diff --git a/src/process_logs/README.md b/src/process_logs/README.md
new file mode 100644
index 0000000..10432ac
--- /dev/null
+++ b/src/process_logs/README.md
@@ -0,0 +1,5 @@
+# Scripts for understanding wpi logs
+
+src/wpilog2ndjson.py - convert the first argument file into a jsonl/ndjson file. Redirect output to a file to save it.
+src/initial_analysis - some helper functions
+initial_plots.py - a script that loads a hardcoded file (edit it) into a polars dataframe (table), does simple filtering, and shows a couple of plots. A starting point
diff --git a/src/process_logs/initial_plots.py b/src/process_logs/initial_plots.py
new file mode 100644
index 0000000..0e3a8b3
--- /dev/null
+++ b/src/process_logs/initial_plots.py
@@ -0,0 +1,142 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+# %load_ext autoreload
+
+# %%
+# %autoreload 2
+
+# %%
+from src.initial_analysis import *
+import polars as pl
+from polars import col as c
+
+# %%
+path = "second_log.jsonl"
+df = load_json_log(path)
+print(f"Loaded {df.height} rows, {df.width} columns from {path}\n")
+
+# %%
+df.sample(10)
+
+# %%
+diagnose_log(df)
+
+# %%
+records_per_entry = df.group_by("entry", "entryname", "entrytype").agg(
+        pl.len().alias("record_count")
+    ).sort("record_count")
+records_per_entry
+
+# %%
+[en for en in records_per_entry.get_column("entryname").to_list() if "Angle" in en]
+
+# %%
+df.filter(c("entryname").str.contains("/RealOutputs/Turret/Turret Pose")).group_by("entryname").agg(count=pl.len()).get_column("entryname").to_list()
+
+# %%
+turret_poses = df.filter(c("entryname").str.contains("/RealOutputs/Turret/Turret Pose"))
+turret_poses.sample(3)
+
+# %%
+df.filter(c("entryname").str.contains("/RealOutputs/Turret/Turret Pose")).filter(c("data_str").str.len_chars() != 32)
+
+# %%
+decode_struct_pose3d(df.filter(c("entryname").str.contains("/RealOutputs/Turret/Turret Pose")))
+
+# %%
+df.filter(c("entryname").str.contains("HoodAngle")).group_by("entryname").agg(count=pl.len())
+
+# %%
+robot_poses = df.filter(c("entryname").str.contains("RobotPoses"))
+robot_poses.group_by("entryname", "entrytype").agg(
+    count=pl.len(),
+    str_data_len_min=c("data_str").str.len_chars().min(),
+    str_data_len_max=c("data_str").str.len_chars().max(),
+)[0].to_dict()['entryname'][0]
+
+# %%
+robot_poses.filter(c("entryname") == "/RealOutputs/Vision/Camera0/RobotPoses")
+
+# %%
+rp = robot_poses.filter(c("data_str") != "").with_columns(
+    accepted=c("entryname").str.contains("RobotPosesAccepted")
+)
+
+# %%
+robot_poses.with_columns(c("entryname").str.split("/").list[-1]).sort("entry", "timestamp")#.filter(c("entry") == 208)
+
+# %%
+swerve_poses = decode_struct_pose2d(df).filter(c("entryname").str.contains("CommandSwerveDrive"))
+
+# %%
+swerve_poses = swerve_poses.with_columns(
+    x=c("pose2d").struct.field("x"),
+    y=c("pose2d").struct.field("y"),
+    rotation=c("pose2d").struct.field("rotation"),
+).with_columns(
+    dx=(c("x")-c("x").shift(1)),
+    dy=(c("y")-c("y").shift(1)),
+    dt=(c("timestamp")-c("timestamp").shift(1)),
+).with_columns(
+    dxdt=c("dx") / c("dt"),
+    dydt=c("dy") / c("dt"),
+).with_columns(
+    abs_dxdt=c("dxdt").abs(),
+    abs_dydt=c("dydt").abs(),
+    vel_mag=(c("dxdt")**2 + c("dydt")**2).sqrt(),
+)
+swerve_poses.sort("abs_dxdt").tail(30)
+
+# %%
+big_x_speeds = swerve_poses.sort("dxdt").filter([
+        c("timestamp") > 280,
+        c("timestamp") < 390,        
+])
+
+
+# %%
+big_x_speeds.get_column("dxdt").abs().quantile(1)
+
+# %%
+import altair as alt
+alt.data_transformers.enable("vegafusion")
+
+# %%
+swerve_poses.columns
+
+# %%
+alt.Chart(big_x_speeds.filter([
+    c("timestamp") > 350,
+    c("timestamp") < 360,
+])).mark_circle().encode(
+    x=alt.X("timestamp:Q").scale(zero=False, domainMin=350),
+    y="x:Q",
+    tooltip="dx:Q dt:Q dxdt:Q".split()
+).properties(width=800, height=600).interactive()
+
+
+# %%
+alt.Chart(big_x_speeds.filter([
+    c("timestamp") > 350,
+    c("timestamp") < 380,
+])).mark_circle().encode(
+    x=alt.X("timestamp:Q").scale(zero=False, domainMin=350),
+    y="vel_mag:Q",
+    tooltip="dx:Q dt:Q dxdt:Q vel_mag:Q".split()
+).properties(width=800, height=600).interactive()
+
+# %%
diff --git a/src/process_logs/pyproject.toml b/src/process_logs/pyproject.toml
new file mode 100644
index 0000000..c15eeff
--- /dev/null
+++ b/src/process_logs/pyproject.toml
@@ -0,0 +1,19 @@
+[project]
+name = "wpilib-log-analysis"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "altair>=6.0.0",
+    "msgpack>=1.1.2",
+    "polars>=1.38.1",
+    "vegafusion>=2.0.3",
+    "vl-convert-python>=1.9.0.post1",
+]
+
+[dependency-groups]
+dev = [
+    "jupyter>=1.1.1",
+    "jupytext>=1.19.1",
+]
diff --git a/src/process_logs/src/initial_analysis.py b/src/process_logs/src/initial_analysis.py
new file mode 100644
index 0000000..42b9c04
--- /dev/null
+++ b/src/process_logs/src/initial_analysis.py
@@ -0,0 +1,381 @@
+import polars as pl
+import sys
+
+# Map each entrytype to the column that should hold its data.
+# Exact matches are checked first; prefix matches (struct:*) are handled
+# separately in the analysis functions.
+TYPE_TO_COLUMN = {
+    "double": "data_double",
+    "float": "data_double",
+    "int64": "data_int",
+    "string": "data_str",
+    "json": "data_str",
+    "msgpack": "data_str",
+    "boolean": "data_bool",
+    "boolean[]": "data_str",
+    "double[]": "data_str",
+    "float[]": "data_str",
+    "int64[]": "data_str",
+    "string[]": "data_str",
+    "structschema": "data_str",
+    "raw": "data_str",
+}
+
+# Entrytype prefixes that map to data_str (binary data stored as base64)
+PREFIX_TO_COLUMN = {
+    "struct:": "data_str",
+}
+
+DATA_COLUMNS = ["data_double", "data_int", "data_str", "data_bool"]
+
+
+def _expected_column_expr() -> pl.Expr:
+    """Build a when/then expression mapping entrytype -> expected data column name."""
+    items = list(TYPE_TO_COLUMN.items())
+    expr = pl.when(pl.col("entrytype") == items[0][0]).then(pl.lit(items[0][1]))
+    for etype, col in items[1:]:
+        expr = expr.when(pl.col("entrytype") == etype).then(pl.lit(col))
+    for prefix, col in PREFIX_TO_COLUMN.items():
+        expr = expr.when(pl.col("entrytype").str.starts_with(prefix)).then(pl.lit(col))
+    return expr.otherwise(pl.lit(None))
+
+
+def _is_known_type_expr() -> pl.Expr:
+    """Expression that is true when entrytype is a known exact or prefix type."""
+    known_exact = set(TYPE_TO_COLUMN.keys())
+    expr = pl.col("entrytype").is_in(known_exact)
+    for prefix in PREFIX_TO_COLUMN:
+        expr = expr | pl.col("entrytype").str.starts_with(prefix)
+    return expr
+
+
+def add_expected_col_null(df: pl.DataFrame) -> pl.DataFrame:
+    """Add expected_col_null (bool) and expected_col_null_comment (str).
+
+    expected_col_null is true when the type-appropriate data column is null.
+    expected_col_null_comment explains known cases (e.g. sanitized non-finite
+    double/float); empty string means needs investigation.
+    """
+    # Build the "is null in expected column" expression per type
+    items = list(TYPE_TO_COLUMN.items())
+    is_null_expr = pl.when(pl.col("entrytype") == items[0][0]).then(
+        pl.col(items[0][1]).is_null()
+    )
+    for etype, col in items[1:]:
+        is_null_expr = is_null_expr.when(pl.col("entrytype") == etype).then(
+            pl.col(col).is_null()
+        )
+    for prefix, col in PREFIX_TO_COLUMN.items():
+        is_null_expr = is_null_expr.when(
+            pl.col("entrytype").str.starts_with(prefix)
+        ).then(pl.col(col).is_null())
+    # Rows with unknown entrytype: no expected column, flag as null
+    is_null_expr = is_null_expr.otherwise(pl.lit(True))
+
+    # Comment: explain known cases, leave empty for unknowns
+    sanitized_float_types = {"double", "float"}
+    has_unrepresentable = (
+        pl.col("unrepresentable").is_not_null()
+        if "unrepresentable" in df.columns
+        else pl.lit(False)
+    )
+    comment_expr = (
+        pl.when(pl.col("entrytype").is_in(sanitized_float_types) & has_unrepresentable)
+        .then(pl.lit("see unrepresentable column"))
+        .when(~_is_known_type_expr())
+        .then(pl.lit("unhandled entrytype"))
+        .otherwise(pl.lit(""))
+    )
+
+    return df.with_columns(
+        is_null_expr.alias("expected_col_null"),
+        pl.when(is_null_expr)
+        .then(comment_expr)
+        .otherwise(pl.lit(None))
+        .alias("expected_col_null_comment"),
+    )
+
+
+def add_wrong_col_populated(df: pl.DataFrame) -> pl.DataFrame:
+    """Add wrong_col_populated (bool) and wrong_col_populated_fields (str).
+
+    wrong_col_populated is true when a data column that should be null for the
+    row's entrytype is non-null. wrong_col_populated_fields lists those column
+    names, comma-separated.
+    """
+    # For each data column, build an expression: "this column is expected for this row"
+    # A column is expected if any exact type or prefix type maps to it.
+    wrong_per_col = {}
+    for col in DATA_COLUMNS:
+        expected_exact = [et for et, c in TYPE_TO_COLUMN.items() if c == col]
+        expected_prefixes = [p for p, c in PREFIX_TO_COLUMN.items() if c == col]
+        is_expected = pl.col("entrytype").is_in(expected_exact)
+        for prefix in expected_prefixes:
+            is_expected = is_expected | pl.col("entrytype").str.starts_with(prefix)
+        wrong_per_col[col] = ~is_expected & pl.col(col).is_not_null()
+
+    any_wrong = wrong_per_col[DATA_COLUMNS[0]]
+    for col in DATA_COLUMNS[1:]:
+        any_wrong = any_wrong | wrong_per_col[col]
+
+    # Build the fields string: comma-separated names of wrongly populated columns
+    fields_parts = [
+        pl.when(wrong_per_col[col]).then(pl.lit(col)).otherwise(pl.lit(None))
+        for col in DATA_COLUMNS
+    ]
+    fields_expr = pl.concat_str(fields_parts, separator=",", ignore_nulls=True)
+
+    return df.with_columns(
+        any_wrong.alias("wrong_col_populated"),
+        pl.when(any_wrong)
+        .then(fields_expr)
+        .otherwise(pl.lit(None))
+        .alias("wrong_col_populated_fields"),
+    )
+
+
+def add_duplicate_timestamp(df: pl.DataFrame) -> pl.DataFrame:
+    """Add duplicate_timestamp (bool).
+
+    True when this (entry, timestamp) pair appears more than once.
+    """
+    counts = df.group_by("entry", "timestamp").agg(pl.len().alias("_dup_count"))
+    return (
+        df.join(counts, on=["entry", "timestamp"], how="left")
+        .with_columns((pl.col("_dup_count") > 1).alias("duplicate_timestamp"))
+        .drop("_dup_count")
+    )
+
+
+def add_timestamp_decreased(df: pl.DataFrame) -> pl.DataFrame:
+    """Add timestamp_decreased (bool) and timestamp_prev (f64).
+
+    Computed over rows ordered by their original position within each entry.
+    timestamp_decreased is true when this row's timestamp is less than the
+    previous row's timestamp for the same entry. timestamp_prev is that
+    previous timestamp (null for first row of each entry).
+    """
+    # Add a row index to preserve original order, compute over that
+    return (
+        df.with_row_index("_row_idx")
+        .sort("entry", "_row_idx")
+        .with_columns(
+            pl.col("timestamp").shift(1).over("entry").alias("timestamp_prev"),
+        )
+        .with_columns(
+            (pl.col("timestamp") < pl.col("timestamp_prev")).alias(
+                "timestamp_decreased"
+            ),
+        )
+        .sort("_row_idx")
+        .drop("_row_idx")
+    )
+
+
+UNREPRESENTABLE_MAP = {"inf": float("inf"), "-inf": float("-inf"), "nan": float("nan")}
+
+
+def restore_unrepresentable_floats(df: pl.DataFrame) -> pl.DataFrame:
+    """Parse the unrepresentable column back into data_double where possible.
+
+    Known values (inf, -inf, nan) are restored into data_double and the
+    unrepresentable column is cleared for those rows.
+    """
+    if "unrepresentable" not in df.columns:
+        return df
+
+    parseable = pl.col("unrepresentable").is_in(list(UNREPRESENTABLE_MAP.keys()))
+
+    restored = (
+        pl.when(pl.col("unrepresentable") == "inf")
+        .then(pl.lit(float("inf")))
+        .when(pl.col("unrepresentable") == "-inf")
+        .then(pl.lit(float("-inf")))
+        .when(pl.col("unrepresentable") == "nan")
+        .then(pl.lit(float("nan")))
+        .otherwise(pl.lit(None))
+    )
+
+    return df.with_columns(
+        pl.when(parseable)
+        .then(restored)
+        .otherwise(pl.col("data_double"))
+        .alias("data_double"),
+        pl.when(parseable)
+        .then(pl.lit(None).cast(pl.Utf8))
+        .otherwise(pl.col("unrepresentable"))
+        .alias("unrepresentable"),
+    )
+
+
+def decode_struct_pose2d(df: pl.DataFrame) -> pl.DataFrame:
+    """Decode struct:Pose2d rows from base64 data_str into a Polars struct column.
+
+    Adds a 'pose2d' column of type Struct{translation.x, translation.y,
+    rotation.value} (all f64).  Non-Pose2d rows get null.
+
+    The base64 payload is 24 bytes: three little-endian doubles
+    (Translation2d.x, Translation2d.y, Rotation2d.value).
+
+    Uses native Polars expressions (str.decode → bin.slice → bin.reinterpret)
+    so the entire decode runs in the Rust engine with no Python-level loop.
+    """
+    is_pose2d = pl.col("entrytype") == "struct:Pose2d"
+    payload = pl.col("data_str").str.decode("base64", strict=False)
+    pose2d_dtype = pl.Struct(
+        {
+            "translation.x": pl.Float64,
+            "translation.y": pl.Float64,
+            "rotation.value": pl.Float64,
+        }
+    )
+
+    return df.with_columns(
+        pl.when(is_pose2d)
+        .then(
+            pl.struct(
+                payload.bin.slice(0, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("translation.x"),
+                payload.bin.slice(8, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("translation.y"),
+                payload.bin.slice(16, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("rotation.value"),
+            )
+        )
+        .otherwise(pl.lit(None, dtype=pose2d_dtype))
+        .alias("pose2d")
+    )
+
+
+def decode_struct_pose3d(df: pl.DataFrame) -> pl.DataFrame:
+    """Decode struct:Pose3d rows from base64 data_str into a Polars struct column.
+
+    Adds a 'pose3d' column with all 7 leaf fields:
+      translation.x/y/z (f64) and rotation.q.w/x/y/z (f64).
+    Non-Pose3d rows get null.
+
+    The base64 payload is 56 bytes: seven little-endian doubles
+    (Translation3d x/y/z, then Quaternion w/x/y/z).
+    """
+    is_pose3d = pl.col("entrytype") == "struct:Pose3d"
+    payload = pl.col("data_str").str.decode("base64", strict=False)
+    pose3d_dtype = pl.Struct(
+        {
+            "translation.x": pl.Float64,
+            "translation.y": pl.Float64,
+            "translation.z": pl.Float64,
+            "rotation.q.w": pl.Float64,
+            "rotation.q.x": pl.Float64,
+            "rotation.q.y": pl.Float64,
+            "rotation.q.z": pl.Float64,
+        }
+    )
+
+    return df.with_columns(
+        pl.when(is_pose3d)
+        .then(
+            pl.struct(
+                payload.bin.slice(0, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("translation.x"),
+                payload.bin.slice(8, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("translation.y"),
+                payload.bin.slice(16, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("translation.z"),
+                payload.bin.slice(24, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("rotation.q.w"),
+                payload.bin.slice(32, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("rotation.q.x"),
+                payload.bin.slice(40, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("rotation.q.y"),
+                payload.bin.slice(48, 8)
+                .bin.reinterpret(dtype=pl.Float64)
+                .alias("rotation.q.z"),
+            )
+        )
+        .otherwise(pl.lit(None, dtype=pose3d_dtype))
+        .alias("pose3d")
+    )
+
+
+def load_json_log(path):
+    """Scan all rows for schema inference so rare columns like
+    "unrepresentable" are not missed."""
+
+    df = pl.read_ndjson(path, infer_schema_length=None)
+    df = restore_unrepresentable_floats(df)
+    return df
+
+
+def diagnose_log(df, fail_fast=True):
+    """Print summary of findings per analysis column, and if fail_fast then quit if any issues."""
+    df = add_expected_col_null(df)
+    df = add_wrong_col_populated(df)
+    df = add_duplicate_timestamp(df)
+    df = add_timestamp_decreased(df)
+    fail = False
+    for col, comment_col in [
+        ("expected_col_null", "expected_col_null_comment"),
+        ("wrong_col_populated", "wrong_col_populated_fields"),
+        ("duplicate_timestamp", None),
+        ("timestamp_decreased", "timestamp_prev"),
+    ]:
+        flagged = df.filter(pl.col(col))
+        print(f"{col}: {flagged.height} rows flagged")
+        if not flagged.is_empty() and comment_col:
+            print(
+                flagged.group_by(comment_col)
+                .agg(pl.len().alias("count"))
+                .sort("count", descending=True)
+            )
+            fail = True
+    if fail:
+        raise (ValueError("Printed findings above, and fail_fast=True"))
+
+
+def main():
+    path = sys.argv[1]
+    df = load_json_log(path)
+    print(f"Loaded {df.height} rows, {df.width} columns from {path}\n")
+
+    diagnose_log(df)
+
+    # --- Summaries ---
+    print("\n" + "=" * 60)
+    print("Timeseries summary")
+    print("=" * 60)
+    records_per_entry = df.group_by("entry", "entryname", "entrytype").agg(
+        pl.len().alias("record_count")
+    )
+    print(f"Distinct timeseries (by entry): {records_per_entry.height}\n")
+    print("Records per timeseries distribution:")
+    print(records_per_entry.select("record_count").describe())
+
+    print("\n" + "=" * 60)
+    print("Struct schemas")
+    print("=" * 60)
+    schemas = (
+        df.filter(pl.col("entrytype") == "structschema")
+        .select("entryname", "data_str")
+        .unique()
+    )
+    print(f"Distinct struct schemas: {schemas.height}\n")
+    if schemas.height <= 20:
+        with pl.Config(fmt_str_lengths=200, tbl_width_chars=200):
+            print(schemas)
+    else:
+        with pl.Config(fmt_str_lengths=200, tbl_width_chars=200):
+            print(schemas.head(20))
+        print(f"... and {schemas.height - 20} more")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/process_logs/src/wpilog2ndjson.py b/src/process_logs/src/wpilog2ndjson.py
new file mode 100755
index 0000000..7f654b3
--- /dev/null
+++ b/src/process_logs/src/wpilog2ndjson.py
@@ -0,0 +1,378 @@
+#! /usr/bin/env python3
+# Copyright (c) FIRST and other WPILib contributors.
+# Open Source Software; you can modify and/or share it under the terms of
+# the WPILib BSD license file in the root directory of this project.
+
+import array
+import base64
+import math
+import struct
+from typing import List, SupportsBytes
+import json
+
+import msgpack
+
+__all__ = ["StartRecordData", "MetadataRecordData", "DataLogRecord", "DataLogReader"]
+
+floatStruct = struct.Struct("<f")
+doubleStruct = struct.Struct("<d")
+
+kControlStart = 0
+kControlFinish = 1
+kControlSetMetadata = 2
+
+
+class StartRecordData:
+    """Data contained in a start control record as created by DataLog.start() when
+    writing the log. This can be read by calling DataLogRecord.getStartData().
+
+    entry: Entry ID; this will be used for this entry in future records.
+    name: Entry name.
+    type: Type of the stored data for this entry, as a string, e.g. "double".
+    metadata: Initial metadata.
+    """
+
+    def __init__(self, entry: int, name: str, type: str, metadata: str):
+        self.entry = entry
+        self.name = name
+        self.type = type
+        self.metadata = metadata
+
+
+class MetadataRecordData:
+    """Data contained in a set metadata control record as created by
+    DataLog.setMetadata(). This can be read by calling
+    DataLogRecord.getSetMetadataData().
+
+    entry: Entry ID.
+    metadata: New metadata for the entry.
+    """
+
+    def __init__(self, entry: int, metadata: str):
+        self.entry = entry
+        self.metadata = metadata
+
+
+class DataLogRecord:
+    """A record in the data log. May represent either a control record
+    (entry == 0) or a data record."""
+
+    def __init__(self, entry: int, timestamp: int, data: SupportsBytes):
+        self.entry = entry
+        self.timestamp = timestamp
+        self.data = data
+
+    def isControl(self) -> bool:
+        return self.entry == 0
+
+    def _getControlType(self) -> int:
+        return self.data[0]
+
+    def isStart(self) -> bool:
+        return (
+            self.entry == 0
+            and len(self.data) >= 17
+            and self._getControlType() == kControlStart
+        )
+
+    def isFinish(self) -> bool:
+        return (
+            self.entry == 0
+            and len(self.data) == 5
+            and self._getControlType() == kControlFinish
+        )
+
+    def isSetMetadata(self) -> bool:
+        return (
+            self.entry == 0
+            and len(self.data) >= 9
+            and self._getControlType() == kControlSetMetadata
+        )
+
+    def getStartData(self) -> StartRecordData:
+        if not self.isStart():
+            raise TypeError("not a start record")
+        entry = int.from_bytes(self.data[1:5], byteorder="little", signed=False)
+        name, pos = self._readInnerString(5)
+        type, pos = self._readInnerString(pos)
+        metadata = self._readInnerString(pos)[0]
+        return StartRecordData(entry, name, type, metadata)
+
+    def getFinishEntry(self) -> int:
+        if not self.isFinish():
+            raise TypeError("not a finish record")
+        return int.from_bytes(self.data[1:5], byteorder="little", signed=False)
+
+    def getSetMetadataData(self) -> MetadataRecordData:
+        if not self.isSetMetadata():
+            raise TypeError("not a finish record")
+        entry = int.from_bytes(self.data[1:5], byteorder="little", signed=False)
+        metadata = self._readInnerString(5)[0]
+        return MetadataRecordData(entry, metadata)
+
+    def getBoolean(self) -> bool:
+        if len(self.data) != 1:
+            raise TypeError("not a boolean")
+        return self.data[0] != 0
+
+    def getInteger(self) -> int:
+        if len(self.data) != 8:
+            raise TypeError("not an integer")
+        return int.from_bytes(self.data, byteorder="little", signed=True)
+
+    def getFloat(self) -> float:
+        if len(self.data) != 4:
+            raise TypeError("not a float")
+        return floatStruct.unpack(self.data)[0]
+
+    def getDouble(self) -> float:
+        if len(self.data) != 8:
+            raise TypeError("not a double")
+        return doubleStruct.unpack(self.data)[0]
+
+    def getString(self) -> str:
+        return str(self.data, encoding="utf-8")
+
+    def getMsgPack(self):
+        return msgpack.unpackb(self.data)
+
+    def getBooleanArray(self) -> List[bool]:
+        return [x != 0 for x in self.data]
+
+    def getIntegerArray(self) -> array.array:
+        if (len(self.data) % 8) != 0:
+            raise TypeError("not an integer array")
+        arr = array.array("l")
+        arr.frombytes(self.data)
+        return arr
+
+    def getFloatArray(self) -> array.array:
+        if (len(self.data) % 4) != 0:
+            raise TypeError("not a float array")
+        arr = array.array("f")
+        arr.frombytes(self.data)
+        return arr
+
+    def getDoubleArray(self) -> array.array:
+        if (len(self.data) % 8) != 0:
+            raise TypeError("not a double array")
+        arr = array.array("d")
+        arr.frombytes(self.data)
+        return arr
+
+    def getStringArray(self) -> List[str]:
+        size = int.from_bytes(self.data[:4], byteorder="little", signed=False)
+        if size > ((len(self.data) - 4) / 4):
+            raise TypeError("not a string array")
+        arr = []
+        pos = 4
+        for _ in range(size):
+            val, pos = self._readInnerString(pos)
+            arr.append(val)
+        return arr
+
+    def _readInnerString(self, pos: int) -> tuple[str, int]:
+        size = int.from_bytes(
+            self.data[pos : pos + 4], byteorder="little", signed=False
+        )
+        end = pos + 4 + size
+        if end > len(self.data):
+            raise TypeError("invalid string size")
+        return str(self.data[pos + 4 : end], encoding="utf-8"), end
+
+
+class DataLogIterator:
+    """DataLogReader iterator."""
+
+    def __init__(self, buf: SupportsBytes, pos: int):
+        self.buf = buf
+        self.pos = pos
+
+    def __iter__(self):
+        return self
+
+    def _readVarInt(self, pos: int, len: int) -> int:
+        val = 0
+        for i in range(len):
+            val |= self.buf[pos + i] << (i * 8)
+        return val
+
+    def __next__(self) -> DataLogRecord:
+        if len(self.buf) < (self.pos + 4):
+            raise StopIteration
+        entryLen = (self.buf[self.pos] & 0x3) + 1
+        sizeLen = ((self.buf[self.pos] >> 2) & 0x3) + 1
+        timestampLen = ((self.buf[self.pos] >> 4) & 0x7) + 1
+        headerLen = 1 + entryLen + sizeLen + timestampLen
+        if len(self.buf) < (self.pos + headerLen):
+            raise StopIteration
+        entry = self._readVarInt(self.pos + 1, entryLen)
+        size = self._readVarInt(self.pos + 1 + entryLen, sizeLen)
+        timestamp = self._readVarInt(self.pos + 1 + entryLen + sizeLen, timestampLen)
+        if len(self.buf) < (self.pos + headerLen + size):
+            raise StopIteration
+        record = DataLogRecord(
+            entry,
+            timestamp,
+            self.buf[self.pos + headerLen : self.pos + headerLen + size],
+        )
+        self.pos += headerLen + size
+        return record
+
+
+class DataLogReader:
+    """Data log reader (reads logs written by the DataLog class)."""
+
+    def __init__(self, buf: SupportsBytes):
+        self.buf = buf
+
+    def __bool__(self):
+        return self.isValid()
+
+    def isValid(self) -> bool:
+        """Returns true if the data log is valid (e.g. has a valid header)."""
+        return (
+            len(self.buf) >= 12
+            and self.buf[:6] == b"WPILOG"
+            and self.getVersion() >= 0x0100
+        )
+
+    def getVersion(self) -> int:
+        """Gets the data log version. Returns 0 if data log is invalid.
+
+        @return Version number; most significant byte is major, least significant is
+            minor (so version 1.0 will be 0x0100)"""
+        if len(self.buf) < 12:
+            return 0
+        return int.from_bytes(self.buf[6:8], byteorder="little", signed=False)
+
+    def getExtraHeader(self) -> str:
+        """Gets the extra header data.
+
+        @return Extra header data
+        """
+        if len(self.buf) < 12:
+            return ""
+        size = int.from_bytes(self.buf[8:12], byteorder="little", signed=False)
+        return str(self.buf[12 : 12 + size], encoding="utf-8")
+
+    def __iter__(self) -> DataLogIterator:
+        extraHeaderSize = int.from_bytes(
+            self.buf[8:12], byteorder="little", signed=False
+        )
+        return DataLogIterator(self.buf, 12 + extraHeaderSize)
+
+
+if __name__ == "__main__":
+    import mmap
+    import sys
+    from datetime import datetime
+
+    if len(sys.argv) != 2:
+        print("Usage: datalog.py <file>", file=sys.stderr)
+        sys.exit(1)
+
+    with open(sys.argv[1], "r") as f:
+        mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
+        reader = DataLogReader(mm)
+        if not reader:
+            print("not a log file", file=sys.stderr)
+            sys.exit(1)
+
+        entries = {}
+        for record in reader:
+            timestamp = record.timestamp / 1000000
+            if record.isStart():
+                try:
+                    data = record.getStartData()
+                    # print(
+                    #    f"Start({data.entry}, name='{data.name}', type='{data.type}', metadata='{data.metadata}') [{timestamp}]"
+                    # )
+                    if data.entry in entries:
+                        print("...DUPLICATE entry ID, overriding")
+                    entries[data.entry] = data
+                except TypeError:
+                    print("Start(INVALID)")
+            elif record.isFinish():
+                try:
+                    entry = record.getFinishEntry()
+                    # print(f"Finish({entry}) [{timestamp}]")
+                    if entry not in entries:
+                        print("...ID not found")
+                    else:
+                        del entries[entry]
+                except TypeError:
+                    print("Finish(INVALID)")
+            elif record.isSetMetadata():
+                try:
+                    data = record.getSetMetadataData()
+                    print(f"SetMetadata({data.entry}, '{data.metadata}') [{timestamp}]")
+                    if data.entry not in entries:
+                        print("...ID not found")
+                except TypeError:
+                    print("SetMetadata(INVALID)")
+            elif record.isControl():
+                print("Unrecognized control record")
+            else:
+                # print(f"{record.entry},{len(record.data)}", end="")
+                entry = entries.get(record.entry)
+                if entry is None:
+                    print("<ID not found>")
+                    continue
+                # print(f"{entry.name},{entry.type},{timestamp},", end="")
+                output = dict(
+                    entry=record.entry,
+                    len=len(record.data),
+                    entryname=entry.name,
+                    entrytype=entry.type,
+                    timestamp=timestamp,
+                )
+
+                try:
+                    # handle systemTime specially
+                    if entry.name == "systemTime" and entry.type == "int64":
+                        dt = datetime.fromtimestamp(record.getInteger() / 1000000)
+                        date_string = "{:%Y-%m-%d %H:%M:%S.%f}".format(dt)
+                        output["systemtime"] = date_string
+                        continue
+
+                    if entry.type == "double":
+                        v = record.getDouble()
+                        if math.isfinite(v):
+                            output["data_double"] = v
+                        else:
+                            output["unrepresentable"] = repr(v)
+                    elif entry.type == "int64":
+                        output["data_int"] = record.getInteger()
+                    elif entry.type in ("string", "json"):
+                        output["data_str"] = record.getString()
+                    elif entry.type == "msgpack":
+                        output["data_str"] = json.dumps(record.getMsgPack())
+                    elif entry.type == "boolean":
+                        output["data_bool"] = record.getBoolean()
+                    elif entry.type == "boolean[]":
+                        output["data_str"] = json.dumps(list(record.getBooleanArray()))
+                    elif entry.type == "double[]":
+                        output["data_str"] = json.dumps(list(record.getDoubleArray()))
+                    elif entry.type == "float[]":
+                        output["data_str"] = json.dumps(list(record.getFloatArray()))
+                    elif entry.type == "int64[]":
+                        output["data_str"] = json.dumps(list(record.getIntegerArray()))
+                    elif entry.type == "string[]":
+                        output["data_str"] = json.dumps(list(record.getStringArray()))
+                    elif entry.type == "float":
+                        v = record.getFloat()
+                        if math.isfinite(v):
+                            output["data_double"] = v
+                        else:
+                            output["unrepresentable"] = repr(v)
+                    elif entry.type == "structschema":
+                        output["data_str"] = record.getString()
+                    elif entry.type.startswith("struct:") or entry.type == "raw":
+                        output["data_str"] = base64.b64encode(
+                            bytes(record.data)
+                        ).decode("ascii")
+                    print(json.dumps(output))
+                except TypeError as e:
+                    raise e
+                    print(f"invalid: {e}")
diff --git a/src/process_logs/uv.lock b/src/process_logs/uv.lock
new file mode 100644
index 0000000..a7720ac
--- /dev/null
+++ b/src/process_logs/uv.lock
@@ -0,0 +1,1592 @@
+version = 1
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+source = { registry = "https://pypi.org/simple" }
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+    { name = "narwhals" },
+    { name = "packaging" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f7/c0/184a89bd5feba14ff3c41cfaf1dd8a82c05f5ceedbc92145e17042eb08a4/altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4", size = 763834, upload-time = "2025-11-12T08:59:11.519Z" }
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