<jupyter_start><jupyter_text>Using high-level science dataHigh-level science data includes information provided as functions, histograms, tables etc. containing data derived from e.g. simulations like detector acceptance, background expectations etc.<jupyter_code>from openkm3.store import KM3Store
store = KM3Store()<jupyter_output>INFO:root:Loaded catalog from cache.<jupyter_text>Lookup table: Detector acceptanceThe detector acceptance (here for the ANTARES 2007-2017 neutrino sample) is provided as lookup table.<jupyter_code>acceptance = store.get("ana20_01_acc")
acceptance.show_paraminfo()
acceptance.lookup(xvalue = 100, yvalue = 10)
df = acceptance.get_dataframe()
df.plot(kind = "line", logy=True, legend=False)<jupyter_output><empty_output><jupyter_text>Function: Number of background events estimateThis information (again for the ANTARES 2007-2017 sample) is provided as polynomial function.<jupyter_code>bkg = store.get("ana20_01_bkg")
bkg.functiontype
bkg.show_paraminfo()
bkg.evaluate(20) # returns function result for given value<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08635097493036212, 'fraction_numerical': 0.031569173630454965, 'mean_word_length': 8.058823529411764, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '630228', 'license': 'MIT', 'n_tokens_mistral': 346, 'n_tokens_neox': 318, 'n_words': 90, 'path': 'neutrinos/openkm3_datalevel4.ipynb', 'repo_name': 'vishu-byte/school2021', 'size': '1077'}