<jupyter_start><jupyter_text>Notebook arguments- **data_id** (*int*): Select which data file to load. Valid values: 0, 1, 2.- **analysis_type** (*string*): Which analysis type to perform. Valid valuse 'a', 'b' and 'c'  Template NotebookThis notebook (pretends to) executes one of the three analysis type 'a', 'b' or 'c', on one of the data files in a set (identified by an index).You can either run this notebook directly, or run it through the [master notebook](example-master-notebook.ipynb) for batch processing.  Load the dataWe mock loading some data. We use `data_id` as a seed to generate different data-sets.In the real-world case can be the index in a list of data files.<jupyter_code>import numpy as np
np.random.seed(data_id)
data = np.random.randn(100)<jupyter_output><empty_output><jupyter_text>Processing<jupyter_code>analysis_dict = dict(a=np.mean, b=np.max, c=np.min)
result = analysis_dict[analysis_type](data)
print('Result of analysis "%s" on dataset %d is %.3f.' % (analysis_type, data_id, result))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11545801526717557, 'fraction_numerical': 0.006679389312977099, 'mean_word_length': 5.7243589743589745, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '318916', 'license': 'MIT', 'n_tokens_mistral': 340, 'n_tokens_neox': 331, 'n_words': 126, 'path': 'notebooks/othernotebook.ipynb', 'repo_name': 'chrisbarnettster/cfg_array_analysis_notebooks', 'size': '1048'}