<jupyter_start><jupyter_text>Calculate Likelihood!<jupyter_code>import scipy.linalg as linalg
good_cov = ACT_E.cov[:,ACT_E.good_bins][ACT_E.good_bins,:]
tmp = linalg.cho_solve( linalg.cho_factor(good_cov), b = (Cl_d - Cl_th) )
like = (Cl_d - Cl_th).T @ tmp
like<jupyter_output><empty_output><jupyter_text>We calculate the fractional error, $ \delta \mathcal{L} / \mathcal{L} $.<jupyter_code>(like - 240.385376700682) / like<jupyter_output><empty_output><jupyter_text>There's some minute differences due to some numerical issue in the tSZ, will resolve soon.<jupyter_code># plt.plot((Cl_d - Cl_th) - np.genfromtxt('untitled.txt'))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13960546282245828, 'fraction_numerical': 0.02276176024279211, 'mean_word_length': 7.048780487804878, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '601068', 'license': 'MIT', 'n_tokens_mistral': 281, 'n_tokens_neox': 264, 'n_words': 45, 'path': 'ACT-E Likelihood Calc.ipynb', 'repo_name': 'xzackli/mfpy', 'size': '659'}