85h8LFE9ulTXUuMVXA15Z8NN9lSOpkDNp6n5qiavYuGcxeK1ey1/RX6TP50XyX1OOsrNmJw6d/isaWtg0SM6uukCveHdG+RWmzzKgr4tP15/GXXheQqluU73LUzl4Z/FEenVCZ7zIkdTIGbVGYKmTX8hHs3VzAuLlT2XrySwRezHdZPVZT+TCOX/QVFmxmdwV1Pv9dVMGPBn+L7zV8d50fuExKJNBQtTWTSnflscZtWdxUyICCFfRNL6dPWE4FSylvWUpJ02KKGhdTUL+IVO2iDq2xK2spqeHsdw/OdxmSOiGDdg92cNVoTliyhN3ffZHiBt/O7gxaelXx2aZv8vaK4nyXooRcM2MoO2x5LifO+EWi+6mvGsWU0t14tHEbbpo7nHdmbfo5NbCogeHFtQztVcuBpdM4bMH1pGoXJlBtx4mpDEv7juGpgrHcsHAbTqmeyJEL/rxZx3VH1aeZtbCwHauU1F0YtHuoysIKLn7tMUrql+e7FOXEdBEXlnyHh2dW5bsUJexr7+zKqFGnsuO7N7Rbmw2VWzKlbDceb9qWm+aO4O02BOu1zaovZFZ9IU9Rwa0MYEivnblq6P1sN+PWLnVv8FhUwYy++/BA825cPWskM6YWrV726MIxDOm1Y5uPq7bPaM6ftHN7lyypmzBo91Cf7DWUkvpX8l2GciKBa/t9g+snD8p3KeogJ75zJE8Nn06fWY+0afuGypFMK9uV/zZtxy3zh/Pa7N4wu52LXMv0uiKOnHAMB/UZxy/L/tap76LSWLEFb5bvzT9W7sQNswZTv+SDb7m36rgO7LMPvyi/mT6zNv5zKT+Nn6I5ejs/Sevm7f16oLKCUu6bNoOyOoeG7iweGvolPjNh73yXoQ42oKiBR6p/QtGi9d/JJxKor96Wyb135vHGrbl13vBO0b3ovGET+Xz9tRQsmZzvUoghzfK+u/K/orH8ZcF2jF9Y3ea2vjJsEuc0XEvh4nfWu97MwYez9zunt3k/kroOb++njXZqyRaU1b2e7zKU8+bQjxqye6jZ9YWcVvtVbir+DqnaBavnx1SGldWjmVi8E4/Ub80tc4cwfWbRelrKj0umbcUf0v+P34x8kgPmXEdo6LiuaC3F1Syq3JG3M9vwRN1w/jl/INOmtc+w55dPG8lV6Qu5dItnOWzenwn1778oETPFfGn+Ce2yP0ndl0G7h+mdKeGTb3Xet3t7mrmDDuKYd47NdxnKo2cWl/ODQd/i/PLbeKtwNA/VbcVtcwYzb3rXuOvMiuYUn5kwjm1Ld+X3Q+5ixPR/Emjfd0pjuogV1dsztde2PNe0Jf9ePJgnF1VAgndJrG1O8/mJYxlRvDNXDr2PraffRojNq5c/M/BUnp/gIFCS1s+g3cN8vHRLKmrfzHcZAlbW7Mxh757R5ntlq/v4y8zB/IWv5LuMzfLm8hIOmHgKJ/bfnx8WXk/pvOfb1E5MZWgsH8as0h14ha14aNlQ7pvflxXv5qcf9JTaXhw24TgOrdmXn5Vm+6U3lw7i81P2y0s9kroWg3YPUpwp5vS3n8p3GSJ7r+zjFv0fixr9L6ju5fY5/bidr/PDLV7j1GXXkKpfQnOvahqKqqktqGR5upKlqXIWxjLmt5Qxq6mUmQ0lTK8vYdLKYmbUFhJXdr4/Pu+fX83988/hvGGHQ4BF8/2/K2nD/E3Rg3y0dBRVKxw+Pd9aelVxZvM3mdAJPswmJeX7k0fzg/BLYgywLN/VtJ9Lpm2Z7xIkdSEG7R6iV7qIMyY8k+8yerzm0kF8r+A8HpnlvbLV/cXY+a5MS1JHMmj3ECeVb0PNxLvyXUaPFdNFPDvoVM6asr/dRSRJ6iF8xe8BClOFfPqd5/JdRo81d9CBfHnhKTw9oTzfpUiSpA5k0O4BTqjYhn7v3J3vMnqchsotubzgTH47aUS+S5EkSXlg0O7mClIFfHbSS/kuo0eJRWU82O9TfHnSHtQ2p/NdjiRJyhODdjd3bMW2DPBqdoeIBKYOOZbPzz6GNyeU5LscSZKUZwbtbiwTMnx2yiv5LqNHWFmzMz9oOoNbJw7IdymSJKmTMGh3Y0dVbsuQSffku4xuraWkhtuqzuSbk3byVmaSJGkNBu1uKh3SnDXtjXyX0W3FojJe6P8Rzpm2P7MWFua7HEmS1AkZtLupwyu3Y5hXs9tdS3E1T9aczDfe3ZMZbxfluxxJktSJGbS7oVRIcdb0t/NdRrfS3Ls/46tP4ZtTx7BgUUG+y5EkSV2AQbsbOqRyO0ZO+ne+y+gWmsqHck/5KXx7yi6sWOCt+iRJ0sYzaHczgcDZMyblu4wur6FyS/5ecgoXThlN/dxUvsuRJEldkEG7mzmwcjtGTb4332V0qFjYm5XlWzGn10gmMpQ6MgwOi6iJC6hsnEtx3RwKVswmNNVusK3aPqO5oeAkfjJ1FM3RgC1JktrOoN3NfH72tHyXkJiYLqKuckvmFY9kUhjGS/WDeHRxX55fVkpcuuFb6w0vrmP70uVs3WspIwoXMzi1iL5xAZVN8wC4ruUILps2MunDkCRJPYRBuxvZv2o7tn3+vnyX0W4WD9ib1wt24JWGQTyxrC9PLK6kcUXb71U9tbYXU2t78W9q2rFKSZKkdTNodyNnz5mZ7xLaxcqaHbkkfpI/TRma71IkSZLazKDdTYyr3JYdXrg/32VslqbyodzQ+wwunLKdoyxKkqQuz6DdTXx+/rx8l9BmLb0qeajvaXxl8h6smOst9CRJUvdg0O4Gjq/akV2ev3uTt7tnyFdJ08xO8S36LX6R9Io5CVT3wWK6iFcGncIX3z2Q6RMcZVGSJHUvBu0ublBxP775+mObvF1dn+354sTdc1N7AmfwofJlHNfnXfbITGD4ylfptfBNQmxu13oBIoEZQ47k3HnH8uyEsnZvX5IkqTMwaHdhgcD/WxkorVu6ydveVPiR9817bmkZzy3dHtgeOI6+hY2c0G8W+xdPYtvGN6ha9BKhftP31dqS/ntyUd3HuH1iv81qR5IkqbMzaHdhn6jckT1euGuTt2ssH87FU7fd4HrzGgq4avowrmIYsD/p0MJBfRazTckSqlN1VKTrqEjVUkYtpaykJK6kuGUFRS0rKGxaTkHjCtKNSwkNy2koG8YfCk7n0qnep1qSJPUMBu0uakTvwZz76oNt2vbf5R+hce6m39WjOaa4f34191O96TtdvumbSJIkdWWOMd0FpUOaHy9eQa/GDQ8pvraWkhq+M2WX9i9KkiRJazBod0Fnlm/PjtNfbtO2j/c5mWVNvpEhSZKUNIN2F7Nd2XA+/3LbBqaJhaV8+9092rkiSZIkrYtBuwspTBVy8Zy5FLQ0tmn7l/ufwIw671ctSZLUEQzaXcg5vUcxas5bbdo2pgu5YPZ+7VyRJEmSPohBu4vYtWIrPvXKfW3eftLAo3ltWe92rEiSJEnrY9DuAoozxVz87iRSsaVN28eQ4qKFB7dzVZIkSVofg3YX8PWi4QxdMKXN288ZdDCPLqxst3okSZK0YQbtTm5c5bac8mrb7jKyyi+WH9FO1UiSJGljGbQ7sbKCUi6a9OpmtbF4wF78fU7/dqpIkiRJG8ug3YldkBpA/yUzN6uNK5uPbadqJEmStCkM2p3UIVWjOfrNhzarjdqaHfj9u8PbqSJJkiRtCoN2J9SnqIrvvfXMZrdzY+bEdqhGkiRJbWHQ7oR+0FxG1YoFm9VGY8UW/Hjq1u1UkSRJkjZVJt8F6D2DS/rz8XQfDnj53s1u667Sj9A8x7+jJEmS8sWgnWfVRVUcVjyEI+dPZ5fX/tcubTb37sd3p+7cLm1JkiSpbQzaedA7U8KBZSM5cvFC9nz7WTItL7Vr+49Wn8yKBel2bVOSJEmbxqDdQQpSBYwr34qjVtax/6Rn6NX4ZiL7iUVlXDBt90TaliRJ0sYzaCcoFVJ8qHxLjmwMHDLpWSreeSfxfb7Q70RmTShMfD+SJElaP4N2ArYtG85RlHL4tJcZMOnBDttvzPTigln7dtj+JEmS9MEM2u1kcEl/jizox1EzJ7Dl5MfyUsPEgcfw5oSSvOxbkiRJazJob4bKwgoO6z2Mo+bNZJfXniUQ81ZLDGm+P//AvO1fkiRJazJob6Je6SL2L9+Ko5YuY9yEpyloeSXfJQEwc9AhPPlORb7LkCRJUo5BeyOkQ5qxFVtxVF0LB016ht71E/Jd0hpiSPHTZUfkuwxJkiS1YtBej1GlwzgulHLU5BeomfRAvstZp5gu4uq+3+JfU/rmuxRJkiS1YtBeS3VRFUcWD+G42ZPZdvLj+S5nvWJRBRf1/g5/njIk36VIkiRpLQZtoDBVyP4VozhuyRL2fvuZdh+pMQnNpQP5At/h/pnV+S5FkiRJ69Cjg/Yu5VtyTFOGwyc9Q/k7E/Ndzkarr9qaj674Oi8uLc13KZIkSfoAPS5oDyzuy7GFAzh2+usMmzw+3+VssmX9xnDE3HOYXleU71IkSZK0Hj0maKdDmtMrtudLL/+Hwubn8l1Om8wedAiHTDuNZU095scmSZLUZfWIxDai92B+tGQlO79wd75LabO3h57MEROPozmm8l2KJEmSNkK3DtqpkOKTFaP5v1cepKipLt/ltNnjQ8/mkxM+nO8yJEmStAm6bdAe3nsQ/29JPbt24avYMZXh7wPO4+sTds53KZIkSdpEeQnaIYRK4GpgByACnwHeAm4GRgBTgFNijIs2te1USPGJih34yqsP0quxtr1K7nCxoITLqy7gskkj812KJEmS2iBfHX4vB+6NMW4L7Ay8AXwLeDDGOAp4MDe9SYaWDOCapj5884W7unTIbinuwzd6/4jLphmyJUmSuqoOv6IdQqgA9gM+BRBjbAAaQgjHAfvnVrsOeBj45ka1SeBjlTvw1dfGU9ywsr1L7lCNFSM4s/EbPDq7Mt+lSJIkaTPko+vIFsA84NoQws7Ac8BXgP4xxlm5dWYD/TemscEl/fl/K2D3LtwXGyAWlvJY/9P48pS9WNLYbbvOS5Ik9Rj5SHQZYDfgyzHGp0MIl7NWN5EYYwwhxHVtHEI4CzgLoKpvb26f8DolDSuSrjkxMaR5Z8jxfHHmkbw9oTjf5UiSJKmd5KOP9nRgeozx6dz0bWSD95wQwkCA3Pe569o4xnhVjHFMjHFMYa9+hPItOqToJCwcsA9n976cgyd8hLdXGLIlSZK6kw4P2jHG2cC7IYRtcrMOAl4H7gTOyM07A/jnhtpa2pxhl5nf4tGhXyCmCxOpNwn1VVtzSd+L2W3KF7l/fnW+y5EkSVIC8tUZ+MvADSGEQmAS8Gmyof+WEMKZwFTglI1pqL4lxekT9uWQmtFc3uuPlMx/KbGiN1dLSQ13Vn2K8yfvSmNLyHc5kiRJSlBegnaM8UVgzDoWHdTWNh+YX83OqW9w5ZZPcMCsPxE60UiQMdOLFwZ9jC9O3Z/ZC7vOlXdJkiS1Xb7uo52IxpbAZyaM49OFl7C87275LgeA6YOP5CPpyznx7UOZXW/IliRJ6im65X3kHl5Yxc6LvsYftnyGg2f/kdCU7OA1MZWhuXd/6ooHsKSgLwtSfZkZq7l/2Rbc/k6/RPctSZKkzqlbBm2A5pjicxP3ZJ/q7fh99Z8om/tsm9qJBFp696e2ZBBLC/sxP1XDrNiHqU2VTKir4I0Vpby1ojeNK+1zLUmSpPd026C9yuMLK9hl0blcseWzHD7nKkLj+0eOjASaywaxomQo8woHM40BvN3Ql5dWVvPU4koWLcjAgjwUL0mSpC6r2wdtyF7d/sLEPRhbuS2/6H83taGYqXEAbzXU8MKKav63pJxl83rEUyFJkqQO0qPS5dOLy9lv8cfzXYYkSZJ6gG511xFJkiSpszBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQnYpKAdQtgzhHBvCOHhEMLxCdUkSZIkdXmZ9S0MIQyIMc5uNetrwAlAAJ4G7kiuNEmSJKnrWm/QBv4QQnge+HmMsQ5YDJwEtABLE65NkiRJ6rLW23Ukxng88AJwVwjhdOBcoAjoAxyfcG2SJElSl7XBPtoxxn8BhwEVwD+At2OMv44xzku6OEmSJKmrWm/QDiEcG0IYD9wLvAp8FDguhHBTCGHLjihQkiRJ6oo21Ef7R8AeQDFwX4xxD+C8EMIo4GLgYwnXJ0mSJHVJGwraS4ATgRJg7qqZMcYJGLIlSZKkD7ShPtonkP3gYwb4RPLlSJIkSd3Deq9oxxjnA7/poFokSZKkbsMh2CVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpATkLWiHENIhhBdCCHflprcIITwdQpgYQrg5hFCYr9okSZKkzZXPK9pfAd5oNf0z4NIY41bAIuDMvFQlSZIktYO8BO0QwhDgKODq3HQADgRuy61yHXB8PmqTJEmS2kO+rmhfBnwDaMlN9wEWxxibctPTgcF5qEuSJElqFx0etEMIRwNzY4zPtXH7s0IIz4YQnm1asaSdq5MkSZLaRyYP+xwHHBtCOBLoBZQDlwOVIYRM7qr2EGDGujaOMV4FXAVQMnjr2DElS5IkSZumw69oxxi/HWMcEmMcAXwMeCjGeCowHjgpt9oZwD87ujZJkiSpvXSm+2h/E/haCGEi2T7bf8pzPZIkSVKb5aPryGoxxoeBh3OPJwF75LMeSZIkqb10pivakiRJUrdh0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEtDhQTuEMDSEMD6E8HoI4bUQwldy86tDCA+EECbkvld1dG2SJElSe8nHFe0m4LwY4/bAnsA5IYTtgW8BD8YYRwEP5qYlSZKkLqnDg3aMcVaM8fnc42XAG8Bg4Djgutxq1wHHd3RtkiRJUnvJax/tEMIIYFfgaaB/jHFWbtFsoH++6pIkSZI2V96CdgihFPg7cG6McWnrZTHGCMQP2O6sEMKzIYRnm1Ys6YBKJUmSpE2Xl6AdQiggG7JviDHenps9J4QwMLd8IDB3XdvGGK+KMY6JMY7J9K7omIIlSZKkTZSPu44E4E/AGzHGX7VadCdwRu7xGcA/O7o2SZIkqb1k8rDPccBpwCshhBdz8y4AfgrcEkI4E5gKnJKH2iRJkqR20eFBO8b4OBA+YPFBHVmLJElSV1BelOLLY6sYXllA+MAYpc0ViUxd3Mhvnl7E0vqWzW4vH1e0JUmStAm+PLaK3bYcRKakjGwvXCUhxkifPkv5MnDxows2uz2HYJckSerkhlcWGLI7QAiBTEk5wysL2qU9g7YkSVInFwiG7A4SQmi37jkGbUmSJHVZzz/9BCcctBenHLYvdbW1H7jemScfzWsvvdCBldlHW5Ikqcs59or/tmt7d35pXLu215HuvuNWzjznXI4+8aP5LuV9vKItSZKk9Vq5cgVfOuMUTj50H048aC/uvfN2jthrJxYtzH5g8LWXXuDMk4/OrrtiOd/72jl85OC9OemQcfznnjsB+O/4//DRIz7MyYfuw+c+dtzqdr9/3pf4xNEHccrh+zH+vnsAmPjWG9l5h+3LSYeMY+rkd9ZZw+1/u577/3UHv/3lj/n2lz/H/558nC996r3A/ePvns8/b7mxI5+qNXhFW5IkSev1xMMP0rf/QK647hYAli1dwuU/uXCd6155+S8oKy/n7/95AoClixezcMF8LvrmV7jmtnsYMmw4SxYtAuDqX1/CHuP25YeXXMHSJUs49ZiDGLvvh7n1r9dy6plnc9QJp9DY0EBzczOPj3/gfTWUlVfwwjNPsd/Bh3HIUcfxvycfT/7J2ARe0ZYkSdJ6bbXt9jz12Hgu/fEPeP7pJygrr/jAdZ9+/BE+esZnV0+XV1by8vP/40Nj92bIsOEAVFRVAfDko+O55reXccph+/LZU46mob6O2TOms/Nuu3P1FZdyze8uY+aMd+lVXLxJNXQWXtGWJEnSeo0YuRU33fMIj42/nyt+cTF77PNh0ukMLS3ZQV3q6+va1G4k8qurrmfElqPWmD9y1DbsuOsYHn3ofr50+il896eXMnbcfu+r4fPnfmON7dLpNLHlvYFmGurr21RXe/GKtiRJktZr7uxZ9Cou5ugTP8oZn/8yb77yEoOGDuONV14E4MF7/rV63T333Z+br7t69fTSxYvZabfdee7pJ5g+bSrA6q4je+93IDdeexUxRgDeePVlAKZPncKQ4SM49TNns/+hRzLhjdfWWcPaBg0ZyqQJb9FQX8/SJUt4+r+PJPJ8bCyvaEuSJGm9Jrz5Opde/H1SqRSZTAHf+fEl1NfV8oPz/4/f/uLHjNlrn9XrnvV/X+fH3z2fEw/ai3Q6zdlf/SYHH3EM3//ZZXztrNOILS1U1/Tlyhv/wVlfOZ+fX/RtTjpkHC0xMnjoMK74883cd9c/uOv2WyjIZOjTtz+f/dLXePWl599Xw9oGDBrCoUcfz0cO3ptBQ4ez7eidOvJpep+w6i+Irqhk8Nax32mX5rsMSZKkRP3x2IH0HzYy32X0GHOmTeJzd85aPX346AFcefqY52KMYzalHbuOSJIkSQkwaEuSJEkJMGhLkiRJCTBoS5IkSQkwaEuSJEkJMGhLkiRJCTBoS5IkabM9fP89/Om3ydx2+Z+33MiPv3t+Im0nyQFrJEmSupidrh7eru29/Nmpm93G/oceyf6HHtkO1XQfBm1JkiSt14x3p/HF005ip13H8OJzz7DDzrty3Cmn8vtf/YSF8+fz419fxaQJb/Hayy9wwY9+wf133cEfLv0Z6XSa0rJyrv37PUx86w2+f96XaGpsoKWlhUuuup7hW2zJXbffzI3XXEVTYwM77PohvnPxJaTTae64+Qb+9NtLKSuvYJvtd6CgsDDfT8MmM2hLkiRpg96dMolf/v5aLtrmCj5x9IHcc8dt/Pn2e3n4/n/zpyt+xQGHHbV63Ssv+zm//+vf6T9wEEuXLAHg1r9ey6lnns1RJ5xCY0MDzc3NTJrwFvf96x9c9497KSgo4OILzuOef9zKnvvuz+9/9RP+ds/DlJWV89mPHsM2eR5OvS0M2pIkSdqgwUOHM2q70QBsufW2jN1nP0IIjNp2e2ZOn7bGurvsPpbvn3cOhx59PAcdcQwAO++2O3+84lfMmTWTg444huFbbMnT/32EN15+iVOPPhCAuro6qmv68soLzzFmr32o7lMDwKHHnMjUSRM78Gjbh0FbkiRJG9S660YqpCgsLAIgpFI0NTWtse73fnIpL7/wLI89eD8fP3J//nbPwxx5wsnsuOsYHn3ofr50+il896eXEiMcc/LH+Mq3frDG9g/de3fyB9QBvOuIJEmS2tW7Uyaz065jOOfrF1BVXcPsmTOYPnUKQ4aP4NTPnM3+hx7JhDdeY+y4/fjP3XeyYP48AJYsWsTM6dPYcdcP8dxT/2XxooU0NjbywF135PeA2sgr2pIkSWpXv7r4+0yb/A4xRsbu82G22X4HrvndZdx1+y0UZDL06dufz37pa1RUVXHO+d/hC6eeSEtLC5mCAi740S/Yabfd+fxXv8Vpxx1KWXkF247eMd+H1CYhxpjvGtqsZPDWsd9pydyvUZIkqbP447ED6T9sZL7L6DHmTJvE5+6ctXr68NEDuPL0Mc/FGMdsSjt2HZEkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSdIm+f2vfsp1f/hNvsvgoXvv5p2338x3GR/IAWskSZK6mFMf3Ldd27vhoMfatb3WmpqayGSSiZzj77ub/Q4+jC233jaR9jeXQVuSJEkb9Mdf/5I7b7uJ6poaBgwczPY77sKbr73Cj779NepqVzJk+Bb88JdXUF5ZyZknH802o3fkhWee4vDjPsKj/7mXbUfvxPPPPElt7Up+dOnv+dNvL2Xim69z2DEn8KVvfBeAc888ldmzZlBfX8+pnzmbk079FAB7bjOEUz9zNo8+eB9FvYq5/E838O7UyTz8wL959un/8sdf/5JLrryeoSO2yOMz9H52HZEkSdJ6vf7yi9x75+3cct+j/Pa6W3jtpRcA+O65n+fcb1/IbQ/8l1Hbbs8fLvvZ6m0aGxr42z3jOePsLwGQKSzgb/eM5+RPfppzzzyVC370C/7+nyf4561/Y/GihQBc9MsruOmeh/nbXQ9x4zVXrp5fu3IFO+42hlvvf5wPjd2Lv994PbuMGcv+hxzB177zQ26577FOF7LBoC1JkqQNeP6ZJznw8KMpLi6htKycDx9yBLW1K1m2dAlj9hoHwLEnfZznnn5i9TaHHXPiGm3sf8gRAGy17fZsuc229O0/gMKiIoYMG87smTMAuPHaKzn50H047bhDmDNrBtMmvwNAQWEhHz74cAC223EXZk6flvgxtwe7jkiSJKndFZeUrDFdWFgEQCqVoqCwcPX8VCpFc1MT/3vycZ56/GGu/+f9FBeXcObJR1NfXw9AJlNACAGAdDpNc1NTBx3F5vGKtiRJktbrQ2P3Zvx9d1NXW8uK5ct49D/3UlxcQnlFJc/nrmLfdfvNjBk7rs37WL50KeUVlRQXlzB54tu8/MKzG9ympLSUFcuXt3mfSTNoS5Ikab2223FnDjvmBE4+bF++ePrJjN55VwD+36W/51cX/4CTDhnHW6+9wtnnfqPN+xi3/0E0NzVx/AFjufwnF7HTrmM2uM3hx57IdVf+hlMO3493p0xu876TEmKM+a6hzUoGbx37nXZpvsuQJElK1B+PHUj/YSPzXUaPMWfaJD5356zV04ePHsCVp495Lsa44fTfile0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkqQEGLQlSZKkBBi0JUmS1CZ7bjMkkXa/99Uv8sDd/0yk7VXOPPloXnvphUT34RDskiRJXUzBwXu1a3uN/3myXdtTlkFbkiRJG3Tumacye9YM6uvrOfUzZ3PSqZ8C4BcXXsATj42npm8/fvbbP1Hdp4a/33gdf7/hOhobGxg6YiQXX/4HiotL+N5Xv0jvsjJef/kF5s+by1cvuIhDjjqOGCM/+d43eOqxhxkwaDAFBQWr9/v0449wyY++R3NTE6N33o3v/vgSCouKOGKvnTjmpI/zyH/upamxkV/+4c9ssdXWrFy5gp9+75tMfOsNmpoa+cJXv8UBhx1JXW0t3z/vHN5+4zVGbDmKurq6xJ8zu45IkiRpgy765RXcdM/D/O2uh7jxmitZvGghtStXsP3Ou/CPB5/kQ3uO4w+X/gyAg444hhvvfohb73+ckVttzT9u+uvqdubPncOfb7+X31x7E5f/5CIAHrz3LqZOmsg/HnqKH136e1567hkA6uvq+N7XvsjPf3cNf//PEzQ3N3HLX65Z3VZldR9u/vcjnHLaZ7juyisAuPrXl7DHuH258a4Hufrmf/Gri7/PypUruOUv19CruIQ7xj/NF8/7Fm+88mLiz5lBW5IkSRt047VXcvKh+3DacYcwZ9YMpk1+h1QqxWHHnAjA0Secwov/ewqAiW++wadOPIKPHLw3d99xK++8/cbqdg447EhSqRRbbr0tC+bPA+D5p5/g8GM/Qjqdpt+Agey+934ATJk0gcFDhzNi5FYAHHvSx3nu6SdWt3XQ4UcDsN1OuzBz+jQAnnx0PNf89jJOOWxfPnvK0TTU1zF7xnSef/oJjjrxFAC23m4HRm03OsmnC7DriCRJkjbgf08+zlOPP8z1/7yf4uISzjz5aOrr69+/YggAfO+8L3LZ1X9lm+135J+33MizTz6+epXCwqLVj2OMm1VXYVG2rXQqTXNTU7ZNIr+66npGbDlqs9puD17RliRJ0notX7qU8opKiotLmDzxbV5+4VkAWlpaVt8d5J47bmPX3fcEYOXy5dT0G0BjYyP33HHrBtvfbeze3Pevf9Dc3My8ObP535OPATBi5ChmTp/GtMmTALjr7zczZs9x621r7/0O5MZrr1od4t949eXV+/j3HbcBMOHN15nwxmub+jRsMq9oS5Ikab3G7X8Qt/71Go4/YCwjRm7FTruOAaC4pDevvvg8f/z1L6mu6cvPf5ftP33O1y/gk8ceTFV1DTvu+iFWLl++3vYPOvxonvnvo5xw4J4MHDyEnXfbHYCiXr344SW/5etf+NTqD0Oe/MlPr7ets75yPj+/6NucdMg4WmJk8NBhXPHnmznltM/w/fPO4fgDxrLFVluz3Y67bP4TswFhcy/Z51PJ4K1jv9MuzXcZkiRJifrjsQPpP2xkvsvoMeZMm8Tn7py1evrw0QO48vQxz8UYx2xKO3YdkSRJkhJg0JYkSZISYNCWJEmSEmDQliRJkhJg0JYkSZISYNCWJEmSEmDQliRJUpvsuc2QRNu/+jeXJNp+0hywRpIkqYt57EfPt2t7+353t3Ztr71cfcWlfPbL5+W7jDYzaEuSJGmDzj3zVGbPmkF9fT2nfuZsTjr1UwD84sILeOKx8dT07cfPfvsnqvvUcMM1V3LbX68lnU4zctQ2/Px317By5Qp++r1vMvGtN2hqauQLX/0WBxx2JP+85UYefuDf1NXWMn3qZA48/Ci++p0fctlPLqS+rpZTDtuXLbfelp/85o/5fQLawKAtSZKkDbrol1dQUVVFXW0tnzj6QA4+8lhqV65g+5134fwLf8wfLvs5f7j0Z1zwo19w7W8v454nXqSwqIilS5YAcPWvL2GPcfvyw0uuYOmSJZx6zEGM3ffDALz1+ivc/O9HKCws4rj9d+fjnz6Lc799ITf9+Wpuue+xfB72ZjFoS5IkaYNuvPZKHrr3LgDmzJrBtMnvkEqlOOyYEwE4+oRT+NpZpwEwarvRfPv/zuKAw47kwMOOAuDJR8fz8AP/5vorrwCgob6O2TOmAzB23IcpK68AYOSobZg5/V0GDEq2/3dHMGhLkiRpvf735OM89fjDXP/P+ykuLuHMk4+mvr7+/SuGAMAV193Mc08/wSMP3MvVv/kVtz3wXyKRX111PSO2HLXGJq+88BwFhYWrp1PpNM3NzYkeT0fxriOSJElar+VLl1JeUUlxcQmTJ77Nyy88C0BLSwsP3P1PAO654zZ23X1PWlpamD1zBnvsvS/nXnAhy5cuZeWKFey934HceO1VxBgBeOPVlze430xBhsbGxuQOLGFe0ZYkSdJ6jdv/IG796zUcf8BYRozcip12HQNAcUlvXn3xef74619SXdOXn//uGpqbm7ngK2exfOlSYox8/DNnUV5RwVlfOZ+fX/RtTjpkHC0xMnjoMK74883r3e9HPnEGJx+6D9vtsFOX/DBkWPVXRVdUMnjr2O+0S/NdhiRJUqL+eOxA+g8bme8yeow50ybxuTtnrZ4+fPQArjx9zHMxxjGb0o5dRyRJkqQEGLQlSZKkBBi0JUmSpAQYtCVJkjq5SKQrf66uK4kxEmmf59qgLUmS1MlNXdxI08qlhu2ExRhpWrmUqYvb55aC3t5PkiSpk/vN04v4MjC8cj6BkO9yuq1IZOriRn7z9KJ2aa9TBe0QwuHA5UAauDrG+NM8lyRJkpR3S+tbuPjRBfkuQ5uo03QdCSGkgd8CRwDbAx8PIWyf36okSZKktuk0QRvYA5gYY5wUY2wAbgKOy3NNkiRJUpt0pq4jg4F3W01PB8aub4PigjQ7DC5PtChJkiT1bMP7lLRpu84UtDdKCOEs4KzcZP3d/7ffq/msR51SDTA/30Wo0/G80Lp4XmhdPC+0hruz37bZ1O06U9CeAQxtNT0kN28NMcargKsAQgjPbuqY8+r+PC+0Lp4XWhfPC62L54XWJYTw7KZu05n6aP8PGBVC2CKEUAh8DLgzzzVJkiRJbdJprmjHGJtCCF8C7iN7e79rYoyv5bksSZIkqU06TdAGiDHeA9yzCZtclVQt6tI8L7QunhdaF88LrYvnhdZlk8+L4FCekiRJUvvrTH20JUmSpG6jywbtEMLhIYS3QggTQwjfync9yo8QwjUhhLkhhFdbzasOITwQQpiQ+16VzxrVsUIIQ0MI40MIr4cQXgshfCU33/OiBwsh9AohPBNCeCl3XlyUm79FCOHp3GvJzbkP46uHCSGkQwgvhBDuyk17XvRwIYQpIYRXQggvrrrbSFteR7pk0Ha4drXyZ+DwteZ9C3gwxjgKeDA3rZ6jCTgvxrg9sCdwTu73g+dFz1YPHBhj3BnYBTg8hLAn8DPg0hjjVsAi4Mz8lag8+grwRqtpzwsBHBBj3KXVrR43+XWkSwZtHK5dOTHGR4GFa80+Drgu9/g64PiOrEn5FWOcFWN8Pvd4GdkXz8F4XvRoMWt5brIg9xWBA4HbcvM9L3qgEMIQ4Cjg6tx0wPNC67bJryNdNWiva7j2wXmqRZ1P/xjjrNzj2UD/fBaj/AkhjAB2BZ7G86LHy3UPeBGYCzwAvAMsjjE25VbxtaRnugz4BtCSm+6D54Wyf4jfH0J4LjcqObThdaRT3d5Pam8xxhhC8NY6PVAIoRT4O3BujHFp9iJVludFzxRjbAZ2CSFUAv8Ats1vRcq3EMLRwNwY43MhhP3zXI46l31ijDNCCP2AB0IIb7ZeuLGvI131ivZGDdeuHmtOCGEgQO773DzXow4WQiggG7JviDHenpvteSEAYoyLgfHAXkBlCGHVRSdfS3qeccCxIYQpZLuhHghcjudFjxdjnJH7PpfsH+Z70IbXka4atB2uXetzJ3BG7vEZwD/zWIs6WK5/5Z+AN2KMv2q1yPOiBwsh9M1dySaEUAwcQrb//njgpNxqnhc9TIzx2zHGITHGEWSzxEMxxlPxvOjRQgi9Qwhlqx4DhwKv0obXkS47YE0I4Uiy/apWDdd+cX4rUj6EEP4G7A/UAHOAHwB3ALcAw4CpwCkxxrU/MKluKoSwD/AY8Arv9bm8gGw/bc+LHiqEsBPZDy+lyV5kuiXG+MMQwkiyVzKrgReAT8YY6/NXqfIl13Xk6zHGoz0verbcz/8fuckMcGOM8eIQQh828XWkywZtSZIkqTPrql1HJEmSpE7NoC1JkiQlwKAtSZIkJcCgLUmSJCXAoC1JkiQlwKAtSVpDCGH/EMJd+a5Dkro6g7YkSZKUAIO2JHVRIYRPhhCeCSG8GEK4MoSQDiEsDyFcGkJ4LYTwYAihb27dXUIIT4UQXg4h/COEUJWbv1UI4T8hhJdCCM+HELbMNV8aQrgthPBmCOGG3IibkqRNYNCWpC4ohLAd8FFgXIxxF6AZOBXoDTwbYxwNPEJ2tFSA64Fvxhh3Ijtq5qr5NwC/jTHuDOwNzMrN3xU4F9geGAmMS/iQJKnbyeS7AElSmxwEfAj4X+5iczEwl+yw8zfn1vkrcHsIoQKojDE+kpt/HXBrCKEMGBxj/AdAjLEOINfeMzHG6bnpF4ERwOOJH5UkdSMGbUnqmgJwXYzx22vMDOF7a60X29h+favHzfh6IUmbzK4jktQ1PQicFELoBxBCqA4hDCf7e/2k3DqfAB6PMS4BFoUQ9s3NPw14JMa4DJgeQjg+10ZRCKGkIw9Ckrozr1BIUhcUY3w9hPBd4P4QQgpoBM4BVgB75JbNJduPG+AM4A+5ID0J+HRu/mnAlSGEH+baOLkDD0OSurUQY1vfVZQkdTYhhOUxxtJ81yFJsuuIJEmSlAivaEuSJEkJ8Iq2JEmSlACDtiRJkpQAg7YkSZKUAIO2JEmSlACDtiRJkpQAg7YkSZKUgP8PaaNWOj2f8QUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9\" ><caption>Participation rates (%) first 10 epochs</caption><thead>    <tr>        <th class=\"col_heading level0 col0\" >epoch</th>        <th class=\"col_heading level0 col1\" >All data</th>        <th class=\"col_heading level0 col2\" >Excluding absent</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row0_col1\" class=\"data row0 col1\" >59.8</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row0_col2\" class=\"data row0 col2\" >62.4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row1_col1\" class=\"data row1 col1\" >57.7</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row1_col2\" class=\"data row1 col2\" >60.3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row2_col1\" class=\"data row2 col1\" >58.2</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row2_col2\" class=\"data row2 col2\" >60.7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row3_col1\" class=\"data row3 col1\" >58.5</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row3_col2\" class=\"data row3 col2\" >61.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row4_col1\" class=\"data row4 col1\" >60.2</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row4_col2\" class=\"data row4 col2\" >62.9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row5_col0\" class=\"data row5 col0\" >5</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row5_col1\" class=\"data row5 col1\" >63.0</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row5_col2\" class=\"data row5 col2\" >65.8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row6_col0\" class=\"data row6 col0\" >6</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row6_col1\" class=\"data row6 col1\" >69.6</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row6_col2\" class=\"data row6 col2\" >72.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row7_col0\" class=\"data row7 col0\" >7</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row7_col1\" class=\"data row7 col1\" >68.9</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row7_col2\" class=\"data row7 col2\" >71.9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row8_col0\" class=\"data row8 col0\" >8</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row8_col1\" class=\"data row8 col1\" >68.7</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row8_col2\" class=\"data row8 col2\" >71.7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row9_col0\" class=\"data row9 col0\" >9</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row9_col1\" class=\"data row9 col1\" >70.4</td>\n",
       "                        <td id=\"T_07f7566e_12d3_11eb_b89d_23b5274849f9row9_col2\" class=\"data row9 col2\" >73.5</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fc9da011550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw stacked plot for the first 100 epochs - but excluding absent validators\n",
    "\n",
    "participation_data = pd.DataFrame({'successful': success_count,\n",
    "                                   'missed'    : missed_count,\n",
    "                                   'dormant'   : dormant_count,\n",
    "                                   'abandoned' : abandoned_count,\n",
    "                                   'absent'    : absent_count})\n",
    "\n",
    "participation_data_100 = participation_data.iloc[range(0,51)]\n",
    "\n",
    "perc_participation_data_100 = participation_data_100.divide(participation_data.sum(axis=1), axis=0) * 100\n",
    "\n",
    "fig=plt.figure(figsize=(12,8))\n",
    "plt.stackplot(range(len(perc_participation_data_100.index)),\n",
    "              perc_participation_data_100['successful'],\n",
    "              perc_participation_data_100['missed'],\n",
    "              perc_participation_data_100['dormant'],\n",
    "              perc_participation_data_100['abandoned'],\n",
    "              perc_participation_data_100['absent'],\n",
    "              labels=['successful','missed','dormant','abandoned', 'absent'])\n",
    "plt.legend(loc='lower right')\n",
    "plt.margins(0,0)\n",
    "plt.title('Validator participation at genesis')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('%')\n",
    "plt.show()\n",
    "\n",
    "first_10 = perc_participation_data['successful'].iloc[range(0,10)]\n",
    "first_10_less_absent = perc_participation_data[['successful','missed','dormant','abandoned']].iloc[range(0,10)]\n",
    "first_10_less_absent = first_10_less_absent.divide(first_10_less_absent.sum(axis=1), axis=0) * 100\n",
    "comp = pd.concat([first_10, first_10_less_absent['successful']], axis=1)\n",
    "comp.index.names = ['epoch']\n",
    "comp.columns = ['All data','Excluding absent']\n",
    "\n",
    "display(comp.reset_index().head(10).style\n",
    "        .hide_index()\n",
    "        .format({\"All data\": \"{:,.1f}\", \"Excluding absent\": \"{:,.1f}\"})\n",
    "        .set_caption('Participation rates (%) first 10 epochs'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation delays\n",
    "An interesting question arises from the delay in reaching quorum after genesis — do new validators always struggle to join the network, or is this an issue which occurred at genesis and was less important once the network was established? We can look at this by counting how *late* each validator is with its first attestation. Genesis validators are expected to attest in epoch 0, while later validators should first attest during their `activation_epoch`.\n",
    "\n",
    "As we saw above, from the genesis set, 60% of validators attested in the first epoch, and the pattern after that is messy, with larger groups of validators not attesting until epochs 5-6, and then epoch 14. This may reflect less-stable network conditions during the genesis period as many new nodes attempted to sync.\n",
    "\n",
    "By comparison, looking at validators who activated from epoch 5000, over 80% successfully attested in their first active epoch, with the number of epochs late dropping off rapidly from less than 2% at 1-epoch late. There was however a larger number of post-5000 validators who were more than 50 epochs late — 9.7% as compared with 2.1%).\n",
    "\n",
    "This difference could be due to the fact that some later-joining validators on becoming aware that they might have to wait in a validation queue for several days may not have bothered to sync their nodes until they were already at the front of the queue (at a less certain time in the future), as compared with the genesis set who were working to a widely publicised gensis time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genesis validators\n",
      "------------------\n",
      "Number of validators: 20084 (of which 95.8% non-absent)\n",
      "59.8% of non-absent genesis validators attested successfully in their first active epoch.\n",
      "2.1% of non-absent genesis validators first attested more than 50 epochs late.\n",
      "\n",
      "Validators activated since epoch 5000\n",
      "-------------------------------------\n",
      "Number of validators: 40390 (of which 96.5% non-absent)\n",
      "82.3% of non-absent validators attested successfully in their first active epoch.\n",
      "9.7% of non-absent validators first attested more than 50 epochs late.\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate and plot first attestation delay\n",
    "\n",
    "genesis_delay = []\n",
    "post5000_delay = []\n",
    "for validator in validators:\n",
    "    delay = validator['first_attestation'] - validator['activation_epoch']\n",
    "    if validator['activation_epoch'] == 0:\n",
    "        genesis_delay.append(delay)\n",
    "    else:\n",
    "        genesis_delay.append(None)\n",
    "    if validator['activation_epoch'] >= 5000:\n",
    "        post5000_delay.append(delay)\n",
    "    else:\n",
    "        post5000_delay.append(None)\n",
    "        \n",
    "delay_data = pd.DataFrame({'genesis':genesis_delay, 'post5000':post5000_delay})\n",
    "\n",
    "genesis_validators   = [v for v in validators if v['activation_epoch'] == 0]\n",
    "n_genesis_validators = len(genesis_validators)\n",
    "n_genesis_live       = sum([v['first_attestation'] != -1 for v in genesis_validators])\n",
    "n_genesis_prompt     = sum([v['first_attestation'] ==  0 for v in genesis_validators])\n",
    "n_genesis_late       = sum([v['first_attestation'] >  50 for v in genesis_validators])\n",
    "\n",
    "print(\"Genesis validators\")\n",
    "print(\"------------------\")\n",
    "print(f\"Number of validators: {n_genesis_validators:,} \"\n",
    "      f\"(of which {100*n_genesis_live/n_genesis_validators:.1f}% non-absent)\\n\"\n",
    "      f\"{100*n_genesis_prompt/n_genesis_validators:.1f}% \"\n",
    "      f\"of non-absent genesis validators attested successfully in their first active epoch.\\n\"\n",
    "      f\"{100*n_genesis_late/n_genesis_validators:.1f}% \"\n",
    "      f\"of non-absent genesis validators first attested more than 50 epochs late.\\n\")\n",
    "\n",
    "post5000_validators   = [v for v in validators if v['activation_epoch'] >= 5000]\n",
    "n_post5000_validators = len(post5000_validators)\n",
    "n_post5000_live       = sum([v['first_attestation'] != -1 for v in post5000_validators])\n",
    "n_post5000_prompt     = sum([v['first_attestation'] == v['activation_epoch'] for v in post5000_validators])\n",
    "n_post5000_late       = sum([v['first_attestation']  - v['activation_epoch'] > 50 for v in post5000_validators])\n",
    "\n",
    "\n",
    "print(\"Validators activated since epoch 5000\")\n",
    "print(\"-------------------------------------\")\n",
    "print(f\"Number of validators: {n_post5000_validators:,} \"\n",
    "      f\"(of which {100*n_post5000_live/n_post5000_validators:.1f}% non-absent)\\n\"\n",
    "      f\"{100*n_post5000_prompt/n_post5000_validators:.1f}% \"\n",
    "      f\"of non-absent validators attested successfully in their first active epoch.\\n\"\n",
    "      f\"{100*n_post5000_late/n_post5000_live:.1f}% \"\n",
    "      f\"of non-absent validators first attested more than 50 epochs late.\\n\")\n",
    "\n",
    "fig, axs = plt.subplots(2, figsize=(12,9))\n",
    "fig.suptitle('Validator first attestation delay (excluding zero-delay)')\n",
    "\n",
    "delay_data['genesis'].plot.hist(density=True, bins=50, range=(0,50), ax=axs[0])\n",
    "axs[0].set_title('genesis validators')\n",
    "axs[0].set_xlabel('')\n",
    "axs[0].set_ylabel('proportion of validators')\n",
    "axs[0].set_xlim(1,50)\n",
    "axs[0].set_ylim(0,0.07)\n",
    "axs[0].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0))\n",
    "\n",
    "delay_data['post5000'].plot.hist(density=True, bins=50, range=(0,50), ax=axs[1])\n",
    "axs[1].set_title('validators activated since epoch 5000')\n",
    "axs[1].set_xlabel('first attestation delay (epochs)')\n",
    "axs[1].set_ylabel('proportion of validators')\n",
    "axs[1].set_xlim(1,50)\n",
    "axs[1].set_ylim(0,0.07)\n",
    "axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1, decimals=0));"
   ]
  },
  {
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    "# Conclusions\n",
    "Much of the analysis conducted so far rests on an important assumption — that validators who were dormant, absent or abandoned were so because the people responsible for them weren't properly incentivised. As a testnet with no value at stake, Medalla relies to a significant extent on participants acting non-maliciously, and on being reliable for the good of the network. This was always going to be a significant weakness of Medalla.\n",
    "\n",
    "Without evidence to support an alternative explanation for this validator behaviour (such as significant numbers of users complaining they are unable to join or remain synchronised to the network), it seems reasonable to conclude that dormant/absent/abandoned validators are largely the result of apathy resulting from a lack of real-value incentivsiation. It therefore makes sense to try and exclude these types of validators from our analysis, since very few such validators would be expected to exist on a properly incentivised network.\n",
    "\n",
    "With this in mind, this analysis seems to show that beacon chain protocol and the clients that implement it are able to achieve consistently high participation rates, which we should expect will be even higher once real value is on the line. It seems likely that this difference will be enough to push genesis participation up from 60% to the 66% required for immediate finality, recent results from Zinken testnet are encouraging in this regard. \n",
    "\n",
    "Still, the genesis period remains a period of greater risk, and every effort should be made to ensure the genesis validators are ready before proceeding. For example, last-minute client releases should be considered as potential grounds for \"stopping the timer\" on genesis delay. To prevent this from being necessary, all clients should ensure they have their mainnet configurations released before the deposit contract goes live, and should avoid new releases in between deposit contract launch and genesis unless critical.\n",
    "\n",
    "Overall, this data is encouraging from the perspective of demonstrating the feasibility of a beacon chain under mainnet-like loads, at least in terms of numbers of validators, can run smoothly. However, it's important to remember that the beacon chain in its initial form does not support any data or execution shards, which will add additional load to the protocol in future. "
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