<jupyter_start><jupyter_text>EOF analysis (S-mode)EOF analysis in S-mode (maximising temporal variance)<jupyter_code>import xarray as xr
import matplotlib.pyplot as plt

da = xr.tutorial.load_dataset('air_temperature')['air']
da.isel(time=0).plot()
plt.savefig('blaa.jpg')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1357615894039735, 'fraction_numerical': 0.0033112582781456954, 'mean_word_length': 8.774193548387096, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '548375', 'license': 'MIT', 'n_tokens_mistral': 119, 'n_tokens_neox': 114, 'n_words': 20, 'path': 'docs/auto_examples/uni/plot_example.ipynb', 'repo_name': 'mschulzie/xeofs', 'size': '302'}