diff --git a/experiments/cf_nolearn.py b/experiments/cf_nolearn.py
index 9fb3792a..0928a568 100644
--- a/experiments/cf_nolearn.py
+++ b/experiments/cf_nolearn.py
@@ -33,11 +33,7 @@
     SavedProfile,
     is_cuda_ready,
 )
-from emevo.rl.ppo_normal import (
-    NormalPPONet,
-    vmap_apply,
-    vmap_net,
-)
+from emevo.rl.ppo_normal import NormalPPONet, vmap_apply, vmap_net
 from emevo.spaces import BoxSpace
 
 PROJECT_ROOT = Path(__file__).parent.parent
diff --git a/experiments/cf_simple.py b/experiments/cf_simple.py
index 4ccb1802..dd9efe40 100644
--- a/experiments/cf_simple.py
+++ b/experiments/cf_simple.py
@@ -1,4 +1,5 @@
 """Asexual reward evolution with Circle Foraging"""
+
 import dataclasses
 import json
 from pathlib import Path
@@ -56,11 +57,11 @@ class RewardExtractor:
     _max_norm: jax.Array = dataclasses.field(init=False)
 
     def __post_init__(self) -> None:
-        self._max_norm = jnp.sqrt(jnp.sum(self.act_space.high ** 2, axis=-1))
+        self._max_norm = jnp.sqrt(jnp.sum(self.act_space.high**2, axis=-1))
 
     def normalize_action(self, action: jax.Array) -> jax.Array:
         scaled = self.act_space.sigmoid_scale(action)
-        norm = jnp.sqrt(jnp.sum(scaled ** 2, axis=-1, keepdims=True))
+        norm = jnp.sqrt(jnp.sum(scaled**2, axis=-1, keepdims=True))
         return norm / self._max_norm
 
     def extract(
diff --git a/noxfile.py b/noxfile.py
index 071392ef..9518f9f6 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -6,7 +6,7 @@
 
 import nox
 
-SOURCES = ["src/emevo", "tests", "smoke-tests", "experiments"]
+SOURCES = ["src/emevo", "tests", "smoke-tests", "experiments", "scripts"]
 
 
 def _sync(session: nox.Session, requirements: str) -> None:
diff --git a/pyproject.toml b/pyproject.toml
index 086a4c19..915d3905 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -73,5 +73,6 @@ select = ["E", "F", "B", "UP"]
 "__init__.py" = ["F401"]
 "src/emevo/reward_fn.py" = ["B023"]
 # For typer
-"experiments/**/*.py" = ["B008", "UP006", "UP007"]
-"smoke-tests/*.py" = ["B008", "UP006", "UP007"]
\ No newline at end of file
+"experiments/*.py" = ["B008", "UP006", "UP007"]
+"smoke-tests/*.py" = ["B008", "UP006", "UP007"]
+"scripts/*.py" = ["UP006", "UP035"]
\ No newline at end of file
diff --git a/scripts/make_web_data.py b/scripts/make_web_data.py
new file mode 100644
index 00000000..debdee69
--- /dev/null
+++ b/scripts/make_web_data.py
@@ -0,0 +1,141 @@
+"""Asexual reward evolution with Circle Foraging"""
+
+import warnings
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+import polars as pl
+import typer
+
+from emevo.analysis.log_plotting import load_log
+
+PROJECT_ROOT = Path(__file__).parent.parent
+
+
+def _make_stats_df(profile_and_rewards_path: Path) -> tuple[pl.DataFrame, pl.DataFrame]:
+    rdf = pl.read_parquet(profile_and_rewards_path)
+    ldf = load_log(profile_and_rewards_path.parent).cast({"unique_id": pl.Int64})
+    nc_df = rdf.group_by("parent").agg(n_children=pl.col("unique_id").len())
+    age_df = (
+        ldf.group_by("unique_id").agg(lifetime=pl.col("unique_id").count()).collect()
+    )
+    food_df = ldf.group_by("unique_id").agg(eaten=pl.col("n_got_food").sum()).collect()
+    df = (
+        rdf.join(
+            nc_df, left_on="unique_id", right_on="parent", how="left", coalesce=True
+        )
+        .with_columns(pl.col("n_children").replace(None, 0))
+        .join(age_df, left_on="unique_id", right_on="unique_id")
+        .join(food_df, left_on="unique_id", right_on="unique_id")
+    )
+    return df, ldf
+
+
+def _agg_df(
+    path: Path,
+    start: int,
+    length: int,
+    ldf: pl.DataFrame,
+    ldf_offset: int,
+) -> tuple[pl.DataFrame, pl.DataFrame]:
+    npzfile = np.load(path)
+    caxy = npzfile["circle_axy"][start : start + length]  # (length, 200, 3)
+    cact = npzfile["circle_is_active"][start : start + length]  # (length, 200)
+    saxy = npzfile["static_circle_axy"][start : start + length]
+    sact = npzfile["static_circle_is_active"][start : start + length]
+    cx_list, cy_list, ca_list = [], [], []
+    sx_list, sy_list = [], []
+    uniqueid_list, c_nsteps_list, s_nsteps_list = [], [], []
+    for i in range(length):
+        active_slots = np.nonzero(cact[i])
+        caxy_i = caxy[i][active_slots]
+        saxy_i = saxy[i][sact[i]]
+
+        sx_list.append(saxy_i[:, 1])
+        sy_list.append(saxy_i[:, 2])
+
+        ca_list.append(caxy_i[:, 0])
+        cx_list.append(caxy_i[:, 1])
+        cy_list.append(caxy_i[:, 2])
+        df = ldf.filter(pl.col("step") == ldf_offset + start + i).sort("slots")
+        if len(df) != len(caxy_i):
+            warnings.warn(
+                "Number of active agents doesn't match"
+                + f"State: {len(saxy_i)} Log: {len(df)}"
+                + f"at step {ldf_offset + start + i}",
+                stacklevel=1,
+            )
+            df = df.unique(subset="unique_id", keep="first")
+            df = df.filter(((pl.col("unique_id") == 0) & (pl.col("slots") != 0)).not_())
+        uniqueid_list.append(df["unique_id"])
+        # Num. steps
+        c_nsteps_list.append(df["step"])
+        s_nsteps_list.append([ldf_offset + start + i] * len(saxy_i))
+
+    cx = np.concatenate(cx_list)
+    cy = np.concatenate(cy_list)
+    ca = np.concatenate(ca_list)
+    unique_id = pl.concat(uniqueid_list)
+    c_nsteps = pl.concat(c_nsteps_list)
+
+    sx = np.concatenate(sx_list)
+    sy = np.concatenate(sy_list)
+    s_nsteps = np.concatenate(s_nsteps_list)
+    cxy_df = pl.DataFrame(
+        {
+            "angle": ca,
+            "x": cx,
+            "y": cy,
+            "unique_id": unique_id,
+            "nsteps": c_nsteps,
+        }
+    )
+    sxy_df = pl.DataFrame(
+        {
+            "x": sx,
+            "y": sy,
+            "nsteps": s_nsteps,
+        }
+    )
+    return cxy_df, sxy_df
+
+
+def main(
+    profile_and_rewards_path: Path,
+    starting_points: List[int],
+    write_dir: Optional[Path] = None,
+    length: int = 100,
+) -> None:
+    if write_dir is None:
+        write_dir = Path("saved-web-data")
+
+    stats_df, ldf = _make_stats_df(profile_and_rewards_path)
+    stats_df.write_parquet(write_dir / "stats.parqut", compression="snappy")
+
+    log_path = profile_and_rewards_path.parent.expanduser()
+
+    for point in starting_points:
+        index = point // 1024000
+        ldfi = ldf.filter(
+            (pl.col("step") >= point) & (pl.col("step") < point + length)
+        ).collect()  # Offloading here for speedup
+        cxy_df, sxy_df = _agg_df(
+            log_path / f"state-{index + 1}.npz",
+            point - index * 1024000,
+            length,
+            ldfi,
+            index * 1024000,
+        )
+        cxy_df.write_parquet(
+            write_dir / f"saved_cpos-{point}.parqut",
+            compression="snappy",
+        )
+        sxy_df.write_parquet(
+            write_dir / f"saved_spos-{point}.parqut",
+            compression="snappy",
+        )
+
+
+if __name__ == "__main__":
+    typer.run(main)
diff --git a/src/emevo/exp_utils.py b/src/emevo/exp_utils.py
index 67cd0816..148f3a5b 100644
--- a/src/emevo/exp_utils.py
+++ b/src/emevo/exp_utils.py
@@ -134,7 +134,7 @@ class GopsConfig:
     path: str
     init_std: float
     init_mean: float
-    params: dict[str, float | dict[str, float]]
+    params: dict[str, float | dict[str, Any]]
     init_kwargs: dict[str, float] = dataclasses.field(default_factory=dict)
 
     def load_model(self) -> gops.Mutation | gops.Crossover: