<jupyter_start><jupyter_text>Load Data<jupyter_code>df = pd.read_csv('../assets/results/decision_point_results.csv')
df = df[df.columns[:12]]
df.head()
# test = df[df['range_noise'] == 0.09]
# test[~test['found_hole']]<jupyter_output><empty_output><jupyter_text>Visualize Breaks<jupyter_code>def visualize_break(df, break_point=0.1):
    df['found_hole'] = df['hole_remaining'] <= break_point
    df['missed_hole'] = ~df['found_hole']
    
    
    df_simple = df[df['missed_hole']]
    g = sns.catplot(x="height", col="range_noise", kind='count', hue='lidar_beams', data=df_simple)
#     g.set(ylim=(0, 10))
    
visualize_break(df)
data_decision = [
    dict(lidar_beams=16, range_noise=0.01, height=15),
    dict(lidar_beams=16, range_noise=0.03, height=10),
    dict(lidar_beams=16, range_noise=0.05, height=10),
    dict(lidar_beams=16, range_noise=0.07, height=5),
    dict(lidar_beams=16, range_noise=0.09, height=5),
    dict(lidar_beams=32, range_noise=0.01, height=20),
    dict(lidar_beams=32, range_noise=0.03, height=20),
    dict(lidar_beams=32, range_noise=0.05, height=20),
    dict(lidar_beams=32, range_noise=0.07, height=20),
    dict(lidar_beams=32, range_noise=0.09, height=20),
    dict(lidar_beams=64, range_noise=0.01, height=30),
    dict(lidar_beams=64, range_noise=0.03, height=30),
    dict(lidar_beams=64, range_noise=0.05, height=30),
    dict(lidar_beams=64, range_noise=0.07, height=30),
    dict(lidar_beams=64, range_noise=0.09, height=30),
]
df_decision = pd.DataFrame.from_records(data_decision)
df_decision = df_decision.rename(columns={"lidar_beams": "# Beams", "height": "Decision Height (m)", "range_noise": "Range Noise (m)"})
g = sns.catplot(data=df_decision, y='Decision Height (m)', x='Range Noise (m)', hue='# Beams', legend_out=False)
g.savefig('../assets/imgs/decision_point.pdf')
def visualize_iou(df, break_point=0.1):
    
    df_simple = df.melt(id_vars=["uid", "sub_uid", "range_noise", "lidar_beams", "height"],
                                    value_vars=['pl_base_iou', 'pl_seg_infer_iou'],
                                    var_name="alg", 
                                    value_name="iou")
#     return df_simple
    g = sns.catplot(x="height", y='iou', col="range_noise", row='lidar_beams', hue='alg', data=df_simple, kind='bar')
    
    
visualize_iou(df)<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '405305', 'license': 'MIT', 'n_tokens_mistral': 1039, 'n_tokens_neox': 948, 'n_words': 124, 'path': 'notebooks/DecisionPointAnalysis.ipynb', 'repo_name': 'JeremyBYU/UnrealRooftopLanding', 'size': '2354'}