diff --git a/training/gen2_architecture/optimal_sensor_placement/compute_optimal_sensor_placement.py b/training/gen2_architecture/optimal_sensor_placement/compute_optimal_sensor_placement.py
index 8949be2..48e2659 100644
--- a/training/gen2_architecture/optimal_sensor_placement/compute_optimal_sensor_placement.py
+++ b/training/gen2_architecture/optimal_sensor_placement/compute_optimal_sensor_placement.py
@@ -1,23 +1,39 @@
 from load_data import df, Data
 import numpy as np
 
-Cfs = np.stack([
-    df[f"{side}_bl_H_{i}"]
+df = df.to_pandas().dropna()
+
+data = np.stack([
+    # (df[f"{side}_bl_theta_{i}"])
+    (df[f"{side}_bl_H_{i}"])
+    for side in [
+        "upper",
+        "lower",
+    ]
     for i in range(Data.N)
+], axis=1)
+
+data_flipped = np.stack([
+    # (df[f"{side}_bl_theta_{i}"])
+    (df[f"{side}_bl_H_{i}"])
     for side in [
+        "lower",
         "upper",
-        # "lower"
     ]
+    for i in range(Data.N)
 ], axis=1)
 
-# Remove rows with any nans
-Cfs = Cfs[~np.any(np.isnan(Cfs), axis=1)]
+# Flip and augment
+data = np.concatenate([
+    data,
+    data_flipped
+], axis=0)
 
 import pysensors as ps
 
-model = ps.SSPOR(n_sensors=30)
+model = ps.SSPOR(n_sensors=64)
 model.fit(
-    x=Cfs[:300],
+    x=data,
 )
 
 sensor_ids = np.sort(model.get_selected_sensors())
@@ -30,6 +46,8 @@
 import matplotlib.pyplot as plt
 import aerosandbox.tools.pretty_plots as p
 
+fig, ax = plt.subplots(figsize=(6, 3))
+
 af = asb.KulfanAirfoil("naca0012")
 af.draw(show=False)
 for i in sensor_ids:
@@ -39,4 +57,30 @@
     else:
         x = np.linspace(0, 1, Data.N)[i - Data.N]
         plt.plot(*af.lower_coordinates(x).T, ".k", markersize=10)
-p.show_plot("Optimal Sensor Locations")
+
+subtitle = "Question: \"If I have 64 sensors, where should I put them on a generic airfoil to reconstruct $H(s)$ with minimum error?\""
+import textwrap
+
+plt.text( # Top center of axes, textwrapped
+    0.5, 0.95,
+    textwrap.fill(subtitle, width=65),
+    horizontalalignment='center',
+    verticalalignment='top',
+    transform=plt.gca().transAxes,
+    fontsize=11, alpha=0.7,
+)
+
+answer = "Answer: \"Roughly evenly across the airfoil.\""
+
+plt.text(
+    0.5, 0.05,
+    textwrap.fill(answer, width=65),
+    horizontalalignment='center',
+    verticalalignment='bottom',
+    transform=plt.gca().transAxes,
+    fontsize=11, alpha=0.7,
+)
+
+p.show_plot(
+    "Optimal Sensor Locations for Compressed\nSensing of Shape Factor"
+)
diff --git a/training/gen2_architecture/optimal_sensor_placement/generate_high_dim_xfoil_data.py b/training/gen2_architecture/optimal_sensor_placement/generate_high_dim_xfoil_data.py
index 13aca82..4364463 100644
--- a/training/gen2_architecture/optimal_sensor_placement/generate_high_dim_xfoil_data.py
+++ b/training/gen2_architecture/optimal_sensor_placement/generate_high_dim_xfoil_data.py
@@ -14,7 +14,7 @@
     # num_cpus=2,
 )
 
-datafile = "data_xfoil.csv"
+datafile = "hd_data_xfoil.csv"
 n_procs = int(ray.cluster_resources()["CPU"])
 print(f"Running on {n_procs} processes.")
 
diff --git a/training/gen2_architecture/optimal_sensor_placement/load_data.py b/training/gen2_architecture/optimal_sensor_placement/load_data.py
index e8fe883..41ca42e 100644
--- a/training/gen2_architecture/optimal_sensor_placement/load_data.py
+++ b/training/gen2_architecture/optimal_sensor_placement/load_data.py
@@ -15,7 +15,7 @@
 
 raw_dfs = {}
 
-for csv_file in data_directory.glob("data*.csv"):
+for csv_file in data_directory.glob("hd_data*.csv"):
     print(f"Reading {csv_file}...")
     raw_dfs[csv_file.stem] = pl.read_csv(
         csv_file, has_header=False,