<jupyter_start><jupyter_text>p2plane metrics<jupyter_code>d[['A->B,rms1PSNR,p2plane', 'B->A,rms2PSNR,p2plane', 'Symmetric,rmsFPSNR,p2plane']].describe()
d[['A->B,rms1PSNR,p2plane', 'B->A,rms2PSNR,p2plane', 'Symmetric,rmsFPSNR,p2plane']].boxplot()
d2[['A->B,rms1PSNR,p2plane', 'B->A,rms2PSNR,p2plane', 'Symmetric,rmsFPSNR,p2plane']].describe()
d2[['A->B,rms1PSNR,p2plane', 'B->A,rms2PSNR,p2plane', 'Symmetric,rmsFPSNR,p2plane']].boxplot()<jupyter_output><empty_output><jupyter_text>p2point metrics<jupyter_code>d[['A->B,rms1PSNR,p2point', 'B->A,rms2PSNR,p2point', 'Symmetric,rmsFPSNR,p2point']].describe()
d[['A->B,rms1PSNR,p2point', 'B->A,rms2PSNR,p2point', 'Symmetric,rmsFPSNR,p2point']].boxplot()
d2[['A->B,rms1PSNR,p2point', 'B->A,rms2PSNR,p2point', 'Symmetric,rmsFPSNR,p2point']].describe()
d2[['A->B,rms1PSNR,p2point', 'B->A,rms2PSNR,p2point', 'Symmetric,rmsFPSNR,p2point']].boxplot()<jupyter_output><empty_output><jupyter_text>All PSNR metrics<jupyter_code>d[[x for x in d.columns if 'PSNR' in x]].describe()
d2[[x for x in d2.columns if 'PSNR' in x]].describe()<jupyter_output><empty_output><jupyter_text>rmsPSNR<jupyter_code>d[[x for x in d.columns if 'rms' in x and 'PSNR' in x]].boxplot()
d2[[x for x in d.columns if 'rms' in x and 'PSNR' in x]].boxplot()<jupyter_output><empty_output><jupyter_text>hPSNR<jupyter_code>d[[x for x in d.columns if 'hPSNR' in x]].boxplot()
d2[[x for x in d.columns if 'hPSNR' in x]].boxplot()<jupyter_output><empty_output><jupyter_text>Bitrates<jupyter_code>d[['bpov']].describe()
d2[['bpov']].describe()<jupyter_output><empty_output><jupyter_text>Points<jupyter_code>d['out point count'].describe()
d[d['decompressed_file'].map(lambda x: 'msft_6/phil9/ply/frame0000' in x)].iloc[0]
d2[d2['compressed_file'].map(lambda x: 'msft_bin_000001/phil9/ply/frame0000' in x)].iloc[0]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2077006507592191, 'fraction_numerical': 0.039045553145336226, 'mean_word_length': 11.466216216216216, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 45, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '631411', 'license': 'MIT', 'n_tokens_mistral': 904, 'n_tokens_neox': 880, 'n_words': 99, 'path': 'src/report.ipynb', 'repo_name': 'mauriceqch/pcc_geo_cnn', 'size': '1844'}