diff --git a/in-progress/8757-fees/design.md b/in-progress/8757-fees/design.md
index 4feddc2..398087e 100644
--- a/in-progress/8757-fees/design.md
+++ b/in-progress/8757-fees/design.md
@@ -58,6 +58,24 @@ They then specify a max fee per mana (and max priority fee) they are willing to
 
 The transaction can only be included if the specified max fee per mana is greater than the L2 base fee, barring caveats such as sufficient balance, etc.
 
+
+## Protocol Defined Constants
+
+- `OVERHEAD_MANA_PER_TX` - the overhead cost in mana for a transaction (e.g. 21_000)
+- `TARGET_MANA_PER_BLOCK` - the amount of mana that an "average" block is expected to consume (e.g. 15e6)
+- `MAXIMUM_MANA_PER_BLOCK` - the maximum amount of mana that a block can consume (e.g. 2 \* TARGET_MANA_PER_BLOCK)
+- `BLOBS_PER_BLOCK` - the number of blobs per block that are compensated for in the base fee (e.g. 3)
+- `L2_SLOTS_PER_L2_EPOCH` - the number of L2 slots in each L2 epoch (e.g. 32)
+- `L1_GAS_PER_BLOCK_PROPOSAL` - the amount of L1 gas required to propose an L2 block on L1 (e.g. 0.2e6)
+- `L1_GAS_PER_EPOCH_VERIFICATION` - the amount of L1 gas required to verify an L2 epoch on L1 (e.g. 1e6)
+- `MAXIMUM_FEE_PER_EPOCH_PROOF_QUOTE` - the maximum basis point fee a prover can submit for an epoch proof quote (e.g. 9000)
+- `MINIMUM_L2_SLOTS_PER_UNDERLYING_BASE_FEE_ORACLE_UPDATE` - the minimum number of L2 slots between updates to the underlying base fee oracle (e.g. 4)
+- `MINIMUM_FEE_ASSET_PER_ETH` - the minimum price of the fee asset in eth (e.g. 10)
+- `MAXIMUM_FEE_ASSET_PER_ETH_PERCENT_CHANGE_PER_L2_SLOT` - the maximum percentage increase in the price of the fee asset per block (e.g. 1%)
+- `FEE_ASSET_PRICE_UPDATE_FRACTION` - a value used to update the `fee_asset_price_modifier` (e.g. 1e11)
+- `MINIMUM_CONGESTION_MULTIPLIER` - the minimum value the congestion multiplier can take (e.g. 1)
+- `CONGESTION_MULTIPLIER_UPDATE_FRACTION` - the constant factor to dampen movement in the congestion multiplier (e.g. 8.547 \* TARGET_MANA_PER_BLOCK)
+
 ### Why is `BLOBS_PER_BLOCK` a constant?
 
 It makes computation easier. Otherwise block builder would need to know the number of blobs consumed by the transaction while it is building.
@@ -67,7 +85,7 @@ Then if we are publishing L2 blocks every 36 seconds, we can fit 131 \* NUM_BLOB
 
 This means that it should always be the case that a proposer has its data costs covered when building at up to 10 TPS.
 
-### L1 Congestion
+## L1 Congestion
 
 If L1 gas spikes during an epoch, this will be reflected in the `wei_per_l1_gas` and `wei_per_l1_blob_gas` oracles and make their way into the cost.
 
@@ -89,23 +107,6 @@ Regardless, the AVM supports DA gas metering per opcode, so it would not be diff
 
 Further, it is assumed that in the immediate term the proving cost will dominate the cost of the transaction, so this simplification makes implementation easier, and UX better.
 
-## Protocol Defined Constants
-
-- `OVERHEAD_MANA_PER_TX` - the overhead cost in mana for a transaction (e.g. 21_000)
-- `TARGET_MANA_PER_BLOCK` - the amount of mana that an "average" block is expected to consume (e.g. 15e6)
-- `MAXIMUM_MANA_PER_BLOCK` - the maximum amount of mana that a block can consume (e.g. 2 \* TARGET_MANA_PER_BLOCK)
-- `BLOBS_PER_BLOCK` - the number of blobs per block that are compensated for in the base fee (e.g. 3)
-- `L2_SLOTS_PER_L2_EPOCH` - the number of L2 slots in each L2 epoch (e.g. 32)
-- `L1_GAS_PER_BLOCK_PROPOSAL` - the amount of L1 gas required to propose an L2 block on L1 (e.g. 0.2e6)
-- `L1_GAS_PER_EPOCH_VERIFICATION` - the amount of L1 gas required to verify an L2 epoch on L1 (e.g. 1e6)
-- `MAXIMUM_FEE_PER_EPOCH_PROOF_QUOTE` - the maximum basis point fee a prover can submit for an epoch proof quote (e.g. 9000)
-- `MINIMUM_L2_SLOTS_PER_UNDERLYING_BASE_FEE_ORACLE_UPDATE` - the minimum number of L2 slots between updates to the underlying base fee oracle (e.g. 4)
-- `MINIMUM_FEE_ASSET_PER_ETH` - the minimum price of the fee asset in eth (e.g. 10)
-- `MAXIMUM_FEE_ASSET_PER_ETH_PERCENT_CHANGE_PER_L2_SLOT` - the maximum percentage increase in the price of the fee asset per block (e.g. 1%)
-- `FEE_ASSET_PRICE_UPDATE_FRACTION` - a value used to update the `fee_asset_price_modifier` (e.g. 1e11)
-- `MINIMUM_CONGESTION_MULTIPLIER` - the minimum value the congestion multiplier can take (e.g. 1)
-- `CONGESTION_MULTIPLIER_UPDATE_FRACTION` - the constant factor to dampen movement in the congestion multiplier (e.g. 8.547 \* TARGET_MANA_PER_BLOCK)
-
 ## Transaction Fields
 
 - `max_fee_per_mana` - the maximum fee per mana the user is willing to pay
@@ -128,27 +129,6 @@ The rollup contract contains the following fields:
 - `wei_per_l1_gas` - the cost of L1 gas in wei. Updated by anyone to the current L1 gas price at most every `MINIMUM_L2_SLOTS_PER_UNDERLYING_BASE_FEE_ORACLE_UPDATE` slots
 - `wei_per_l1_blob_gas` - the cost of L1 blob gas in wei. Updated by anyone to the current L1 blob gas price at most every `MINIMUM_L2_SLOTS_PER_UNDERLYING_BASE_FEE_ORACLE_UPDATE` slots
 
-## L1 Execution Cost of an L2 Block
-
-The cost of an L2 block's execution on L1 in wei is equal to the following:
-
-```math
-\begin{aligned}
-\text{L1 execution gas per L2 block} &= \text{L1\_GAS\_PER\_BLOCK\_PROPOSED}  \\
-&+ \text{BLOBS\_PER\_BLOCK} * \text{POINT\_EVALUATION\_PRECOMPILE\_GAS} \\
-&+ \frac{\text{L1\_GAS\_PER\_EPOCH\_VERIFIED}}{\text{L2\_SLOTS\_PER\_L2\_EPOCH}} \\
-\text{L1 execution wei per L2 block} &= \text{L1 execution gas per L2 block} * \text{wei per L1 gas}
-\end{aligned}
-```
-
-## Data Cost of an L2 Block
-
-The cost of an L2 block's data in wei is equal to the following:
-
-```math
-\text{L1 data wei per L2 block} = \text{BLOBS\_PER\_BLOCK} * \text{L1\_GAS\_PER\_BLOB} * \text{wei per L1 blob gas}
-```
-
 ## Exponentially Computed Values
 
 There are 2 important variables that are computed exponentially:
@@ -209,18 +189,42 @@ First we compute the excess mana in the current block by considering the parent
 
 ## Governance Defined Values
 
-To reduce the power that sequencer have over the fee market, the `proving_cost_per_mana` is set by governance.
+To reduce the power that sequencer have over the fee market, the `proving_cost_per_mana` is set by governance, and it is the amount of eth (in wei) that is neede to prove 1 manas worth of work.
+
+## Sequencer Cost of on L2 Block
+
+The L1 cost to propose an L2 block that the sequencer must cover is:
+
+```math
+\begin{aligned}
+\text{Sequencer L1 cost per L2 block} &= \left(\text{L1\_GAS\_PER\_BLOCK\_PROPOSED} \right. \\
+&+ \left. \text{BLOBS\_PER\_BLOCK} * \text{POINT\_EVALUATION\_PRECOMPILE\_GAS} \right) \\
+&* \text{wei\_per\_l1\_gas} \\
+&+ \text{BLOBS\_PER\_BLOCK} * \text{L1\_GAS\_PER\_BLOB} * \text{wei\_per\_l1\_blob\_gas}\\
+\end{aligned}
+```
+
+## Prover Cost of an L2 Block
+
+The L1 cost for an L2 block covered by the prover. Will be assuming a full epoch for this computation. Some parts are amortized (for example the submission cost is shared across the full epoch).
+
+```math
+\begin{aligned}
+\text{Prover L1 cost per L2 block} &= \left\lceil \frac{\text{L1\_GAS\_PER\_EPOCH\_VERIFIED}}{\text{L2\_SLOTS\_PER\_L2\_EPOCH}} \right\rceil * \text{wei\_per\_l1\_gas}\\ 
+&+ \text{proving\_cost\_per\_mana} * \text{TARGET\_MANA\_PER\_BLOCK} \\
+\end{aligned}
+```
 
 ## Deriving the base fee
 
-When a proposer is building an L2 block, it calculates:
+When a proposer is building an L2 block, it calculates a sequencer and a prover component and a congestion multiplier and from there the base fee that the user must cover.
 
 ```math
 \begin{aligned}
-\text{L1 cost in wei per L2 block} &= \text{L1 execution wei per L2 block} + \text{L1 data wei per L2 block} \\
-\text{wei/mana} &= \frac{\text{L1 cost in wei per L2 block}}{\text{TARGET\_MANA\_PER\_BLOCK}} + \text{proving cost wei per mana} \\
-\text{base fee in wei/mana} &= \text{wei/mana} * \text{base fee congestion multiplier} \\
-\text{base fee in fee asset /mana} &= \text{base fee in wei/mana} * \text{fee asset per wei}
+    \text{sequencer cost per mana} &= \left\lceil \frac{\text{Sequencer L1 cost per L2 block}}{\text{TARGET\_MANA\_PER\_BLOCK}} \right\rceil \\ 
+    \text{prover cost per mana} &= \left\lceil \frac{\text{Prover L1 cost per L2 block}}{\text{TARGET\_MANA\_PER\_BLOCK}} \right\rceil \\ 
+    \text{base\_fee\_in\_wei} &= \left(\text{sequencer cost per mana} + \text{prover cost per mana} \right) * \text{base fee congestion multiplier} \\
+    \text{base\_fee\_in\_fee\_asset} &= \left\lceil \text{base\_fee\_in\_wei} * \text{fee asset per wei} \right\rceil
 \end{aligned}
 ```
 
diff --git a/in-progress/8757-fees/notebook/README.md b/in-progress/8757-fees/notebook/README.md
index 3429797..d134521 100644
--- a/in-progress/8757-fees/notebook/README.md
+++ b/in-progress/8757-fees/notebook/README.md
@@ -1,3 +1,3 @@
-Go look in the `fee-model.ipynb` notebook for the actual simulation and fee model.
+Go look in the `fee-model.py` marimo notebook for the actual simulation and fee model.
 
-The setup is created with `uv`, so you should be good to run `uv sync` to get setup. Consider running `uv venv` to create a virtual environment first. You can add the kernel for the notebook by running `python -m ipykernel install --user --name=fee-model --display-name "Python (fee-model)"` and then selecting the kernel in the notebook settings.
+The setup is created with `uv`, so you should be good to run `uv sync` to get setup. Consider running `uv venv` to create a virtual environment first. Use `marimo` to open the notebook then; `marimo edit fee-model.py`.
diff --git a/in-progress/8757-fees/notebook/fee-model.ipynb b/in-progress/8757-fees/notebook/fee-model.ipynb
deleted file mode 100644
index f92f593..0000000
--- a/in-progress/8757-fees/notebook/fee-model.ipynb
+++ /dev/null
@@ -1,1218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ecabf04d-c299-45b3-8967-d7cef0a2d92b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ape import networks\n",
-    "import matplotlib.pyplot as plt\n",
-    "import math\n",
-    "\n",
-    "from typing import List, Optional, ClassVar\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "import os\n",
-    "\n",
-    "import json\n",
-    "\n",
-    "from pydantic import StrictInt, field_validator, Field\n",
-    "from pydantic.dataclasses import dataclass\n",
-    "from dataclasses import fields\n",
-    "from copy import deepcopy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "dcde820a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def json_serializable(cls):\n",
-    "    \"\"\"Decorator to make a dataclass JSON serializable and copyable.\"\"\"\n",
-    "\n",
-    "    def copy(self):\n",
-    "        return deepcopy(self)\n",
-    "\n",
-    "    cls.copy = copy\n",
-    "\n",
-    "    def to_dict(self):\n",
-    "        def convert_value(obj):\n",
-    "            if hasattr(obj, \"to_dict\"):\n",
-    "                return obj.to_dict()\n",
-    "            elif isinstance(obj, (list, tuple)):\n",
-    "                return [convert_value(x) for x in obj]\n",
-    "            elif isinstance(obj, dict):\n",
-    "                return {k: convert_value(v) for k, v in obj.items()}\n",
-    "            return obj\n",
-    "\n",
-    "        # Convert each field using our custom converter and sort keys\n",
-    "        return dict(\n",
-    "            sorted(\n",
-    "                (field.name, convert_value(getattr(self, field.name)))\n",
-    "                for field in fields(self)\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    cls.to_dict = to_dict\n",
-    "    return cls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0d014e6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def bounded_int(min_value: int, max_value: int):\n",
-    "    \"\"\"\n",
-    "    Decorator for creating bounded integer types with validation\n",
-    "    Inclusive of min_value, exclusive of max_value\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def decorator(cls):\n",
-    "        @dataclass\n",
-    "        class BoundedInt:\n",
-    "            value: StrictInt\n",
-    "\n",
-    "            @field_validator(\"value\")\n",
-    "            def check_range(cls, v):\n",
-    "                if not (min_value <= v < max_value):\n",
-    "                    raise ValueError(\n",
-    "                        f\"Value don't satisfy {min_value} <= {v} <= {max_value}\"\n",
-    "                    )\n",
-    "                return v\n",
-    "\n",
-    "            def to_dict(self) -> int:\n",
-    "                # Custom serialization to return just the integer value\n",
-    "                return self.value\n",
-    "\n",
-    "            def __eq__(self, other):\n",
-    "                if isinstance(other, BoundedInt):\n",
-    "                    return self.value == other.value\n",
-    "                return False\n",
-    "\n",
-    "            def __ne__(self, other):\n",
-    "                return not self.__eq__(other)\n",
-    "\n",
-    "            def __gt__(self, other):\n",
-    "                return self.value > other.value\n",
-    "\n",
-    "            def __ge__(self, other):\n",
-    "                return self.value >= other.value\n",
-    "\n",
-    "            def __lt__(self, other):\n",
-    "                return self.value < other.value\n",
-    "\n",
-    "            def __le__(self, other):\n",
-    "                return self.value <= other.value\n",
-    "\n",
-    "            def __abs__(self):\n",
-    "                return BoundedInt(value=abs(self.value))\n",
-    "\n",
-    "            def __neg__(self):\n",
-    "                return BoundedInt(value=-self.value)\n",
-    "\n",
-    "            def __add__(self, other):\n",
-    "                if isinstance(other, BoundedInt):\n",
-    "                    result = self.value + other.value\n",
-    "                    if result > max_value:\n",
-    "                        raise OverflowError(\"Integer overflow\")\n",
-    "                    return BoundedInt(value=result)\n",
-    "                else:\n",
-    "                    raise TypeError(\n",
-    "                        f\"Unsupported operand type for +: '{cls.__name__}' and '{type(other)}'\"\n",
-    "                    )\n",
-    "\n",
-    "            def __sub__(self, other):\n",
-    "                if isinstance(other, BoundedInt):\n",
-    "                    result = self.value - other.value\n",
-    "                    if result < min_value:\n",
-    "                        raise ValueError(\"Integer underflow\")\n",
-    "                    return BoundedInt(value=result)\n",
-    "                else:\n",
-    "                    raise TypeError(\n",
-    "                        f\"Unsupported operand type for -: '{cls.__name__}' and '{type(other)}'\"\n",
-    "                    )\n",
-    "\n",
-    "            def __mul__(self, other):\n",
-    "                if isinstance(other, BoundedInt):\n",
-    "                    result = self.value * other.value\n",
-    "                    if result > max_value:\n",
-    "                        raise OverflowError(\"Integer overflow\")\n",
-    "                    return BoundedInt(value=result)\n",
-    "                else:\n",
-    "                    raise TypeError(\n",
-    "                        f\"Unsupported operand type for *: '{cls.__name__}' and '{type(other)}'\"\n",
-    "                    )\n",
-    "\n",
-    "            def __truediv__(self, other):\n",
-    "                if isinstance(other, BoundedInt):\n",
-    "                    if other.value == 0:\n",
-    "                        raise ZeroDivisionError(\"Division by zero\")\n",
-    "                    return BoundedInt(value=self.value // other.value)\n",
-    "                else:\n",
-    "                    raise TypeError(\n",
-    "                        f\"Unsupported operand type for /: '{cls.__name__}' and '{type(other)}'\"\n",
-    "                    )\n",
-    "\n",
-    "            def mul_div(self, other, denominator, round_up=False):\n",
-    "                temp = self.value * other.value\n",
-    "                result = temp // denominator.value\n",
-    "                if round_up and temp % denominator.value != 0:\n",
-    "                    result += 1\n",
-    "                return BoundedInt(value=result)\n",
-    "\n",
-    "        # Copy the class name and update annotations\n",
-    "        BoundedInt.__name__ = cls.__name__\n",
-    "        BoundedInt.__qualname__ = cls.__qualname__\n",
-    "        return BoundedInt\n",
-    "\n",
-    "    return decorator\n",
-    "\n",
-    "\n",
-    "@bounded_int(min_value=0, max_value=2**256 - 1)\n",
-    "class Uint256:\n",
-    "    pass\n",
-    "\n",
-    "\n",
-    "@bounded_int(min_value=-(2**255), max_value=2**255 - 1)\n",
-    "class Int256:\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "66dd7b88-58d0-4b50-bf04-d28947f19c72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MIN_BASE_FEE_PER_BLOB_GAS = Uint256(1)\n",
-    "BLOB_BASE_FEE_UPDATE_FRACTION = Uint256(3338477)\n",
-    "BLOB_SIZE_IN_FIELDS = Uint256(4096)\n",
-    "GAS_PER_BLOB = Uint256(2**17)\n",
-    "\n",
-    "\n",
-    "def fake_exponential(\n",
-    "    factor: Uint256, numerator: Uint256, denominator: Uint256\n",
-    ") -> Uint256:\n",
-    "    \"\"\"\n",
-    "    An approximation of the exponential function: factor * e ** (numerator / denominator)\n",
-    "    Approximated using a taylor series.\n",
-    "    For shorthand below, let `a = factor`, `x = numerator`, `d = denominator`\n",
-    "\n",
-    "    f(x) =  a\n",
-    "         + (a * x) / d\n",
-    "         + (a * x ** 2) / (2 * d ** 2)\n",
-    "         + (a * x ** 3) / (6 * d ** 3)\n",
-    "         + (a * x ** 4) / (24 * d ** 4)\n",
-    "         + (a * x ** 5) / (120 * d ** 5)\n",
-    "         + ...\n",
-    "\n",
-    "    For integer precision purposes, we will multiply by the denominator for intermediary steps and then finally do a division by it.\n",
-    "    The notation below might look slightly strange, but it is to try to convey the program flow below.\n",
-    "\n",
-    "    e(x) = (       a * d\n",
-    "         +         a * d * x / d\n",
-    "         +       ((a * d * x / d) * x) / (2 * d)\n",
-    "         +     ((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)\n",
-    "         +   ((((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)) * x) / (4 * d)\n",
-    "         + ((((((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)) * x) / (4 * d)) * x) / (5 * d)\n",
-    "         + ...\n",
-    "           ) / d\n",
-    "\n",
-    "    While the notation might make it a bit of a pain to look at. f(x) and e(x) are the same, gotta lover integer math.\n",
-    "    \"\"\"\n",
-    "    i = Uint256(1)\n",
-    "    output = Uint256(0)\n",
-    "    numerator_accum = factor * denominator\n",
-    "    while numerator_accum > Uint256(0):\n",
-    "        output += numerator_accum\n",
-    "        numerator_accum = (numerator_accum * numerator) / (denominator * i)\n",
-    "        i += Uint256(1)\n",
-    "    return output / denominator\n",
-    "\n",
-    "\n",
-    "# Small check to see if the fake exponential is working as intended\n",
-    "a = Uint256(5415357955)\n",
-    "b = Uint256(2611772262)\n",
-    "c = Uint256(100000000000)\n",
-    "d = fake_exponential(a, b, c)\n",
-    "e = Uint256(5558657961)\n",
-    "assert d == e, f\"Expected {d} to be {e}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe74b3a3",
-   "metadata": {},
-   "source": [
-    "# Collecting data\n",
-    "\n",
-    "In the following section we will be collecting data from an Etheruem node. If it is the same range as we last collected we will simply load it from a pickle file.\n",
-    "\n",
-    "Note that we only keep track of minimal information related to the L1 block.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "9e843684",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class L1BlockSub:\n",
-    "    number: Uint256\n",
-    "    timestamp: Uint256\n",
-    "    blob_fee: Uint256\n",
-    "    base_fee: Uint256\n",
-    "    excess_blob_gas: Uint256\n",
-    "\n",
-    "\n",
-    "def get_l1_block_sub(block_number: int) -> L1BlockSub:\n",
-    "    block = networks.provider.web3.eth.get_block(block_number)\n",
-    "    blob_fee = fake_exponential(\n",
-    "        MIN_BASE_FEE_PER_BLOB_GAS,\n",
-    "        Uint256(block.excessBlobGas),\n",
-    "        BLOB_BASE_FEE_UPDATE_FRACTION,\n",
-    "    )\n",
-    "    return L1BlockSub(\n",
-    "        number=Uint256(block.number),\n",
-    "        blob_fee=blob_fee,\n",
-    "        base_fee=Uint256(block.baseFeePerGas),\n",
-    "        excess_blob_gas=Uint256(block.excessBlobGas),\n",
-    "        timestamp=Uint256(block.timestamp),\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def get_blocks(start_number: int, number_of_blocks: int):\n",
-    "    if os.path.exists(\"blocks.pkl\"):\n",
-    "        with open(\"blocks.pkl\", \"rb\") as f:\n",
-    "            candidated_blocks = pickle.load(f)\n",
-    "            if (\n",
-    "                candidated_blocks[0].number.value == start_number\n",
-    "                and candidated_blocks[-1].number.value == start_number + number_of_blocks\n",
-    "            ):\n",
-    "                return candidated_blocks\n",
-    "    networks.parse_network_choice(\"ethereum:mainnet:node\").__enter__()\n",
-    "    blocks = [\n",
-    "        get_l1_block_sub(i)\n",
-    "        for i in range(start_number, start_number + number_of_blocks + 1)\n",
-    "    ]\n",
-    "    with open(\"blocks.pkl\", \"wb\") as f:\n",
-    "        pickle.dump(blocks, f)\n",
-    "    return blocks\n",
-    "\n",
-    "\n",
-    "# You should not change these unless you have access to the endpoint in the `ape-config.yaml` file.\n",
-    "# This is because we are using the `ape` library to fetch the data from the Ethereum node, or just\n",
-    "# fetching the data from a pickle file otherwise if we already have it.\n",
-    "\n",
-    "block_start_number = 20973664 - 500\n",
-    "blocks_to_pull = 2000\n",
-    "\n",
-    "blocks = get_blocks(block_start_number, blocks_to_pull)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "d353407f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "block_numbers = [b.number.value for b in blocks]\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.plot(block_numbers, [b.blob_fee.value for b in blocks], label=\"Blob Gas Price (wei)\")\n",
-    "plt.plot(block_numbers, [b.base_fee.value for b in blocks], label=\"Base Fee (wei)\")\n",
-    "plt.xlabel(\"Block Number\")\n",
-    "plt.ylabel(\"Fee (wei)\")\n",
-    "plt.title(\"Blob Gas Price and Base Fee over Recent Blocks\")\n",
-    "plt.legend()\n",
-    "plt.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6648a90",
-   "metadata": {},
-   "source": [
-    "# Model\n",
-    "\n",
-    "We want a model where the `mana` that a transaction requires is **constant** and independent of the l1 cost, but with the base fee fluctuating.  \n",
-    "This is slightly different from the previously specified model, where the `mana` amount itself could also fluctuate, but it is simpler for the users if we have a fixed amount.  \n",
-    "Also it will avoid landing in an issue where your tx is now out of gas because the l1 cost increased.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4e1b98c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@dataclass\n",
-    "class Tx:\n",
-    "    mana_spent: Uint256\n",
-    "\n",
-    "    nullifiers: Uint256 = Field(default_factory=lambda: Uint256(1))\n",
-    "    notes: Uint256 = Field(default_factory=lambda: Uint256(0))\n",
-    "    public_state_diffs: Uint256 = Field(default_factory=lambda: Uint256(1 + 2 + 2 + 2))\n",
-    "    encrypted_logs_size: Uint256 = Field(default_factory=lambda: Uint256(256))\n",
-    "\n",
-    "    def size_in_bytes(self) -> Uint256:\n",
-    "        return (\n",
-    "            Uint256(32) * self.nullifiers\n",
-    "            + Uint256(64) * self.public_state_diffs\n",
-    "            + self.notes * (Uint256(32) + self.encrypted_logs_size)\n",
-    "        )\n",
-    "\n",
-    "    def size_in_fields(self) -> Uint256:\n",
-    "        return Uint256(math.ceil(self.size_in_bytes().value / 32))\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class BlockHeader:\n",
-    "    l1_block_number: Uint256\n",
-    "    block_number: Uint256\n",
-    "    slot_number: Uint256\n",
-    "    timestamp: Uint256\n",
-    "    mana_spent: Uint256\n",
-    "    blobs_needed: Uint256\n",
-    "    size_in_fields: Uint256\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class Block:\n",
-    "    l1_block_number: Uint256\n",
-    "    block_number: Uint256\n",
-    "    slot_number: Uint256\n",
-    "    timestamp: Uint256\n",
-    "    txs: list[Tx]\n",
-    "\n",
-    "    def size_in_fields(self) -> Uint256:\n",
-    "        return sum((tx.size_in_fields() for tx in self.txs), start=Uint256(0))\n",
-    "\n",
-    "    def mana_spent(self) -> Uint256:\n",
-    "        return sum((tx.mana_spent for tx in self.txs), start=Uint256(0))\n",
-    "\n",
-    "    def blobs_needed(self) -> Uint256:\n",
-    "        # Note that we cast this slightly different as we want the ceiling of the division\n",
-    "        return Uint256(math.ceil(self.size_in_fields().value / 4096))\n",
-    "\n",
-    "    def compute_header(self) -> BlockHeader:\n",
-    "        return BlockHeader(\n",
-    "            l1_block_number=self.l1_block_number,\n",
-    "            block_number=self.block_number,\n",
-    "            slot_number=self.slot_number,\n",
-    "            timestamp=self.timestamp,\n",
-    "            mana_spent=self.mana_spent(),\n",
-    "            blobs_needed=self.blobs_needed(),\n",
-    "            size_in_fields=self.size_in_fields(),\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class L1Fees:\n",
-    "    blob_fee: Uint256\n",
-    "    base_fee: Uint256\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class L1GasOracle:\n",
-    "    LIFETIME = Uint256(5)\n",
-    "    LATENCY = Uint256(2)\n",
-    "\n",
-    "    pre: L1Fees\n",
-    "    post: L1Fees\n",
-    "    slot_of_change: Uint256\n",
-    "\n",
-    "    def value_at(self, slot_number: Uint256) -> L1Fees:\n",
-    "        if slot_number < self.slot_of_change:\n",
-    "            return self.pre\n",
-    "        return self.post\n",
-    "\n",
-    "    def queue_change(self, slot_number: Uint256, fees: L1Fees):\n",
-    "        # If the value have been active `self.LIFETIME - self.LATENCY` we can queue the next\n",
-    "        if slot_number >= self.slot_of_change + (self.LIFETIME - self.LATENCY):\n",
-    "            self.pre = self.post\n",
-    "            self.post = fees\n",
-    "            # This value should at the earliest be equal to the slot_of_change + LIFETIME.\n",
-    "            assert slot_number + self.LATENCY <= self.slot_of_change + self.LIFETIME\n",
-    "            self.slot_of_change = slot_number + self.LATENCY\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class FeeHeader:\n",
-    "    excess_mana: Uint256 = Field(default_factory=lambda: Uint256(0))\n",
-    "    mana_used: Uint256 = Field(default_factory=lambda: Uint256(0))\n",
-    "    fee_asset_price_numerator: Uint256 = Field(default_factory=lambda: Uint256(0))\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class ManaBaseFeeComponents:\n",
-    "    data_cost: Uint256\n",
-    "    gas_cost: Uint256\n",
-    "    proving_cost: Uint256\n",
-    "    congestion_cost: Uint256\n",
-    "    congestion_multiplier: Uint256\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class OracleInput:\n",
-    "    fee_asset_price_modifier: Int256\n",
-    "\n",
-    "\n",
-    "@dataclass\n",
-    "class FeeModel:\n",
-    "    \"\"\"\n",
-    "    The fee model here will not be perfect, it does not take into account for example that there might be slots with no txs, in reality\n",
-    "    it should be possible for the model, to take this into account mainly impact the `calc_excess_mana` function.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    AZTEC_SLOT_DURATION = Uint256(36)\n",
-    "    AZTEC_EPOCH_DURATION = Uint256(32)\n",
-    "    MAX_FEE_ASSET_PRICE_MODIFIER = Int256(int(1e6))\n",
-    "    CONGESTION_MULTIPLIER_DIVISOR = Uint256(int(1e9))\n",
-    "    GAS_PER_BLOB = Uint256(2**17)\n",
-    "\n",
-    "    mana_target: Uint256\n",
-    "    l1_gas_per_block_proposed: Uint256\n",
-    "    l1_gas_per_epoch_verified: Uint256\n",
-    "    minimum_fee_asset_per_eth: Uint256\n",
-    "    proving_cost_per_mana: Uint256\n",
-    "\n",
-    "    genesis_timestamp: Uint256\n",
-    "\n",
-    "    # Below is mutable\n",
-    "    current_timestamp: Uint256\n",
-    "    l1_gas_oracle: L1GasOracle\n",
-    "    fee_headers: List[FeeHeader] = Field(default_factory=lambda: [FeeHeader()])\n",
-    "\n",
-    "    def set_timestamp(self, timestamp: Uint256):\n",
-    "        self.current_timestamp = timestamp\n",
-    "\n",
-    "    def current_slot_number(self) -> Uint256:\n",
-    "        return (\n",
-    "            self.current_timestamp - self.genesis_timestamp\n",
-    "        ) / FeeModel.AZTEC_SLOT_DURATION\n",
-    "\n",
-    "    def photograph(self, l1_fees: L1Fees):\n",
-    "        self.l1_gas_oracle.queue_change(self.current_slot_number(), l1_fees)\n",
-    "\n",
-    "    def current_l1_fees(self) -> L1Fees:\n",
-    "        return self.l1_gas_oracle.value_at(self.current_slot_number())\n",
-    "\n",
-    "    def fee_update_fraction(self) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        A bit of magic for the fake exponential and integer math. Computing the divisor this way should ensure\n",
-    "        that the multiplier will increase by at most a factor of ~ 1.125 every block.\n",
-    "        \"\"\"\n",
-    "        return Uint256(int(self.mana_target.value / 0.117))\n",
-    "\n",
-    "    def fee_asset_price_update_fraction(self) -> Uint256:\n",
-    "        return Uint256(FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.value * int(100))\n",
-    "\n",
-    "    def execution_cost(self, block: Optional[Block]) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        Return the cost of execution in wei.\n",
-    "        Note that it does NOT take into account the priority fee of the tx. This is on purpose, since it could otherwise\n",
-    "        lead to some weird incentives, as a sequencer could otherwise increase the user fees by paying a higher priority fee.\n",
-    "        \"\"\"\n",
-    "        l1_fees = self.current_l1_fees()\n",
-    "        l1_gas = (\n",
-    "            self.l1_gas_per_block_proposed\n",
-    "            + (block.blobs_needed() if block else Uint256(3)) * Uint256(50_000)\n",
-    "            + self.l1_gas_per_epoch_verified / FeeModel.AZTEC_EPOCH_DURATION\n",
-    "        )\n",
-    "        return l1_gas.mul_div(l1_fees.base_fee, self.mana_target, round_up=True)\n",
-    "\n",
-    "    def data_cost(self, block: Optional[Block], real=False) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        Return the cost of data publication in wei\n",
-    "        \"\"\"\n",
-    "        l1_fees = self.current_l1_fees()\n",
-    "        blob_gas = (\n",
-    "            (block.blobs_needed() if block else Uint256(3)) * FeeModel.GAS_PER_BLOB\n",
-    "            if real\n",
-    "            else (\n",
-    "                block.size_in_fields() * Uint256(32)\n",
-    "                if block\n",
-    "                else Uint256(3) * FeeModel.GAS_PER_BLOB\n",
-    "            )\n",
-    "        )\n",
-    "        return blob_gas.mul_div(l1_fees.blob_fee, self.mana_target, round_up=True)\n",
-    "\n",
-    "    def fee_asset_price(self) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        @TODO write up a proper explanation for this.\n",
-    "        Essentially the amount of fee asset per ether gwei. Uses 1e9 in precision.\n",
-    "        A value > 1e9 means that the fee asset is cheaper than ETH\n",
-    "        A value < 1e9 means that the fee asset is more expensive than ETH\n",
-    "        A value = 1e9 means that the fee asset is the same as ETH\n",
-    "        \"\"\"\n",
-    "        return fake_exponential(\n",
-    "            self.minimum_fee_asset_per_eth,\n",
-    "            self.fee_headers[-1].fee_asset_price_numerator,\n",
-    "            self.fee_asset_price_update_fraction(),\n",
-    "        )\n",
-    "\n",
-    "    def mana_base_fee_components(\n",
-    "        self, block: Optional[Block], in_fee_asset: bool = False\n",
-    "    ) -> ManaBaseFeeComponents:\n",
-    "        data_cost = self.data_cost(block, real=True)\n",
-    "        gas_cost = self.execution_cost(block)\n",
-    "        proving_cost = self.proving_cost_per_mana\n",
-    "        congestion_multiplier = fake_exponential(\n",
-    "            Uint256(int(1e9)), self.calc_excess_mana(), self.fee_update_fraction()\n",
-    "        )\n",
-    "\n",
-    "        total = data_cost + gas_cost + proving_cost\n",
-    "\n",
-    "        congestion_cost = (\n",
-    "            total * congestion_multiplier / FeeModel.CONGESTION_MULTIPLIER_DIVISOR\n",
-    "        ) - total\n",
-    "\n",
-    "        # With precision of 1e9\n",
-    "        precision = Uint256(int(1e9))\n",
-    "        fee_asset_price = self.fee_asset_price() if in_fee_asset else precision\n",
-    "\n",
-    "        # We round up the components to ensure that the fee is always enough\n",
-    "        # Note that this might lead to overpayment of #components wei per mana.\n",
-    "        return ManaBaseFeeComponents(\n",
-    "            data_cost=data_cost.mul_div(fee_asset_price, precision, round_up=True),\n",
-    "            gas_cost=gas_cost.mul_div(fee_asset_price, precision, round_up=True),\n",
-    "            proving_cost=proving_cost.mul_div(\n",
-    "                fee_asset_price, precision, round_up=True\n",
-    "            ),\n",
-    "            congestion_cost=congestion_cost.mul_div(\n",
-    "                fee_asset_price, precision, round_up=True\n",
-    "            ),\n",
-    "            congestion_multiplier=congestion_multiplier,\n",
-    "        )\n",
-    "\n",
-    "    def mana_base_fee(\n",
-    "        self,\n",
-    "        block: Optional[Block],\n",
-    "        apply_congestion_multiplier=False,\n",
-    "        in_fee_asset: bool = False,\n",
-    "    ) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        Return the base fee of mana in wei.\n",
-    "        We assume that there is a minimum amount of mana spent per block, to the sequencer to manipulate the fee completely.\n",
-    "        \"\"\"\n",
-    "        components = self.mana_base_fee_components(block, in_fee_asset)\n",
-    "\n",
-    "        if apply_congestion_multiplier:\n",
-    "            return (\n",
-    "                components.data_cost\n",
-    "                + components.gas_cost\n",
-    "                + components.proving_cost\n",
-    "                + components.congestion_cost\n",
-    "            )\n",
-    "        return components.data_cost + components.gas_cost + components.proving_cost\n",
-    "\n",
-    "    def calc_excess_mana(self) -> Uint256:\n",
-    "        \"\"\"\n",
-    "        Calculate the excess mana in the last block.\n",
-    "        Should be updated to take into account that there might be slots with no txs.\n",
-    "        \"\"\"\n",
-    "        spent = self.fee_headers[-1].mana_used\n",
-    "        excess = self.fee_headers[-1].excess_mana\n",
-    "        if excess + spent < self.mana_target:\n",
-    "            return Uint256(0)\n",
-    "        return excess + spent - self.mana_target\n",
-    "\n",
-    "    def add_slot(\n",
-    "        self, block: Optional[Block], oracle_input: Optional[OracleInput] = None\n",
-    "    ):\n",
-    "        \"\"\"\n",
-    "        Potentially add a block for a slot, if there is one.\n",
-    "        \"\"\"\n",
-    "        assert (\n",
-    "            block is None or block.slot_number == self.current_slot_number()\n",
-    "        ), f\"invalid slot number {block.slot_number} != {self.current_slot_number()}\"\n",
-    "\n",
-    "        assert block is None or block.mana_spent() <= self.mana_target * Uint256(\n",
-    "            2\n",
-    "        ), \"invalid block size\"\n",
-    "\n",
-    "        fee_asset_price_modifier = (\n",
-    "            oracle_input.fee_asset_price_modifier if oracle_input else Int256(0)\n",
-    "        )\n",
-    "        assert fee_asset_price_modifier is None or (\n",
-    "            abs(fee_asset_price_modifier) <= FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER\n",
-    "        ), \"invalid fee asset price modifier\"\n",
-    "\n",
-    "        parent_fee_header = self.fee_headers[-1]\n",
-    "        new_header = FeeHeader(\n",
-    "            excess_mana=self.calc_excess_mana(),\n",
-    "            mana_used=block.mana_spent() if block else Uint256(0),\n",
-    "            fee_asset_price_numerator=Uint256(\n",
-    "                max(\n",
-    "                    fee_asset_price_modifier.value\n",
-    "                    + parent_fee_header.fee_asset_price_numerator.value,\n",
-    "                    0,\n",
-    "                )\n",
-    "            ),\n",
-    "        )\n",
-    "        self.fee_headers.append(new_header)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7a10bf0",
-   "metadata": {},
-   "source": [
-    "## Simulating some blocks\n",
-    "\n",
-    "In the following, we will be simulating a bunch of blocks and then plotting our results.\n",
-    "\n",
-    "For the simulation of a block, we will be generating a random block, with mana spent being a random number between 0 and 2 \\* mana_target.  \n",
-    "We will keep collecting transactions from a \"randomized\" mempool until we have either reached the mana target, or the mempool size limit.  \n",
-    "We only collect transactions find the fee acceptable (another sampling) and don't force us beyond the limit.  \n",
-    "The size of the transactions are also drawn from a distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d122ddc9",
-   "metadata": {},
-   "source": [
-    "> This is probably the place where it would make good sense to get someone that know thier stuff in.  \n",
-    "> We have a setup that could work, but at the same time, it does not seem to work perfectly well.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "0a1f3de6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WEI PER MANA: 180511931.84\n",
-      "GWEI PER MANA / 1e9: 0.18 gwei\n",
-      "The cost: 0.01 USD\n"
-     ]
-    }
-   ],
-   "source": [
-    "USD_PER_ETH = 2638\n",
-    "USD_PER_WEI = USD_PER_ETH / 1e18\n",
-    "MANA_PER_BASE_TX = Uint256(21000)\n",
-    "USD_PER_BASE_TX = 0.01  # 0.3 is the real value, this is me messing around # a value kinda pulled out the ass as of now, expecting big reductions\n",
-    "\n",
-    "USD_PER_MANA = USD_PER_BASE_TX / MANA_PER_BASE_TX.value\n",
-    "\n",
-    "WEI_PER_MANA = USD_PER_MANA / USD_PER_WEI\n",
-    "\n",
-    "print(f\"WEI PER MANA: {WEI_PER_MANA:.2f}\")\n",
-    "print(f\"GWEI PER MANA / 1e9: {WEI_PER_MANA / 1e9:.2f} gwei\")\n",
-    "\n",
-    "print(f\"The cost: {2638 * MANA_PER_BASE_TX.value * WEI_PER_MANA / 1e18:.2f} USD\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "a1248bb2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class TestPointOutputs:\n",
-    "    fee_asset_price_at_execution: Uint256\n",
-    "    mana_base_fee_components_in_wei: ManaBaseFeeComponents\n",
-    "    mana_base_fee_components_in_fee_asset: ManaBaseFeeComponents\n",
-    "    l1_fee_oracle_output: L1Fees  # value_at(now)\n",
-    "    l1_gas_oracle_values: L1GasOracle\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class TestPoint:\n",
-    "    block_header: BlockHeader\n",
-    "    parent_fee_header: FeeHeader\n",
-    "    fee_header: FeeHeader\n",
-    "    oracle_input: OracleInput\n",
-    "    outputs: TestPointOutputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "8dbb574f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fee_model = FeeModel(\n",
-    "    mana_target=Uint256(int(1e8)),\n",
-    "    l1_gas_per_block_proposed=Uint256(int(0.15e6)),\n",
-    "    l1_gas_per_epoch_verified=Uint256(int(1e6)),\n",
-    "    proving_cost_per_mana=Uint256(int(WEI_PER_MANA)),\n",
-    "    minimum_fee_asset_per_eth=Uint256(int(10e9)),\n",
-    "    l1_gas_oracle=L1GasOracle(\n",
-    "        pre=L1Fees(blob_fee=Uint256(1), base_fee=Uint256(int(1e9))),\n",
-    "        post=L1Fees(\n",
-    "            blob_fee=blocks[0].blob_fee,\n",
-    "            base_fee=blocks[0].base_fee,\n",
-    "        ),\n",
-    "        slot_of_change=L1GasOracle.LIFETIME,\n",
-    "    ),\n",
-    "    genesis_timestamp=blocks[0].timestamp - FeeModel.AZTEC_SLOT_DURATION,\n",
-    "    current_timestamp=blocks[0].timestamp,\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def generate_random_with_min(\n",
-    "    mean: Uint256, std_dev: Uint256, min_value: Uint256\n",
-    ") -> Uint256:\n",
-    "    while True:\n",
-    "        value = int(random.gauss(mean.value, std_dev.value))\n",
-    "        if value >= min_value.value:\n",
-    "            return Uint256(value)\n",
-    "\n",
-    "\n",
-    "MEMPOOL_SIZE = 5000\n",
-    "\n",
-    "l2_blocks = []\n",
-    "test_points = []\n",
-    "\n",
-    "last_slot = Uint256(0)\n",
-    "\n",
-    "for l1_block in blocks:\n",
-    "    fee_model.set_timestamp(l1_block.timestamp)\n",
-    "    # We try to photograph the l1 fees at every l1 block\n",
-    "    fee_model.photograph(L1Fees(blob_fee=l1_block.blob_fee, base_fee=l1_block.base_fee))\n",
-    "\n",
-    "    slot_number = fee_model.current_slot_number()\n",
-    "\n",
-    "    # We are in the next slot, let us create a block!\n",
-    "    if slot_number > last_slot:\n",
-    "        last_slot = slot_number\n",
-    "\n",
-    "        cost = fee_model.mana_base_fee_components(None)\n",
-    "        cost_in_fee_asset = fee_model.mana_base_fee_components(None, in_fee_asset=True)\n",
-    "\n",
-    "        real_cost = cost.data_cost + cost.gas_cost + cost.proving_cost\n",
-    "        mana_base_fee = real_cost + cost.congestion_cost\n",
-    "\n",
-    "        mana_spent_block = Uint256(0)\n",
-    "        mana_planned_for_block = min(\n",
-    "            generate_random_with_min(\n",
-    "                fee_model.mana_target,\n",
-    "                fee_model.mana_target,\n",
-    "                Uint256(0),\n",
-    "            ),\n",
-    "            fee_model.mana_target * Uint256(2),\n",
-    "        )\n",
-    "\n",
-    "        txs = []\n",
-    "        count = 0\n",
-    "\n",
-    "        while (\n",
-    "            abs(mana_planned_for_block.value - mana_spent_block.value)\n",
-    "            >= MANA_PER_BASE_TX.value\n",
-    "            and count < MEMPOOL_SIZE\n",
-    "        ):\n",
-    "            count += 1\n",
-    "            mana_spent_tx = generate_random_with_min(\n",
-    "                MANA_PER_BASE_TX * Uint256(2), Uint256(500_000), MANA_PER_BASE_TX\n",
-    "            )\n",
-    "            within_bounds = (\n",
-    "                mana_spent_tx + mana_spent_block <= fee_model.mana_target * Uint256(2)\n",
-    "            )\n",
-    "            acceptable_mana_base_fee = generate_random_with_min(\n",
-    "                real_cost, Uint256(2) * real_cost, Uint256(0)\n",
-    "            )\n",
-    "\n",
-    "            is_fee_acceptable = acceptable_mana_base_fee >= mana_base_fee\n",
-    "\n",
-    "            if within_bounds and is_fee_acceptable:\n",
-    "                txs.append(Tx(mana_spent=mana_spent_tx))\n",
-    "                mana_spent_block += mana_spent_tx\n",
-    "\n",
-    "        block = Block(\n",
-    "            l1_block_number=l1_block.number,\n",
-    "            timestamp=l1_block.timestamp,\n",
-    "            slot_number=slot_number,\n",
-    "            block_number=Uint256(len(l2_blocks) + 1),\n",
-    "            txs=txs,\n",
-    "        )\n",
-    "\n",
-    "        # Deciding oracle movements. Here we assume that there are some fluctuation in the price of ETH and proving, up to 1% per 36 second.\n",
-    "        oracle_input = OracleInput(\n",
-    "            fee_asset_price_modifier=Int256(\n",
-    "                int(max(-1, min(1, random.gauss(0.01, 0.5))) * FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.value)\n",
-    "            ),\n",
-    "        )\n",
-    "\n",
-    "        fee_asset_price_at_execution = fee_model.fee_asset_price()\n",
-    "        fee_model.add_slot(block, oracle_input)\n",
-    "\n",
-    "        test_points.append(\n",
-    "            TestPoint(\n",
-    "                block_header=block.compute_header(),\n",
-    "                fee_header=fee_model.fee_headers[-1],\n",
-    "                parent_fee_header=fee_model.fee_headers[-2],\n",
-    "                oracle_input=oracle_input,\n",
-    "                outputs=TestPointOutputs(\n",
-    "                    fee_asset_price_at_execution=fee_asset_price_at_execution,\n",
-    "                    mana_base_fee_components_in_wei=cost,\n",
-    "                    mana_base_fee_components_in_fee_asset=cost_in_fee_asset,\n",
-    "                    l1_fee_oracle_output=fee_model.current_l1_fees(),\n",
-    "                    l1_gas_oracle_values=fee_model.l1_gas_oracle.copy(),\n",
-    "                ),\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "        l2_blocks.append(block)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c4e6b50",
-   "metadata": {},
-   "source": [
-    "### Plotting the results\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "90b1da2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1200 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(12, 12), sharex=True)\n",
-    "\n",
-    "# For this, if we took our oracle, that would probably also be pretty cool he.\n",
-    "\n",
-    "aztec_l1_block_numbers = [l2_block.l1_block_number.value for l2_block in l2_blocks]\n",
-    "\n",
-    "act = ax1\n",
-    "act.plot(\n",
-    "    block_numbers,\n",
-    "    [l1_block.base_fee.value for l1_block in blocks],\n",
-    "    label=\"L1 BaseFee (wei)\",\n",
-    ")\n",
-    "act.plot(\n",
-    "    aztec_l1_block_numbers,\n",
-    "    [x.outputs.l1_fee_oracle_output.base_fee.value for x in test_points],\n",
-    "    label=\"L1 BaseFee Oracle (wei)\",\n",
-    ")\n",
-    "act.set_ylabel(\"Gas BaseFee (wei)\")\n",
-    "act.set_title(\"Gas base fees over Recent Blocks\")\n",
-    "act.legend()\n",
-    "act.grid(True)\n",
-    "\n",
-    "# I need to load the different components of the cost from my test points\n",
-    "\n",
-    "costs = [x.outputs.mana_base_fee_components_in_wei for x in test_points]\n",
-    "costs_fee_asset = [x.outputs.mana_base_fee_components_in_fee_asset for x in test_points]\n",
-    "\n",
-    "l1_gas_costs = [cost.gas_cost.value for cost in costs]\n",
-    "data_costs = [cost.data_cost.value for cost in costs]\n",
-    "proving_costs = [cost.proving_cost.value for cost in costs]\n",
-    "congestion_costs = [cost.congestion_cost.value for cost in costs]\n",
-    "\n",
-    "act = ax2\n",
-    "\n",
-    "act.plot(aztec_l1_block_numbers, l1_gas_costs, label=\"L1 Gas cost (wei)\")\n",
-    "act.plot(aztec_l1_block_numbers, data_costs, label=\"Blob cost (wei)\")\n",
-    "act.plot(aztec_l1_block_numbers, proving_costs, label=\"Proving cost (wei)\")\n",
-    "\n",
-    "act.set_ylabel(\"Mana BaseFee (wei)\")\n",
-    "act.set_title(\"Mana Base Fee Components over Recent Blocks\")\n",
-    "act.legend()\n",
-    "act.grid(True)\n",
-    "\n",
-    "\n",
-    "real_costs = [\n",
-    "    l1_gas_costs[i] + data_costs[i] + proving_costs[i] for i in range(len(l1_gas_costs))\n",
-    "]\n",
-    "total_costs = [real_costs[i] + congestion_costs[i] for i in range(len(l1_gas_costs))]\n",
-    "\n",
-    "act = ax3\n",
-    "act.fill_between(aztec_l1_block_numbers, 0, real_costs, label=\"Real cost (wei)\")\n",
-    "act.fill_between(\n",
-    "    aztec_l1_block_numbers,\n",
-    "    real_costs,\n",
-    "    total_costs,\n",
-    "    label=\"Congestion cost (wei)\",\n",
-    ")\n",
-    "act.set_ylabel(\"Mana BaseFee (wei)\")\n",
-    "act.set_title(\"Fee over the recent blocks (Ether)\")\n",
-    "act.legend()\n",
-    "act.grid(True)\n",
-    "\n",
-    "\n",
-    "real_costs = [\n",
-    "    c.data_cost.value + c.gas_cost.value + c.proving_cost.value for c in costs_fee_asset\n",
-    "]\n",
-    "total_costs = [\n",
-    "    real_costs[i] + costs_fee_asset[i].congestion_cost.value\n",
-    "    for i in range(len(real_costs))\n",
-    "]\n",
-    "act = ax4\n",
-    "act.fill_between(aztec_l1_block_numbers, 0, real_costs, label=\"Real cost (wei)\")\n",
-    "act.fill_between(\n",
-    "    aztec_l1_block_numbers,\n",
-    "    real_costs,\n",
-    "    total_costs,\n",
-    "    label=\"Congestion cost (wei)\",\n",
-    ")\n",
-    "act.set_ylabel(\"Mana BaseFee (wei)\")\n",
-    "act.set_title(\"Fee over the recent blocks (fee asset)\")\n",
-    "act.legend()\n",
-    "act.grid(True)\n",
-    "\n",
-    "\n",
-    "act = ax5\n",
-    "\n",
-    "t_0 = [len(b.txs) for b in l2_blocks]\n",
-    "t_1 = [b.mana_spent().value for b in l2_blocks]\n",
-    "flem_2 = act.twinx()\n",
-    "(l1,) = act.plot(\n",
-    "    aztec_l1_block_numbers,\n",
-    "    t_0,\n",
-    "    label=\"L2 number of transactions\",\n",
-    "    color=\"green\",\n",
-    "    linewidth=0.5,\n",
-    ")\n",
-    "(l2,) = flem_2.plot(\n",
-    "    aztec_l1_block_numbers, t_1, label=\"L2 mana spent\", color=\"blue\", linewidth=0.5\n",
-    ")\n",
-    "act.set_xlabel(\"Block Number\")\n",
-    "act.set_ylabel(\"Number of transactions\")\n",
-    "flem_2.set_ylabel(\"Mana Spent\")\n",
-    "act.set_title(\"Number of transactions and Mana Spent per Block\")\n",
-    "act.legend([l1, l2], [\"L2 number of transactions\", \"L2 mana spent\"])\n",
-    "act.grid(True)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29731a62",
-   "metadata": {},
-   "source": [
-    "## Points of interest\n",
-    "\n",
-    "- The multiplier makes the base fee more unstable, it might be better to consider a different function to see if we can stabilize it.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "376b4313",
-   "metadata": {},
-   "source": [
-    "## To test with sol\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d9809955",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "uint256 internal constant PROVING_COST_PER_MANA = 180511931;\n",
-      "\n",
-      "uint256 internal constant MINIMUM_FEE_ASSET_PER_ETH = 10000000000;\n",
-      "uint256 internal constant MAX_FEE_ASSET_PRICE_MODIFIER = 1000000;\n",
-      "uint256 internal constant FEE_ASSET_PRICE_UPDATE_FRACTION = 100000000;\n",
-      "\n",
-      "{\"l1_metadata\": [{\"base_fee\": 14402849460, \"blob_fee\": 23, \"block_number\": 20973164, \"timestamp\": 1729021859}, {\"base_fee\": 14715359607, \"blob_fee\": 24, \"block_number\": 20973165, \"timestamp\": 1729021871}, {\"base_fee\": 14114781023, \"blob_fee\": 22, \"block_number\": 20973166, \"timestamp\": 1729021883}, {\"base_fee\": 14415907878, \"blob_fee\": 25, \"block_number\": 20973167, \"timestamp\": 1729021895}, {\"base_fee\": 15049685848, \"blob_fee\": 25, \"block_number\": 20973168, \"timestamp\": 1729021907}, {\"base_fee\": 13986076892, \"blob_fee\": 28, \"block_number\": 20973169, \"timestamp\": 1729021919}, {\"base_fee\": 13994212243, \"blob_fee\": 31, \"block_number\": 20973170, \"timestamp\": 1729021931}, {\"base_fee\": 14831350217, \"blob_fee\": 30, \"block_number\": 20973171, \"timestamp\": 1729021943}, {\"base_fee\": 15086623709, \"blob_fee\": 27, \"block_number\": 20973172, \"timestamp\": 1729021955}, {\"base_fee\": 14784230064, \"blob_fee\": 29, 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{\"block_header\": {\"blobs_needed\": 1, \"block_number\": 660, \"l1_block_number\": 20975128, \"mana_spent\": 81873399, \"size_in_fields\": 2955, \"slot_number\": 660, \"timestamp\": 1729045583}, \"fee_header\": {\"excess_mana\": 1324595034, \"fee_asset_price_numerator\": 18494995, \"mana_used\": 81873399}, \"oracle_input\": {\"fee_asset_price_modifier\": -471929}, \"outputs\": {\"fee_asset_price_at_execution\": 12088496924, \"l1_fee_oracle_output\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 16645519796, \"blob_fee\": 1}, \"slot_of_change\": 660}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 10034297204, \"congestion_multiplier\": 4710415831, \"data_cost\": 13, \"gas_cost\": 522241273, \"proving_cost\": 2182117923}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 830069881, \"congestion_multiplier\": 4710415831, \"data_cost\": 1, \"gas_cost\": 43201506, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1315573947, \"fee_asset_price_numerator\": 18966924, \"mana_used\": 109021087}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 661, \"l1_block_number\": 20975131, \"mana_spent\": 121901802, \"size_in_fields\": 4065, \"slot_number\": 661, \"timestamp\": 1729045619}, \"fee_header\": {\"excess_mana\": 1306468433, \"fee_asset_price_numerator\": 17980663, \"mana_used\": 121901802}, \"oracle_input\": {\"fee_asset_price_modifier\": -514332}, \"outputs\": {\"fee_asset_price_at_execution\": 12031582205, \"l1_fee_oracle_output\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 16645519796, \"blob_fee\": 1}, \"slot_of_change\": 660}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9720994278, \"congestion_multiplier\": 4611568638, \"data_cost\": 13, \"gas_cost\": 519782471, \"proving_cost\": 2171844137}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 807956436, \"congestion_multiplier\": 4611568638, \"data_cost\": 1, \"gas_cost\": 43201506, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1324595034, \"fee_asset_price_numerator\": 18494995, \"mana_used\": 81873399}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 662, \"l1_block_number\": 20975134, \"mana_spent\": 94500708, \"size_in_fields\": 3495, \"slot_number\": 662, \"timestamp\": 1729045655}, \"fee_header\": {\"excess_mana\": 1328370235, \"fee_asset_price_numerator\": 17339779, \"mana_used\": 94500708}, \"oracle_input\": {\"fee_asset_price_modifier\": -640884}, \"outputs\": {\"fee_asset_price_at_execution\": 11969858795, \"l1_fee_oracle_output\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 16645519796, \"blob_fee\": 1}, \"slot_of_change\": 660}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9991656742, \"congestion_multiplier\": 4731267685, \"data_cost\": 12, \"gas_cost\": 517115927, \"proving_cost\": 2160702325}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 834734721, \"congestion_multiplier\": 4731267685, \"data_cost\": 1, \"gas_cost\": 43201506, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1306468433, \"fee_asset_price_numerator\": 17980663, \"mana_used\": 121901802}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 663, \"l1_block_number\": 20975137, \"mana_spent\": 104753001, \"size_in_fields\": 3495, \"slot_number\": 663, \"timestamp\": 1729045691}, \"fee_header\": {\"excess_mana\": 1322870943, \"fee_asset_price_numerator\": 17196236, \"mana_used\": 104753001}, \"oracle_input\": {\"fee_asset_price_modifier\": -143543}, \"outputs\": {\"fee_asset_price_at_execution\": 11893391182, \"l1_fee_oracle_output\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"slot_of_change\": 665}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9847089783, \"congestion_multiplier\": 4700923621, \"data_cost\": 12, \"gas_cost\": 513812411, \"proving_cost\": 2146899009}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 827946347, \"congestion_multiplier\": 4700923621, \"data_cost\": 1, \"gas_cost\": 43201506, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1328370235, \"fee_asset_price_numerator\": 17339779, \"mana_used\": 94500708}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 664, \"l1_block_number\": 20975140, \"mana_spent\": 108047404, \"size_in_fields\": 3555, \"slot_number\": 664, \"timestamp\": 1729045727}, \"fee_header\": {\"excess_mana\": 1327623944, \"fee_asset_price_numerator\": 17077458, \"mana_used\": 108047404}, \"oracle_input\": {\"fee_asset_price_modifier\": -118778}, \"outputs\": {\"fee_asset_price_at_execution\": 11876331298, \"l1_fee_oracle_output\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"slot_of_change\": 665}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9902614845, \"congestion_multiplier\": 4727138332, \"data_cost\": 12, \"gas_cost\": 513075398, \"proving_cost\": 2143819496}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 833810930, \"congestion_multiplier\": 4727138332, \"data_cost\": 1, \"gas_cost\": 43201506, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1322870943, \"fee_asset_price_numerator\": 17196236, \"mana_used\": 104753001}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 665, \"l1_block_number\": 20975143, \"mana_spent\": 96453175, \"size_in_fields\": 3210, \"slot_number\": 665, \"timestamp\": 1729045763}, \"fee_header\": {\"excess_mana\": 1335671348, \"fee_asset_price_numerator\": 17379955, \"mana_used\": 96453175}, \"oracle_input\": {\"fee_asset_price_modifier\": 302497}, \"outputs\": {\"fee_asset_price_at_execution\": 11862233204, \"l1_fee_oracle_output\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"slot_of_change\": 665}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 10047620037, \"congestion_multiplier\": 4771856719, \"data_cost\": 12, \"gas_cost\": 522564635, \"proving_cost\": 2141274622}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 847026008, \"congestion_multiplier\": 4771856719, \"data_cost\": 1, \"gas_cost\": 44052804, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1327623944, \"fee_asset_price_numerator\": 17077458, \"mana_used\": 108047404}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 666, \"l1_block_number\": 20975146, \"mana_spent\": 102350884, \"size_in_fields\": 3420, \"slot_number\": 666, \"timestamp\": 1729045799}, \"fee_header\": {\"excess_mana\": 1332124523, \"fee_asset_price_numerator\": 17532234, \"mana_used\": 102350884}, \"oracle_input\": {\"fee_asset_price_modifier\": 152279}, \"outputs\": {\"fee_asset_price_at_execution\": 11898170430, \"l1_fee_oracle_output\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"slot_of_change\": 665}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 10025259791, \"congestion_multiplier\": 4752095569, \"data_cost\": 12, \"gas_cost\": 524147770, \"proving_cost\": 2147761720}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 842588350, \"congestion_multiplier\": 4752095569, \"data_cost\": 1, \"gas_cost\": 44052804, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1335671348, \"fee_asset_price_numerator\": 17379955, \"mana_used\": 96453175}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 667, \"l1_block_number\": 20975149, \"mana_spent\": 88042304, \"size_in_fields\": 3270, \"slot_number\": 667, \"timestamp\": 1729045835}, \"fee_header\": {\"excess_mana\": 1334475407, \"fee_asset_price_numerator\": 17858412, \"mana_used\": 88042304}, \"oracle_input\": {\"fee_asset_price_modifier\": 326178}, \"outputs\": {\"fee_asset_price_at_execution\": 11916302648, \"l1_fee_oracle_output\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13041964028, \"blob_fee\": 1}, \"slot_of_change\": 665}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 10075563161, \"congestion_multiplier\": 4765184363, \"data_cost\": 12, \"gas_cost\": 524946545, \"proving_cost\": 2151034802}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 845527632, \"congestion_multiplier\": 4765184363, \"data_cost\": 1, \"gas_cost\": 44052804, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1332124523, \"fee_asset_price_numerator\": 17532234, \"mana_used\": 102350884}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 668, \"l1_block_number\": 20975152, \"mana_spent\": 94911270, \"size_in_fields\": 3210, \"slot_number\": 668, \"timestamp\": 1729045871}, \"fee_header\": {\"excess_mana\": 1322517711, \"fee_asset_price_numerator\": 17383604, \"mana_used\": 94911270}, \"oracle_input\": {\"fee_asset_price_modifier\": -474808}, \"outputs\": {\"fee_asset_price_at_execution\": 11955234464, \"l1_fee_oracle_output\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"slot_of_change\": 670}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9930743907, \"congestion_multiplier\": 4698981218, \"data_cost\": 12, \"gas_cost\": 526661601, \"proving_cost\": 2158062459}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 830660740, \"congestion_multiplier\": 4698981218, \"data_cost\": 1, \"gas_cost\": 44052804, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1334475407, \"fee_asset_price_numerator\": 17858412, \"mana_used\": 88042304}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 669, \"l1_block_number\": 20975155, \"mana_spent\": 103433916, \"size_in_fields\": 3630, \"slot_number\": 669, \"timestamp\": 1729045907}, \"fee_header\": {\"excess_mana\": 1317428981, \"fee_asset_price_numerator\": 17803635, \"mana_used\": 103433916}, \"oracle_input\": {\"fee_asset_price_modifier\": 420031}, \"outputs\": {\"fee_asset_price_at_execution\": 11898604603, \"l1_fee_oracle_output\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"slot_of_change\": 670}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9809171441, \"congestion_multiplier\": 4671087477, \"data_cost\": 12, \"gas_cost\": 524166897, \"proving_cost\": 2147840094}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 824396790, \"congestion_multiplier\": 4671087477, \"data_cost\": 1, \"gas_cost\": 44052804, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1322517711, \"fee_asset_price_numerator\": 17383604, \"mana_used\": 94911270}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 670, \"l1_block_number\": 20975158, \"mana_spent\": 109629225, \"size_in_fields\": 3690, \"slot_number\": 670, \"timestamp\": 1729045943}, \"fee_header\": {\"excess_mana\": 1320862897, \"fee_asset_price_numerator\": 17960304, \"mana_used\": 109629225}, \"oracle_input\": {\"fee_asset_price_modifier\": 156669}, \"outputs\": {\"fee_asset_price_at_execution\": 11948687539, \"l1_fee_oracle_output\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"slot_of_change\": 670}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9745379333, \"congestion_multiplier\": 4689892170, \"data_cost\": 12, \"gas_cost\": 484220718, \"proving_cost\": 2156880661}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 815602492, \"congestion_multiplier\": 4689892170, \"data_cost\": 1, \"gas_cost\": 40525013, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1317428981, \"fee_asset_price_numerator\": 17803635, \"mana_used\": 103433916}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 671, \"l1_block_number\": 20975161, \"mana_spent\": 87129901, \"size_in_fields\": 3195, \"slot_number\": 671, \"timestamp\": 1729045979}, \"fee_header\": {\"excess_mana\": 1330492122, \"fee_asset_price_numerator\": 17510433, \"mana_used\": 87129901}, \"oracle_input\": {\"fee_asset_price_modifier\": -449871}, \"outputs\": {\"fee_asset_price_at_execution\": 11967422100, \"l1_fee_oracle_output\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"slot_of_change\": 670}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9901216864, \"congestion_multiplier\": 4743028159, \"data_cost\": 12, \"gas_cost\": 484979937, \"proving_cost\": 2160262473}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 827347509, \"congestion_multiplier\": 4743028159, \"data_cost\": 1, \"gas_cost\": 40525013, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1320862897, \"fee_asset_price_numerator\": 17960304, \"mana_used\": 109629225}}, {\"block_header\": {\"blobs_needed\": 1, \"block_number\": 672, \"l1_block_number\": 20975164, \"mana_spent\": 95155491, \"size_in_fields\": 3495, \"slot_number\": 672, \"timestamp\": 1729046015}, \"fee_header\": {\"excess_mana\": 1317622023, \"fee_asset_price_numerator\": 17658078, \"mana_used\": 95155491}, \"oracle_input\": {\"fee_asset_price_modifier\": 147645}, \"outputs\": {\"fee_asset_price_at_execution\": 11913705058, \"l1_fee_oracle_output\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"l1_gas_oracle_values\": {\"post\": {\"base_fee\": 12233966044, \"blob_fee\": 1}, \"pre\": {\"base_fee\": 13298959497, \"blob_fee\": 1}, \"slot_of_change\": 670}, \"mana_base_fee_components_in_fee_asset\": {\"congestion_cost\": 9670106377, \"congestion_multiplier\": 4672142603, \"data_cost\": 12, \"gas_cost\": 482803053, \"proving_cost\": 2150565906}, \"mana_base_fee_components_in_wei\": {\"congestion_cost\": 811679182, \"congestion_multiplier\": 4672142603, \"data_cost\": 1, \"gas_cost\": 40525013, \"proving_cost\": 180511931}}, \"parent_fee_header\": {\"excess_mana\": 1330492122, \"fee_asset_price_numerator\": 17510433, \"mana_used\": 87129901}}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Constants for the fee maths!\n",
-    "code = f\"\"\"\n",
-    "uint256 internal constant PROVING_COST_PER_MANA = {fee_model.proving_cost_per_mana.to_dict()};\n",
-    "\n",
-    "uint256 internal constant MINIMUM_FEE_ASSET_PER_ETH = {fee_model.minimum_fee_asset_per_eth.to_dict()};\n",
-    "uint256 internal constant MAX_FEE_ASSET_PRICE_MODIFIER = {FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.to_dict()};\n",
-    "uint256 internal constant FEE_ASSET_PRICE_UPDATE_FRACTION = {fee_model.fee_asset_price_update_fraction().to_dict()};\n",
-    "\"\"\"\n",
-    "print(code)\n",
-    "\n",
-    "\n",
-    "@json_serializable\n",
-    "@dataclass\n",
-    "class L1Metadata:\n",
-    "    block_number: Uint256\n",
-    "    timestamp: Uint256\n",
-    "    blob_fee: Uint256\n",
-    "    base_fee: Uint256\n",
-    "\n",
-    "\n",
-    "# Create a json object that we can throw at foundry tests\n",
-    "print(\n",
-    "    json.dumps(\n",
-    "        {\n",
-    "            \"l1_metadata\": [\n",
-    "                L1Metadata(\n",
-    "                    block_number=x.number,\n",
-    "                    timestamp=x.timestamp,\n",
-    "                    blob_fee=x.blob_fee,\n",
-    "                    base_fee=x.base_fee,\n",
-    "                ).to_dict()\n",
-    "                for x in blocks\n",
-    "            ],\n",
-    "            \"points\": [x.to_dict() for x in test_points],\n",
-    "        }\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba61e58d",
-   "metadata": {},
-   "source": [
-    "# Bandwidth\n",
-    "\n",
-    "In Etheruem, the bandwidth consumed by a validator as noted in the Eth 2.0 book is ~3.5mb/s.\n",
-    "\n",
-    "If we follow the assumption that Ethereum transactions are 700 bytes, and have 15 txs per second, the 180 transaction per block will \"require\" 0.01mb/s, so there is a good chunk of overhead etc.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "d629f420",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tx Bandwidth: 0.01 MB/s -> 0.08 mbit/s\n",
-      "ETH_FACTOR: 349.53\n"
-     ]
-    }
-   ],
-   "source": [
-    "ETH_TX_BANDWIDTH = 700\n",
-    "ETH_BLOCK_BANDWIDTH = ETH_TX_BANDWIDTH * 15\n",
-    "\n",
-    "ETH_MB_PER_SEC = ETH_BLOCK_BANDWIDTH / 1024 / 1024\n",
-    "print(f\"Tx Bandwidth: {ETH_MB_PER_SEC:.2f} MB/s -> {ETH_MB_PER_SEC * 8:.2f} mbit/s\")\n",
-    "\n",
-    "ETH_FACTOR = 3.5 / ETH_MB_PER_SEC\n",
-    "\n",
-    "print(f\"ETH_FACTOR: {ETH_FACTOR:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d2b924a",
-   "metadata": {},
-   "source": [
-    "We call this overhead for the `ETH_FACTOR`. The overhead is due to the gossiping across the nodes, the transactions that might not actually make it, the block headers etc.\n",
-    "\n",
-    "We don't expect to have as big of an overhead, for the simple reason that we do not expect to have as many nodes. Nevertheless, it is a good thing to keep in mind when thinking about the bandwidth requirements.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "398f8a74",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bandwidth: 0.78 MB/s -> 6.25 mbit/s\n",
-      "ASSUMED_AZTEC_FACTOR: 10.92\n",
-      "Bandwidth with assumed AZTEC_FACTOR: 68.27 mbit/s\n"
-     ]
-    }
-   ],
-   "source": [
-    "AZTEC_TX_BANDWIDTH = 80 * 1024\n",
-    "AZTEC_BLOCK_BANDWIDTH = AZTEC_TX_BANDWIDTH * 10\n",
-    "AZTEC_MB_PER_SEC = AZTEC_BLOCK_BANDWIDTH / 1024 / 1024\n",
-    "print(f\"Bandwidth: {AZTEC_MB_PER_SEC:.2f} MB/s -> {AZTEC_MB_PER_SEC * 8:.2f} mbit/s\")\n",
-    "\n",
-    "ASSUMED_AZTEC_FACTOR = max(1, ETH_FACTOR / 32)\n",
-    "\n",
-    "print(f\"ASSUMED_AZTEC_FACTOR: {ASSUMED_AZTEC_FACTOR:.2f}\")\n",
-    "\n",
-    "print(\n",
-    "    f\"Bandwidth with assumed AZTEC_FACTOR: {AZTEC_MB_PER_SEC * 8 * ASSUMED_AZTEC_FACTOR:.2f} mbit/s\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (fee-model)",
-   "language": "python",
-   "name": "fee-model"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/in-progress/8757-fees/notebook/fee-model.py b/in-progress/8757-fees/notebook/fee-model.py
new file mode 100644
index 0000000..7266367
--- /dev/null
+++ b/in-progress/8757-fees/notebook/fee-model.py
@@ -0,0 +1,1252 @@
+import marimo
+
+__generated_with = "0.12.8"
+app = marimo.App(width="medium", app_title="Fee Model")
+
+
+@app.cell
+def _():
+    from ape import networks
+    import matplotlib.pyplot as plt
+    import math
+
+    from typing import List, Optional, ClassVar
+    import random
+    import numpy as np
+    import pickle
+    import os
+
+    import json
+
+    from pydantic import StrictInt, field_validator, Field
+    from pydantic.dataclasses import dataclass
+    from dataclasses import fields
+    from copy import deepcopy
+    import marimo as mo
+
+    return (
+        ClassVar,
+        Field,
+        List,
+        Optional,
+        StrictInt,
+        dataclass,
+        deepcopy,
+        field_validator,
+        fields,
+        json,
+        math,
+        mo,
+        networks,
+        np,
+        os,
+        pickle,
+        plt,
+        random,
+    )
+
+
+@app.cell
+def _(deepcopy, fields):
+    def json_serializable(cls):
+        """Decorator to make a dataclass JSON serializable and copyable."""
+
+        def copy(self):
+            return deepcopy(self)
+
+        cls.copy = copy
+
+        def to_dict(self):
+            def convert_value(obj):
+                if hasattr(obj, "to_dict"):
+                    return obj.to_dict()
+                elif isinstance(obj, (list, tuple)):
+                    return [convert_value(x) for x in obj]
+                elif isinstance(obj, dict):
+                    return {k: convert_value(v) for k, v in obj.items()}
+                return obj
+
+            # Convert each field using our custom converter and sort keys
+            return dict(
+                sorted(
+                    (field.name, convert_value(getattr(self, field.name)))
+                    for field in fields(self)
+                )
+            )
+
+        cls.to_dict = to_dict
+        return cls
+
+    return (json_serializable,)
+
+
+@app.cell
+def _(StrictInt, dataclass, field_validator):
+    def bounded_int(min_value: int, max_value: int):
+        """
+        Decorator for creating bounded integer types with validation
+        Inclusive of min_value, exclusive of max_value
+        """
+
+        def decorator(cls):
+            @dataclass
+            class BoundedInt:
+                value: StrictInt
+
+                @field_validator("value")
+                def check_range(cls, v):
+                    if not (min_value <= v < max_value):
+                        raise ValueError(
+                            f"Value don't satisfy {min_value} <= {v} <= {max_value}"
+                        )
+                    return v
+
+                def to_dict(self) -> int:
+                    # Custom serialization to return just the integer value
+                    return self.value
+
+                def __eq__(self, other):
+                    if isinstance(other, BoundedInt):
+                        return self.value == other.value
+                    return False
+
+                def __ne__(self, other):
+                    return not self.__eq__(other)
+
+                def __gt__(self, other):
+                    return self.value > other.value
+
+                def __ge__(self, other):
+                    return self.value >= other.value
+
+                def __lt__(self, other):
+                    return self.value < other.value
+
+                def __le__(self, other):
+                    return self.value <= other.value
+
+                def __abs__(self):
+                    return BoundedInt(value=abs(self.value))
+
+                def __neg__(self):
+                    return BoundedInt(value=-self.value)
+
+                def __add__(self, other):
+                    if isinstance(other, BoundedInt):
+                        result = self.value + other.value
+                        if result > max_value:
+                            raise OverflowError("Integer overflow")
+                        return BoundedInt(value=result)
+                    else:
+                        raise TypeError(
+                            f"Unsupported operand type for +: '{cls.__name__}' and '{type(other)}'"
+                        )
+
+                def __sub__(self, other):
+                    if isinstance(other, BoundedInt):
+                        result = self.value - other.value
+                        if result < min_value:
+                            raise ValueError("Integer underflow")
+                        return BoundedInt(value=result)
+                    else:
+                        raise TypeError(
+                            f"Unsupported operand type for -: '{cls.__name__}' and '{type(other)}'"
+                        )
+
+                def __mul__(self, other):
+                    if isinstance(other, BoundedInt):
+                        result = self.value * other.value
+                        if result > max_value:
+                            raise OverflowError("Integer overflow")
+                        return BoundedInt(value=result)
+                    else:
+                        raise TypeError(
+                            f"Unsupported operand type for *: '{cls.__name__}' and '{type(other)}'"
+                        )
+
+                def __truediv__(self, other):
+                    if isinstance(other, BoundedInt):
+                        if other.value == 0:
+                            raise ZeroDivisionError("Division by zero")
+                        return BoundedInt(value=self.value // other.value)
+                    else:
+                        raise TypeError(
+                            f"Unsupported operand type for /: '{cls.__name__}' and '{type(other)}'"
+                        )
+
+                def mul_div(self, other, denominator, round_up=False):
+                    temp = self.value * other.value
+                    result = temp // denominator.value
+                    if round_up and temp % denominator.value != 0:
+                        result += 1
+                    return BoundedInt(value=result)
+
+            # Copy the class name and update annotations
+            BoundedInt.__name__ = cls.__name__
+            BoundedInt.__qualname__ = cls.__qualname__
+            return BoundedInt
+
+        return decorator
+
+    @bounded_int(min_value=0, max_value=2**256 - 1)
+    class Uint256:
+        pass
+
+    @bounded_int(min_value=-(2**255), max_value=2**255 - 1)
+    class Int256:
+        pass
+
+    return Int256, Uint256, bounded_int
+
+
+@app.cell
+def _(Uint256):
+    MIN_BASE_FEE_PER_BLOB_GAS = Uint256(1)
+    BLOB_BASE_FEE_UPDATE_FRACTION = Uint256(3338477)
+    BLOB_SIZE_IN_FIELDS = Uint256(4096)
+    GAS_PER_BLOB = Uint256(2**17)
+
+    def fake_exponential(
+        factor: Uint256, numerator: Uint256, denominator: Uint256
+    ) -> Uint256:
+        """
+        An approximation of the exponential function: factor * e ** (numerator / denominator)
+        Approximated using a taylor series.
+        For shorthand below, let `a = factor`, `x = numerator`, `d = denominator`
+
+        f(x) =  a
+             + (a * x) / d
+             + (a * x ** 2) / (2 * d ** 2)
+             + (a * x ** 3) / (6 * d ** 3)
+             + (a * x ** 4) / (24 * d ** 4)
+             + (a * x ** 5) / (120 * d ** 5)
+             + ...
+
+        For integer precision purposes, we will multiply by the denominator for intermediary steps and then finally do a division by it.
+        The notation below might look slightly strange, but it is to try to convey the program flow below.
+
+        e(x) = (       a * d
+             +         a * d * x / d
+             +       ((a * d * x / d) * x) / (2 * d)
+             +     ((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)
+             +   ((((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)) * x) / (4 * d)
+             + ((((((((a * d * x / d) * x) / (2 * d)) * x) / (3 * d)) * x) / (4 * d)) * x) / (5 * d)
+             + ...
+               ) / d
+
+        While the notation might make it a bit of a pain to look at. f(x) and e(x) are the same, gotta lover integer math.
+        """
+        i = Uint256(1)
+        output = Uint256(0)
+        numerator_accum = factor * denominator
+        while numerator_accum > Uint256(0):
+            output += numerator_accum
+            numerator_accum = (numerator_accum * numerator) / (denominator * i)
+            i += Uint256(1)
+        return output / denominator
+
+    # Small check to see if the fake exponential is working as intended
+    a = Uint256(5415357955)
+    b = Uint256(2611772262)
+    c = Uint256(100000000000)
+    d = fake_exponential(a, b, c)
+    e = Uint256(5558657961)
+    assert d == e, f"Expected {d} to be {e}"
+    return (
+        BLOB_BASE_FEE_UPDATE_FRACTION,
+        BLOB_SIZE_IN_FIELDS,
+        GAS_PER_BLOB,
+        MIN_BASE_FEE_PER_BLOB_GAS,
+        a,
+        b,
+        c,
+        d,
+        e,
+        fake_exponential,
+    )
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        # Collecting data
+
+        In the following section we will be collecting data from an Etheruem node. If it is the same range as we last collected we will simply load it from a pickle file.
+
+        Note that we only keep track of minimal information related to the L1 block.
+        """
+    )
+    return
+
+
+@app.cell
+def _(
+    BLOB_BASE_FEE_UPDATE_FRACTION,
+    MIN_BASE_FEE_PER_BLOB_GAS,
+    Uint256,
+    dataclass,
+    fake_exponential,
+    json_serializable,
+    mo,
+    networks,
+    os,
+    pickle,
+):
+    @json_serializable
+    @dataclass
+    class L1BlockSub:
+        number: Uint256
+        timestamp: Uint256
+        blob_fee: Uint256
+        base_fee: Uint256
+        excess_blob_gas: Uint256
+
+    def get_l1_block_sub(block_number: int) -> L1BlockSub:
+        block = networks.provider.web3.eth.get_block(block_number)
+        blob_fee = fake_exponential(
+            MIN_BASE_FEE_PER_BLOB_GAS,
+            Uint256(block.excessBlobGas),
+            BLOB_BASE_FEE_UPDATE_FRACTION,
+        )
+        return L1BlockSub(
+            number=Uint256(block.number),
+            blob_fee=blob_fee,
+            base_fee=Uint256(block.baseFeePerGas),
+            excess_blob_gas=Uint256(block.excessBlobGas),
+            timestamp=Uint256(block.timestamp),
+        )
+
+    @mo.cache
+    def get_blocks(start_number: int, number_of_blocks: int):
+        if os.path.exists("blocks.pkl"):
+            with open("blocks.pkl", "rb") as f:
+                candidated_blocks = pickle.load(f)
+                if (
+                    candidated_blocks[0].number.value == start_number
+                    and candidated_blocks[-1].number.value
+                    == start_number + number_of_blocks
+                ):
+                    return candidated_blocks
+        networks.parse_network_choice("ethereum:mainnet:node").__enter__()
+        blocks = [
+            get_l1_block_sub(i)
+            for i in range(start_number, start_number + number_of_blocks + 1)
+        ]
+        with open("blocks.pkl", "wb") as f:
+            pickle.dump(blocks, f)
+        return blocks
+
+    # You should not change these unless you have access to the endpoint in the `ape-config.yaml` file.
+    # This is because we are using the `ape` library to fetch the data from the Ethereum node, or just
+    # fetching the data from a pickle file otherwise if we already have it.
+
+    block_start_number = 20973664 - 500
+    blocks_to_pull = 2000
+
+    blocks = get_blocks(block_start_number, blocks_to_pull)
+    return (
+        L1BlockSub,
+        block_start_number,
+        blocks,
+        blocks_to_pull,
+        get_blocks,
+        get_l1_block_sub,
+    )
+
+
+@app.cell
+def _(blocks, plt):
+    block_numbers = [b.number.value for b in blocks]
+    plt.figure(figsize=(12, 6))
+    plt.plot(
+        block_numbers, [b.blob_fee.value for b in blocks], label="Blob Gas Price (wei)"
+    )
+    plt.plot(block_numbers, [b.base_fee.value for b in blocks], label="Base Fee (wei)")
+    plt.xlabel("Block Number")
+    plt.ylabel("Fee (wei)")
+    plt.title("Blob Gas Price and Base Fee over Recent Blocks")
+    plt.legend()
+    plt.grid(True)
+    return (block_numbers,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        # Model
+
+        We want a model where the `mana` that a transaction requires is **constant** and independent of the l1 cost, but with the base fee fluctuating.  
+        This is slightly different from the previously specified model, where the `mana` amount itself could also fluctuate, but it is simpler for the users if we have a fixed amount.  
+        Also it will avoid landing in an issue where your tx is now out of gas because the l1 cost increased.
+        """
+    )
+    return
+
+
+@app.cell
+def _(
+    Field,
+    Int256,
+    List,
+    Optional,
+    Uint256,
+    dataclass,
+    fake_exponential,
+    json_serializable,
+    math,
+):
+    @dataclass
+    class Tx:
+        mana_spent: Uint256
+
+        nullifiers: Uint256 = Field(default_factory=lambda: Uint256(1))
+        notes: Uint256 = Field(default_factory=lambda: Uint256(0))
+        public_state_diffs: Uint256 = Field(
+            default_factory=lambda: Uint256(1 + 2 + 2 + 2)
+        )
+        encrypted_logs_size: Uint256 = Field(default_factory=lambda: Uint256(256))
+
+        def size_in_bytes(self) -> Uint256:
+            return (
+                Uint256(32) * self.nullifiers
+                + Uint256(64) * self.public_state_diffs
+                + self.notes * (Uint256(32) + self.encrypted_logs_size)
+            )
+
+        def size_in_fields(self) -> Uint256:
+            return Uint256(math.ceil(self.size_in_bytes().value / 32))
+
+    @json_serializable
+    @dataclass
+    class BlockHeader:
+        l1_block_number: Uint256
+        block_number: Uint256
+        slot_number: Uint256
+        timestamp: Uint256
+        mana_spent: Uint256
+        blobs_needed: Uint256
+        size_in_fields: Uint256
+
+    @dataclass
+    class Block:
+        l1_block_number: Uint256
+        block_number: Uint256
+        slot_number: Uint256
+        timestamp: Uint256
+        txs: list[Tx]
+
+        def size_in_fields(self) -> Uint256:
+            return sum((tx.size_in_fields() for tx in self.txs), start=Uint256(0))
+
+        def mana_spent(self) -> Uint256:
+            return sum((tx.mana_spent for tx in self.txs), start=Uint256(0))
+
+        def blobs_needed(self) -> Uint256:
+            # Note that we cast this slightly different as we want the ceiling of the division
+            return Uint256(math.ceil(self.size_in_fields().value / 4096))
+
+        def compute_header(self) -> BlockHeader:
+            return BlockHeader(
+                l1_block_number=self.l1_block_number,
+                block_number=self.block_number,
+                slot_number=self.slot_number,
+                timestamp=self.timestamp,
+                mana_spent=self.mana_spent(),
+                blobs_needed=self.blobs_needed(),
+                size_in_fields=self.size_in_fields(),
+            )
+
+    @json_serializable
+    @dataclass
+    class L1Fees:
+        blob_fee: Uint256
+        base_fee: Uint256
+
+    @json_serializable
+    @dataclass
+    class L1GasOracle:
+        LIFETIME = Uint256(5)
+        LATENCY = Uint256(2)
+
+        pre: L1Fees
+        post: L1Fees
+        slot_of_change: Uint256
+
+        def value_at(self, slot_number: Uint256) -> L1Fees:
+            if slot_number < self.slot_of_change:
+                return self.pre
+            return self.post
+
+        def queue_change(self, slot_number: Uint256, fees: L1Fees):
+            # If the value have been active `self.LIFETIME - self.LATENCY` we can queue the next
+            if slot_number >= self.slot_of_change + (self.LIFETIME - self.LATENCY):
+                self.pre = self.post
+                self.post = fees
+                # This value should at the earliest be equal to the slot_of_change + LIFETIME.
+                assert slot_number + self.LATENCY <= self.slot_of_change + self.LIFETIME
+                self.slot_of_change = slot_number + self.LATENCY
+
+    @json_serializable
+    @dataclass
+    class FeeHeader:
+        excess_mana: Uint256 = Field(default_factory=lambda: Uint256(0))
+        mana_used: Uint256 = Field(default_factory=lambda: Uint256(0))
+        fee_asset_price_numerator: Uint256 = Field(default_factory=lambda: Uint256(0))
+
+    @json_serializable
+    @dataclass
+    class ManaBaseFeeComponents:
+        sequencer_cost: Uint256
+        prover_cost: Uint256
+        congestion_cost: Uint256
+        congestion_multiplier: Uint256
+
+    @json_serializable
+    @dataclass
+    class OracleInput:
+        fee_asset_price_modifier: Int256
+
+    @dataclass
+    class FeeModel:
+        """
+        The fee model here will not be perfect, it does not take into account for example that there might be slots with no txs, in reality
+        it should be possible for the model, to take this into account mainly impact the `calc_excess_mana` function.
+        """
+
+        AZTEC_SLOT_DURATION = Uint256(36)
+        AZTEC_EPOCH_DURATION = Uint256(32)
+        MAX_FEE_ASSET_PRICE_MODIFIER = Int256(int(1e6))
+        CONGESTION_MULTIPLIER_DIVISOR = Uint256(int(1e9))
+        GAS_PER_BLOB = Uint256(2**17)
+
+        mana_target: Uint256
+        l1_gas_per_block_proposed: Uint256
+        l1_gas_per_epoch_verified: Uint256
+        minimum_fee_asset_per_eth: Uint256
+        proving_cost_per_mana: Uint256
+
+        genesis_timestamp: Uint256
+
+        # Below is mutable
+        current_timestamp: Uint256
+        l1_gas_oracle: L1GasOracle
+        fee_headers: List[FeeHeader] = Field(default_factory=lambda: [FeeHeader()])
+
+        def set_timestamp(self, timestamp: Uint256):
+            self.current_timestamp = timestamp
+
+        def current_slot_number(self) -> Uint256:
+            return (
+                self.current_timestamp - self.genesis_timestamp
+            ) / FeeModel.AZTEC_SLOT_DURATION
+
+        def photograph(self, l1_fees: L1Fees):
+            self.l1_gas_oracle.queue_change(self.current_slot_number(), l1_fees)
+
+        def current_l1_fees(self) -> L1Fees:
+            return self.l1_gas_oracle.value_at(self.current_slot_number())
+
+        def fee_update_fraction(self) -> Uint256:
+            """
+            A bit of magic for the fake exponential and integer math. Computing the divisor this way should ensure
+            that the multiplier will increase by at most a factor of ~ 1.125 every block.
+            """
+            return Uint256(int(self.mana_target.value / 0.117))
+
+        def fee_asset_price_update_fraction(self) -> Uint256:
+            return Uint256(FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.value * int(100))
+
+        def compute_sequencer_costs(
+            self, block: Optional[Block], real=False
+        ) -> Uint256:
+            l1_fees = self.current_l1_fees()
+
+            l1_gas = self.l1_gas_per_block_proposed + (
+                block.blobs_needed() if block else Uint256(3)
+            ) * Uint256(50_000)
+            execution = l1_gas * l1_fees.base_fee
+
+            blob_gas = (
+                (block.blobs_needed() if block else Uint256(3)) * FeeModel.GAS_PER_BLOB
+                if real
+                else (
+                    block.size_in_fields() * Uint256(32)
+                    if block
+                    else Uint256(3) * FeeModel.GAS_PER_BLOB
+                )
+            )
+            data = blob_gas * l1_fees.blob_fee
+
+            return (execution + data).mul_div(
+                Uint256(1), self.mana_target, round_up=True
+            )
+
+        def compute_prover_costs(self):
+            l1_fees = self.current_l1_fees()
+            l1_gas = self.l1_gas_per_epoch_verified
+            execution = l1_gas.mul_div(
+                l1_fees.base_fee, FeeModel.AZTEC_EPOCH_DURATION, round_up=True
+            ).mul_div(Uint256(1), self.mana_target, round_up=True)
+
+            return execution + self.proving_cost_per_mana
+
+        def fee_asset_price(self) -> Uint256:
+            """
+            @TODO write up a proper explanation for this.
+            Essentially the amount of fee asset per ether gwei. Uses 1e9 in precision.
+            A value > 1e9 means that the fee asset is cheaper than ETH
+            A value < 1e9 means that the fee asset is more expensive than ETH
+            A value = 1e9 means that the fee asset is the same as ETH
+            """
+            return fake_exponential(
+                self.minimum_fee_asset_per_eth,
+                self.fee_headers[-1].fee_asset_price_numerator,
+                self.fee_asset_price_update_fraction(),
+            )
+
+        def mana_base_fee_components(
+            self, block: Optional[Block], in_fee_asset: bool = False
+        ) -> ManaBaseFeeComponents:
+            sequencer_cost = self.compute_sequencer_costs(block, real=True)
+            prover_cost = self.compute_prover_costs()
+
+            congestion_multiplier = fake_exponential(
+                Uint256(int(1e9)), self.calc_excess_mana(), self.fee_update_fraction()
+            )
+
+            total = sequencer_cost + prover_cost
+
+            congestion_cost = (
+                total * congestion_multiplier / FeeModel.CONGESTION_MULTIPLIER_DIVISOR
+            ) - total
+
+            # With precision of 1e9
+            precision = Uint256(int(1e9))
+            fee_asset_price = self.fee_asset_price() if in_fee_asset else precision
+
+            # We round up the components to ensure that the fee is always enough
+            # Note that this might lead to overpayment of #components wei per mana.
+            return ManaBaseFeeComponents(
+                sequencer_cost=sequencer_cost.mul_div(
+                    fee_asset_price, precision, round_up=True
+                ),
+                prover_cost=prover_cost.mul_div(
+                    fee_asset_price, precision, round_up=True
+                ),
+                congestion_cost=congestion_cost.mul_div(
+                    fee_asset_price, precision, round_up=True
+                ),
+                congestion_multiplier=congestion_multiplier,
+            )
+
+        def mana_base_fee(
+            self,
+            block: Optional[Block],
+            apply_congestion_multiplier=False,
+            in_fee_asset: bool = False,
+        ) -> Uint256:
+            """
+            Return the base fee of mana in wei.
+            We assume that there is a minimum amount of mana spent per block, to the sequencer to manipulate the fee completely.
+            """
+            components = self.mana_base_fee_components(block, in_fee_asset)
+
+            if apply_congestion_multiplier:
+                return (
+                    components.sequencer_cost
+                    + components.prover_cost
+                    + components.congestion_cost
+                )
+            return components.sequencer_cost + components.prover_cost
+
+        def calc_excess_mana(self) -> Uint256:
+            """
+            Calculate the excess mana in the last block.
+            Should be updated to take into account that there might be slots with no txs.
+            """
+            spent = self.fee_headers[-1].mana_used
+            excess = self.fee_headers[-1].excess_mana
+            if excess + spent < self.mana_target:
+                return Uint256(0)
+            return excess + spent - self.mana_target
+
+        def add_slot(
+            self, block: Optional[Block], oracle_input: Optional[OracleInput] = None
+        ):
+            """
+            Potentially add a block for a slot, if there is one.
+            """
+            assert (
+                block is None or block.slot_number == self.current_slot_number()
+            ), f"invalid slot number {block.slot_number} != {self.current_slot_number()}"
+
+            assert block is None or block.mana_spent() <= self.mana_target * Uint256(
+                2
+            ), "invalid block size"
+
+            fee_asset_price_modifier = (
+                oracle_input.fee_asset_price_modifier if oracle_input else Int256(0)
+            )
+            assert fee_asset_price_modifier is None or (
+                abs(fee_asset_price_modifier) <= FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER
+            ), "invalid fee asset price modifier"
+
+            parent_fee_header = self.fee_headers[-1]
+            new_header = FeeHeader(
+                excess_mana=self.calc_excess_mana(),
+                mana_used=block.mana_spent() if block else Uint256(0),
+                fee_asset_price_numerator=Uint256(
+                    max(
+                        fee_asset_price_modifier.value
+                        + parent_fee_header.fee_asset_price_numerator.value,
+                        0,
+                    )
+                ),
+            )
+            self.fee_headers.append(new_header)
+
+    return (
+        Block,
+        BlockHeader,
+        FeeHeader,
+        FeeModel,
+        L1Fees,
+        L1GasOracle,
+        ManaBaseFeeComponents,
+        OracleInput,
+        Tx,
+    )
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        ## Simulating some blocks
+
+        In the following, we will be simulating a bunch of blocks and then plotting our results.
+
+        For the simulation of a block, we will be generating a random block, with mana spent being a random number between 0 and 2 \* mana_target.  
+        We will keep collecting transactions from a "randomized" mempool until we have either reached the mana target, or the mempool size limit.  
+        We only collect transactions find the fee acceptable (another sampling) and don't force us beyond the limit.  
+        The size of the transactions are also drawn from a distribution.
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        > This is probably the place where it would make good sense to get someone that know thier stuff in.  
+        > We have a setup that could work, but at the same time, it does not seem to work perfectly well.
+        """
+    )
+    return
+
+
+@app.cell
+def _(Uint256):
+    USD_PER_ETH = 2638
+    USD_PER_WEI = USD_PER_ETH / 1e18
+    MANA_PER_BASE_TX = Uint256(21000)
+    USD_PER_BASE_TX = 0.01  # 0.3 is the real value, this is me messing around # a value kinda pulled out the ass as of now, expecting big reductions
+
+    USD_PER_MANA = USD_PER_BASE_TX / MANA_PER_BASE_TX.value
+
+    WEI_PER_MANA = USD_PER_MANA / USD_PER_WEI
+
+    print(f"WEI PER MANA: {WEI_PER_MANA:.2f}")
+    print(f"GWEI PER MANA / 1e9: {WEI_PER_MANA / 1e9:.2f} gwei")
+
+    print(f"The cost: {2638 * MANA_PER_BASE_TX.value * WEI_PER_MANA / 1e18:.2f} USD")
+    return (
+        MANA_PER_BASE_TX,
+        USD_PER_BASE_TX,
+        USD_PER_ETH,
+        USD_PER_MANA,
+        USD_PER_WEI,
+        WEI_PER_MANA,
+    )
+
+
+@app.cell
+def _(
+    BlockHeader,
+    FeeHeader,
+    L1Fees,
+    L1GasOracle,
+    ManaBaseFeeComponents,
+    OracleInput,
+    Uint256,
+    dataclass,
+    json_serializable,
+):
+    @json_serializable
+    @dataclass
+    class TestPointOutputs:
+        fee_asset_price_at_execution: Uint256
+        mana_base_fee_components_in_wei: ManaBaseFeeComponents
+        mana_base_fee_components_in_fee_asset: ManaBaseFeeComponents
+        l1_fee_oracle_output: L1Fees  # value_at(now)
+        l1_gas_oracle_values: L1GasOracle
+
+    @json_serializable
+    @dataclass
+    class TestPoint:
+        block_header: BlockHeader
+        parent_fee_header: FeeHeader
+        fee_header: FeeHeader
+        oracle_input: OracleInput
+        outputs: TestPointOutputs
+
+    return TestPoint, TestPointOutputs
+
+
+@app.cell
+def _(
+    Block,
+    FeeModel,
+    Int256,
+    L1Fees,
+    L1GasOracle,
+    MANA_PER_BASE_TX,
+    OracleInput,
+    TestPoint,
+    TestPointOutputs,
+    Tx,
+    Uint256,
+    WEI_PER_MANA,
+    blocks,
+    random,
+):
+    fee_model = FeeModel(
+        mana_target=Uint256(int(1e8)),
+        l1_gas_per_block_proposed=Uint256(int(0.15e6)),
+        l1_gas_per_epoch_verified=Uint256(int(1e6)),
+        proving_cost_per_mana=Uint256(int(WEI_PER_MANA)),
+        minimum_fee_asset_per_eth=Uint256(int(10e9)),
+        l1_gas_oracle=L1GasOracle(
+            pre=L1Fees(blob_fee=Uint256(1), base_fee=Uint256(int(1e9))),
+            post=L1Fees(
+                blob_fee=blocks[0].blob_fee,
+                base_fee=blocks[0].base_fee,
+            ),
+            slot_of_change=L1GasOracle.LIFETIME,
+        ),
+        genesis_timestamp=blocks[0].timestamp - FeeModel.AZTEC_SLOT_DURATION,
+        current_timestamp=blocks[0].timestamp,
+    )
+
+    def generate_random_with_min(
+        mean: Uint256, std_dev: Uint256, min_value: Uint256
+    ) -> Uint256:
+        while True:
+            value = int(random.gauss(mean.value, std_dev.value))
+            if value >= min_value.value:
+                return Uint256(value)
+
+    MEMPOOL_SIZE = 5000
+
+    l2_blocks = []
+    test_points = []
+
+    last_slot = Uint256(0)
+
+    for l1_block in blocks:
+        fee_model.set_timestamp(l1_block.timestamp)
+        # We try to photograph the l1 fees at every l1 block
+        fee_model.photograph(
+            L1Fees(blob_fee=l1_block.blob_fee, base_fee=l1_block.base_fee)
+        )
+
+        slot_number = fee_model.current_slot_number()
+
+        # We are in the next slot, let us create a block!
+        if slot_number > last_slot:
+            last_slot = slot_number
+
+            cost = fee_model.mana_base_fee_components(None)
+            cost_in_fee_asset = fee_model.mana_base_fee_components(
+                None, in_fee_asset=True
+            )
+
+            real_cost = cost.sequencer_cost + cost.prover_cost
+            mana_base_fee = real_cost + cost.congestion_cost
+
+            mana_spent_block = Uint256(0)
+            mana_planned_for_block = min(
+                generate_random_with_min(
+                    fee_model.mana_target,
+                    fee_model.mana_target,
+                    Uint256(0),
+                ),
+                fee_model.mana_target * Uint256(2),
+            )
+
+            txs = []
+            count = 0
+
+            while (
+                abs(mana_planned_for_block.value - mana_spent_block.value)
+                >= MANA_PER_BASE_TX.value
+                and count < MEMPOOL_SIZE
+            ):
+                count += 1
+                mana_spent_tx = generate_random_with_min(
+                    MANA_PER_BASE_TX * Uint256(2), Uint256(500_000), MANA_PER_BASE_TX
+                )
+                within_bounds = (
+                    mana_spent_tx + mana_spent_block
+                    <= fee_model.mana_target * Uint256(2)
+                )
+                acceptable_mana_base_fee = generate_random_with_min(
+                    real_cost, Uint256(2) * real_cost, Uint256(0)
+                )
+
+                is_fee_acceptable = acceptable_mana_base_fee >= mana_base_fee
+
+                if within_bounds and is_fee_acceptable:
+                    txs.append(Tx(mana_spent=mana_spent_tx))
+                    mana_spent_block += mana_spent_tx
+
+            block = Block(
+                l1_block_number=l1_block.number,
+                timestamp=l1_block.timestamp,
+                slot_number=slot_number,
+                block_number=Uint256(len(l2_blocks) + 1),
+                txs=txs,
+            )
+
+            # Deciding oracle movements. Here we assume that there are some fluctuation in the price of ETH and proving, up to 1% per 36 second.
+            oracle_input = OracleInput(
+                fee_asset_price_modifier=Int256(
+                    int(
+                        max(-1, min(1, random.gauss(0.01, 0.5)))
+                        * FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.value
+                    )
+                ),
+            )
+
+            fee_asset_price_at_execution = fee_model.fee_asset_price()
+            fee_model.add_slot(block, oracle_input)
+
+            test_points.append(
+                TestPoint(
+                    block_header=block.compute_header(),
+                    fee_header=fee_model.fee_headers[-1],
+                    parent_fee_header=fee_model.fee_headers[-2],
+                    oracle_input=oracle_input,
+                    outputs=TestPointOutputs(
+                        fee_asset_price_at_execution=fee_asset_price_at_execution,
+                        mana_base_fee_components_in_wei=cost,
+                        mana_base_fee_components_in_fee_asset=cost_in_fee_asset,
+                        l1_fee_oracle_output=fee_model.current_l1_fees(),
+                        l1_gas_oracle_values=fee_model.l1_gas_oracle.copy(),
+                    ),
+                )
+            )
+
+            l2_blocks.append(block)
+    return (
+        MEMPOOL_SIZE,
+        acceptable_mana_base_fee,
+        block,
+        cost,
+        cost_in_fee_asset,
+        count,
+        fee_asset_price_at_execution,
+        fee_model,
+        generate_random_with_min,
+        is_fee_acceptable,
+        l1_block,
+        l2_blocks,
+        last_slot,
+        mana_base_fee,
+        mana_planned_for_block,
+        mana_spent_block,
+        mana_spent_tx,
+        oracle_input,
+        real_cost,
+        slot_number,
+        test_points,
+        txs,
+        within_bounds,
+    )
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""### Plotting the results""")
+    return
+
+
+@app.cell
+def _(block_numbers, blocks, l2_blocks, plt, test_points):
+    def create_plots():
+
+        fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(
+            5, 1, figsize=(12, 12), sharex=True
+        )
+
+        # For this, if we took our oracle, that would probably also be pretty cool he.
+
+        aztec_l1_block_numbers = [
+            l2_block.l1_block_number.value for l2_block in l2_blocks
+        ]
+
+        act = ax1
+        act.plot(
+            block_numbers,
+            [l1_block.base_fee.value for l1_block in blocks],
+            label="L1 BaseFee (wei)",
+        )
+        act.plot(
+            aztec_l1_block_numbers,
+            [x.outputs.l1_fee_oracle_output.base_fee.value for x in test_points],
+            label="L1 BaseFee Oracle (wei)",
+        )
+        act.set_ylabel("Gas BaseFee (wei)")
+        act.set_title("Gas base fees over Recent Blocks")
+        act.legend()
+        act.grid(True)
+
+        # I need to load the different components of the cost from my test points
+
+        costs = [x.outputs.mana_base_fee_components_in_wei for x in test_points]
+        costs_fee_asset = [
+            x.outputs.mana_base_fee_components_in_fee_asset for x in test_points
+        ]
+
+        sequencer_costs = [cost.sequencer_cost.value for cost in costs]
+        prover_costs = [cost.prover_cost.value for cost in costs]
+        congestion_costs = [cost.congestion_cost.value for cost in costs]
+
+        act = ax2
+
+        act.plot(aztec_l1_block_numbers, sequencer_costs, label="Sequencer cost (wei)")
+        act.plot(aztec_l1_block_numbers, prover_costs, label="Prover cost (wei)")
+
+        act.set_ylabel("Mana BaseFee (wei)")
+        act.set_title("Mana Base Fee Components over Recent Blocks")
+        act.legend()
+        act.grid(True)
+
+        real_costs = [
+            sequencer_costs[i] + prover_costs[i] for i in range(len(sequencer_costs))
+        ]
+        total_costs = [
+            real_costs[i] + congestion_costs[i] for i in range(len(sequencer_costs))
+        ]
+
+        act = ax3
+        act.fill_between(aztec_l1_block_numbers, 0, real_costs, label="Real cost (wei)")
+        act.fill_between(
+            aztec_l1_block_numbers,
+            real_costs,
+            total_costs,
+            label="Congestion cost (wei)",
+        )
+        act.set_ylabel("Mana BaseFee (wei)")
+        act.set_title("Fee over the recent blocks (Ether)")
+        act.legend()
+        act.grid(True)
+
+        real_costs = [
+            c.sequencer_cost.value + c.prover_cost.value for c in costs_fee_asset
+        ]
+        total_costs = [
+            real_costs[i] + costs_fee_asset[i].congestion_cost.value
+            for i in range(len(real_costs))
+        ]
+        act = ax4
+        act.fill_between(aztec_l1_block_numbers, 0, real_costs, label="Real cost (wei)")
+        act.fill_between(
+            aztec_l1_block_numbers,
+            real_costs,
+            total_costs,
+            label="Congestion cost (wei)",
+        )
+        act.set_ylabel("Mana BaseFee (wei)")
+        act.set_title("Fee over the recent blocks (fee asset)")
+        act.legend()
+        act.grid(True)
+
+        act = ax5
+
+        t_0 = [len(b.txs) for b in l2_blocks]
+        t_1 = [b.mana_spent().value for b in l2_blocks]
+        flem_2 = act.twinx()
+        (l1,) = act.plot(
+            aztec_l1_block_numbers,
+            t_0,
+            label="L2 number of transactions",
+            color="green",
+            linewidth=0.5,
+        )
+        (l2,) = flem_2.plot(
+            aztec_l1_block_numbers,
+            t_1,
+            label="L2 mana spent",
+            color="blue",
+            linewidth=0.5,
+        )
+        act.set_xlabel("Block Number")
+        act.set_ylabel("Number of transactions")
+        flem_2.set_ylabel("Mana Spent")
+        act.set_title("Number of transactions and Mana Spent per Block")
+        act.legend([l1, l2], ["L2 number of transactions", "L2 mana spent"])
+        act.grid(True)
+
+        plt.tight_layout()
+
+        return ax1, ax2, ax3, ax4, ax5
+
+    create_plots()
+    return (create_plots,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        ## Points of interest
+
+        - The multiplier makes the base fee more unstable, it might be better to consider a different function to see if we can stabilize it.
+        """
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""## To test with sol""")
+    return
+
+
+@app.cell
+def _(FeeModel, fee_model):
+    # Constants for the fee maths!
+    def get_solidity_code():
+        return f"""
+        uint256 internal constant PROVING_COST_PER_MANA = {fee_model.proving_cost_per_mana.to_dict()};
+
+        uint256 internal constant MINIMUM_FEE_ASSET_PER_ETH = {fee_model.minimum_fee_asset_per_eth.to_dict()};
+        uint256 internal constant MAX_FEE_ASSET_PRICE_MODIFIER = {FeeModel.MAX_FEE_ASSET_PRICE_MODIFIER.to_dict()};
+        uint256 internal constant FEE_ASSET_PRICE_UPDATE_FRACTION = {fee_model.fee_asset_price_update_fraction().to_dict()};
+        """
+
+    get_solidity_code()
+    return (get_solidity_code,)
+
+
+@app.cell
+def _(
+    Uint256,
+    blocks,
+    dataclass,
+    fee_model,
+    json,
+    json_serializable,
+    test_points,
+):
+    @json_serializable
+    @dataclass
+    class L1Metadata:
+        block_number: Uint256
+        timestamp: Uint256
+        blob_fee: Uint256
+        base_fee: Uint256
+
+    def get_json():
+        return {
+            "l1_metadata": [
+                L1Metadata(
+                    block_number=x.number,
+                    timestamp=x.timestamp,
+                    blob_fee=x.blob_fee,
+                    base_fee=x.base_fee,
+                ).to_dict()
+                for x in blocks
+            ],
+            "points": [x.to_dict() for x in test_points],
+            "proving_cost": fee_model.proving_cost_per_mana.to_dict(),
+        }
+
+    def get_dump():
+        return json.dumps(get_json())
+
+    # Create a json object that we can throw at foundry tests
+    get_dump()
+    return L1Metadata, get_dump, get_json
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        # Bandwidth
+
+        In Etheruem, the bandwidth consumed by a validator as noted in the Eth 2.0 book is ~3.5mb/s.
+
+        If we follow the assumption that Ethereum transactions are 700 bytes, and have 15 txs per second, the 180 transaction per block will "require" 0.01mb/s, so there is a good chunk of overhead etc.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    ETH_TX_BANDWIDTH = 700
+    ETH_BLOCK_BANDWIDTH = ETH_TX_BANDWIDTH * 15
+
+    ETH_MB_PER_SEC = ETH_BLOCK_BANDWIDTH / 1024 / 1024
+    print(f"Tx Bandwidth: {ETH_MB_PER_SEC:.2f} MB/s -> {ETH_MB_PER_SEC * 8:.2f} mbit/s")
+
+    ETH_FACTOR = 3.5 / ETH_MB_PER_SEC
+
+    print(f"ETH_FACTOR: {ETH_FACTOR:.2f}")
+    return ETH_BLOCK_BANDWIDTH, ETH_FACTOR, ETH_MB_PER_SEC, ETH_TX_BANDWIDTH
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(
+        r"""
+        We call this overhead for the `ETH_FACTOR`. The overhead is due to the gossiping across the nodes, the transactions that might not actually make it, the block headers etc.
+
+        We don't expect to have as big of an overhead, for the simple reason that we do not expect to have as many nodes. Nevertheless, it is a good thing to keep in mind when thinking about the bandwidth requirements.
+        """
+    )
+    return
+
+
+@app.cell
+def _(ETH_FACTOR):
+    AZTEC_TX_BANDWIDTH = 80 * 1024
+    AZTEC_BLOCK_BANDWIDTH = AZTEC_TX_BANDWIDTH * 10
+    AZTEC_MB_PER_SEC = AZTEC_BLOCK_BANDWIDTH / 1024 / 1024
+    print(
+        f"Bandwidth: {AZTEC_MB_PER_SEC:.2f} MB/s -> {AZTEC_MB_PER_SEC * 8:.2f} mbit/s"
+    )
+
+    ASSUMED_AZTEC_FACTOR = max(1, ETH_FACTOR / 32)
+
+    print(f"ASSUMED_AZTEC_FACTOR: {ASSUMED_AZTEC_FACTOR:.2f}")
+
+    print(
+        f"Bandwidth with assumed AZTEC_FACTOR: {AZTEC_MB_PER_SEC * 8 * ASSUMED_AZTEC_FACTOR:.2f} mbit/s"
+    )
+    return (
+        ASSUMED_AZTEC_FACTOR,
+        AZTEC_BLOCK_BANDWIDTH,
+        AZTEC_MB_PER_SEC,
+        AZTEC_TX_BANDWIDTH,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/in-progress/8757-fees/notebook/pyproject.toml b/in-progress/8757-fees/notebook/pyproject.toml
index a482c39..16455cc 100644
--- a/in-progress/8757-fees/notebook/pyproject.toml
+++ b/in-progress/8757-fees/notebook/pyproject.toml
@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "eth-ape[recommended-plugins]",
     "ipykernel>=6.29.5",
+    "marimo>=0.13.2",
     "matplotlib>=3.9.2",
     "numpy>=1.26.4",
     "pydantic>=2.9.2",
diff --git a/in-progress/8757-fees/notebook/uv.lock b/in-progress/8757-fees/notebook/uv.lock
index 7574afc..f8e8185 100644
--- a/in-progress/8757-fees/notebook/uv.lock
+++ b/in-progress/8757-fees/notebook/uv.lock
@@ -85,6 +85,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "anyio"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
+]
+
 [[package]]
 name = "ape-alchemy"
 version = "0.8.2"
@@ -646,6 +660,15 @@ wheels = [
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