diff --git a/aimodel/scripts/crossval-stbl.py b/aimodel/scripts/crossval-stbl.py
new file mode 100755
index 0000000..672bffc
--- /dev/null
+++ b/aimodel/scripts/crossval-stbl.py
@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+
+from loguru import logger
+import pandas as pd
+import scipy
+from matplotlib import pyplot as plt
+
+# This script analyses metrics.tsv files from a series of identical experiments and reports metrics on them.
+# This is sometimes known as cross-validation, but we usually use the model series code crossval-stblX, where X is an integer >0.
+
+
+if len(sys.argv) <= 1:
+	print("""
+Usage:
+	scripts/stbl-crossval.mjs {{path/to/directory}}
+
+...in which the given directory contains a series of experiment root directories to include in the statistical analysis.
+
+This script is not picky about the format of the data in metrics.tsv, so long as it's in the form:
+
+epoch	metric_A	metric_B	…
+0	val:float	val:float	…
+1	val:float	val:float	…
+2	val:float	val:float	…
+⋮
+""")
+	sys.exit(0)
+
+DIRPATH = sys.argv[1]  # [0] == script path
+
+files = 0
+metrics = {}
+epochs = None
+
+for filepath in os.scandir(DIRPATH):
+	if not os.path.isdir(filepath):
+		continue
+	tbl = pd.read_csv(os.path.join(filepath, "metrics.tsv"), sep="\t")
+
+	# metrics.append(tbl)
+
+	for column in tbl.columns:
+		if column == "epoch":
+			if epochs is None:
+				epochs = tbl[column]
+			continue  # Row index implicitly retains this
+
+		if column not in metrics:
+			metrics[column] = []
+
+		metrics[column].append(tbl[column].values)
+		# print(column, tbl[column])
+	
+	# print("DEBUG:metrics", tbl)
+	files += 1
+
+logger.info(f"Read {files} files into crossval-stbl{files} analysis")
+
+stats = {}
+
+for metric in metrics.keys():
+    metrics[metric] = pd.DataFrame(metrics[metric]).transpose()
+
+    if metric not in stats:
+        stats[metric] = {}
+
+    stats[metric]["mad"] = scipy.stats.median_abs_deviation(
+        metrics[metric], axis=1
+    )  # median absolute deviation
+    stats[metric]["stddev"] = metrics[metric].std(axis=1)
+    stats[metric]["mean"] = metrics[metric].mean(axis=1)
+    stats[metric]["min"] = metrics[metric].min(axis=1)
+    stats[metric]["max"] = metrics[metric].max(axis=1)
+    stats[metric]["agg_min"] = stats[metric]["min"].min()
+    stats[metric]["agg_max"] = stats[metric]["max"].max()
+    stats[metric]["agg_stddev"] = metrics[metric].stack().std()
+    stats[metric]["agg_mean"] = metrics[metric].stack().std()
+    stats[metric]["agg_mad"] = scipy.stats.median_abs_deviation(
+        metrics[metric].stack()
+    )  # median absolute deviation
+
+    # print(stats[metric])
+
+    plt.figure(figsize=(12, 8))
+    plt.ylim(min(0, stats[metric]["agg_min"]), max(1, stats[metric]["agg_max"]))
+    plt.grid(visible=True, which="major", axis="y", linewidth=2)
+    plt.grid(visible=True, which="minor", axis="y", linewidth=1)
+    plt.minorticks_on()
+    plt.fill_between(
+        epochs,
+        stats[metric]["min"],
+        stats[metric]["max"],
+        alpha=0.2,
+        facecolor="#B7DE28",
+        edgecolor="#FDE724",
+        linestyle="dotted",
+        linewidth=1,
+    )
+    plt.fill_between(
+        epochs,
+        stats[metric]["mean"] - stats[metric]["mad"],
+        stats[metric]["mean"] + stats[metric]["mad"],
+        alpha=0.5,
+        facecolor="#228A8D",
+        edgecolor="#3CBB74",
+        linestyle="dashed",
+        linewidth=1,
+    )
+    plt.plot(epochs, stats[metric]["mean"], color="#450C54")
+    plt.title(f"{metric} // crossval-stbl{files}")
+    plt.xlabel("epoch")
+    plt.ylabel(metric)
+    plt.savefig(os.path.join(DIRPATH, f"crossval-stbl{files}_{metric}.png"))
+    plt.close()
+
+logger.success(f"Written {len(stats.keys())} graphs to {DIRPATH}")
diff --git a/aimodel/scripts/stbl-crossval.py b/aimodel/scripts/stbl-crossval.py
deleted file mode 100755
index 6536ddc..0000000
--- a/aimodel/scripts/stbl-crossval.py
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-
-from loguru import logger
-import pandas as pd
-
-# This script analyses metrics.tsv files from a series of identical experiments and reports metrics on them.
-# This is sometimes known as cross-validation, but we usually use the model series code crossval-stblX, where X is an integer >0.
-
-
-if len(sys.argv) <= 1:
-	print("""
-Usage:
-	scripts/stbl-crossval.mjs {{path/to/directory}}
-
-...in which the given directory contains a series of experiment root directories to include in the statistical analysis.
-
-This script is not picky about the format of the data in metrics.tsv, so long as it's in the form:
-
-epoch	metric_A	metric_B	…
-0	val:float	val:float	…
-1	val:float	val:float	…
-2	val:float	val:float	…
-⋮
-""")
-	sys.exit(0)
-
-DIRPATH = sys.argv[1]  # [0] == script path
-
-files = 0
-metrics = {}
-
-for filepath in os.scandir(DIRPATH):
-	tbl = pd.read_csv(os.path.join(filepath, "metrics.tsv"), sep="\t")
-
-	# metrics.append(tbl)
-
-	for column in tbl.columns:
-		if column == "epoch":
-			continue  # Row index implicitly retains this
-
-		if column not in metrics:
-			metrics[column] = []
-
-		metrics[column].append(tbl[column].values)
-		# print(column, tbl[column])
-
-	# print("DEBUG:metrics", tbl)
-	files += 1
-
-logger.info(f"Read {files} files into crossval-stbl{files} analysis")
-
-stats = {}
-
-for metric in metrics.keys():
-	metrics[metric] = pd.DataFrame(metrics[metric]).transpose()
-
-	if metric not in stats:
-		stats[metric] = {}
-
-	stats[metric]["aad"] = metrics[metric].max(axis=1) # mean/average absolute deviation
-	stats[metric]["mad"] = metrics[metric].max(axis=1) # median absolute deviation
-	stats[metric]["stddev"] = metrics[metric].std(axis=1)
-	stats[metric]["mean"] = metrics[metric].mean(axis=1)
-	stats[metric]["min"] = metrics[metric].min(axis=1)
-	stats[metric]["max"] = metrics[metric].max(axis=1)
-	stats[metric]["agg_min"] = stats[metric]["min"].min()
-	stats[metric]["agg_max"] = stats[metric]["max"].max()
-	stats[metric]["agg_stddev"] = metrics[metric].stack().std()
-	stats[metric]["agg_mean"] = metrics[metric].stack().std()
-	stats[metric]["agg_aad"] = metrics[metric].stack().max() # mean/average absolute deviation
-
-	print(stats[metric])
-