diff --git a/notebooks/coverage_eda_lb_20251017.py b/notebooks/coverage_eda_lb_20251017.py
new file mode 100644
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+++ b/notebooks/coverage_eda_lb_20251017.py
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+import marimo
+
+__generated_with = "0.16.4"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import os
+    import json
+    import statistics
+    import marimo as mo
+    import pandas as pd
+    import plotly.express as px
+    return json, mo, os, pd, statistics
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+    # EDA of LB Assay Coverage
+
+    Data was generated by `get_panel_coverage.py` that uses the LB panels LB covered bed (with flankning sequence added, i.e. the variant calling bed) with the deduplicated bam/cram.
+
+    The output json from this analysis for each sample has the following form🥇
+
+    ```json
+    {
+        "chr<#>_<start>_<stop>": [
+            "depth_pos1", 
+            "depth_pos2", 
+            "depth_pos3",
+            ...
+            "depth_posN"
+        ]
+    }
+    ```
+
+    Analysis Plan:
+    - Poorly covered regions (i.e. bad probes/challenging areas to align)
+    - Overly covered regions (i.e. potential areas that are challenging for aligners or have low complexity)
+    - Comparison of Serenomica and Quantgene in overlap areas
+    """
+    )
+    return
+
+
+@app.cell
+def _():
+    # data
+    se_path = "../data/coverage_serenomica_QG/se1_se2/"
+    qg_path = "../data/coverage_serenomica_QG/QG_historical/"
+    return qg_path, se_path
+
+
+@app.cell
+def _(json, os):
+    def parse_data(dpath):
+        files = [f for f in os.listdir(dpath) if f.endswith(".json")]
+        parsed_data, mean_data = {}
+        for file in files:
+            name = file.split("_cov")[0]
+            with open(os.path.join(dpath, file), "r") as handle:
+                parsed_data[name] = json.load(handle)
+        return parsed_data
+    return (parse_data,)
+
+
+@app.cell
+def _(parse_data, qg_path, se_path):
+    se_data = parse_data(se_path)
+    qg_data = parse_data(qg_path)
+    return qg_data, se_data
+
+
+@app.cell
+def _(statistics):
+    def summerize_list(lst):
+        vals = list(map(int, lst))
+        return (
+            statistics.mean(vals),
+            statistics.median(vals),
+            statistics.stdev(vals),
+            min(vals),
+            max(vals),
+            sum(vals),
+            len(vals),
+        )
+
+
+    def get_summary_data(sample_data):
+        out_data = {}
+        sample_sum, sample_pos_count = 0, 0
+        for region, depths in sample_data.items():
+            (mean, median, std, min_val, max_val, sum_vals, count) = (
+                summerize_list(depths)
+            )
+            out_data[region] = {
+                "min": min_val,
+                "max": max_val,
+                "mean": mean,
+                "median": median,
+                "std": std,
+            }
+            sample_sum += sum_vals
+            sample_pos_count += count
+        out_data["overall"] = {"mean": sample_sum / sample_pos_count}
+        return out_data
+    return (get_summary_data,)
+
+
+@app.cell
+def _(get_summary_data, qg_data, se_data):
+    se_summary_data = {}
+    for se_sample, se_sample_data in se_data.items():
+        se_summary_data[se_sample] = get_summary_data(se_sample_data)
+
+    qg_summary_data = {}
+    for qg_sample, qg_sample_data in qg_data.items():
+        qg_summary_data[qg_sample] = get_summary_data(qg_sample_data)
+    return (se_summary_data,)
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell
+def _(pd):
+    def extract_stat_as_df(data_dict, access_key):
+        tmp_dict = {}
+        for name, sample_data in data_dict.items():
+            tmp_dict[name] = {
+                region: data[access_key]
+                for region, data in sample_data.items()
+                if region != "overall"
+            }
+        return pd.DataFrame.from_dict(tmp_dict, orient="index")
+    return (extract_stat_as_df,)
+
+
+@app.cell
+def _(extract_stat_as_df, se_summary_data):
+    extract_stat_as_df(se_summary_data, access_key="std")
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()