Tools to extract content from ipython (jupyter) notebooks
To install: pip install sheet
filepath = get_path_of_current_notebook()
# Example: '~/my_notebook_folder/some_notebook.ipynb'from sheet.contents import get_ipynb_cells, get_ipynb_cells_source
filepath = '~/my_notebook_folder/some_notebook.ipynb'
cells = get_ipynb_cells(filepath)
assert type(cells), type(cells[0]) == (list, dict)
cells = get_ipynb_cells_source(filepath)
assert type(cells), type(cells[0]) == (list, str)from sheet.contents import get_ipynb_cells_full_text
notebook_text = get_ipynb_cells_full_text(filepath)
print(notebook_text)Index and search the cells of a notebook
from sheet import SingleNotebookSearch
search = SingleNotebookSearch(src=None) # if no filepath (src) to a notebook is given, will use the "current notebook"
result_indices = search('lines iterize')
print(result_indices)
print("\n---- Contents of first hit ----")
print(search[result_indices[0]]) # print the contents of the first result[70 225 226 198 199 196 200 201 193 197]
---- Contents of first hit ----
process_wf = Line(
partial(fixed_step_chunker, chk_size=DFLT_CHK_SIZE),
iterize(process_chk)
)
from sheet.contents import SearchNotebooks
search = SearchNotebooks('~/my_notebooks_folder', max_levels=0) # enter max_levels=None for full recursive
search('bayesian')array(['Spyn 01 - Potentials.ipynb',
'Bayes 01 - Potentials-Only explanation.ipynb', 'taped.ipynb',
'separation of concerns - how py2store does it.ipynb',
'equate.ipynb', 'peruse.ipynb',
'hum, taped, lined -- feeding audio to a pipeline.ipynb',
'owner.ipynb', 'best of 2020.ipynb',
'Bayes 02 - Potentials - And drug data example.ipynb'],
dtype=object)
Okay, we have a list of notebooks that match our query (i.e. the highest average alignment to our query -- not just keyword matching!), but what cells in particular have the highest relevance?
Well, we can now peruse our notebook at that level, with a notebook cells searcher. (Note: You can combine both to make a cell-level searcher from the folder level.)
ss = search.search_notebook('Spyn 01 - Potentials.ipynb')
ss('bayesian')array([['Spyn 01 - Potentials.ipynb', 6],
['Spyn 01 - Potentials.ipynb', 2],
['Spyn 01 - Potentials.ipynb', 71],
['Spyn 01 - Potentials.ipynb', 88],
['Spyn 01 - Potentials.ipynb', 91],
['Spyn 01 - Potentials.ipynb', 84],
['Spyn 01 - Potentials.ipynb', 85],
['Spyn 01 - Potentials.ipynb', 86],
['Spyn 01 - Potentials.ipynb', 87],
['Spyn 01 - Potentials.ipynb', 82]], dtype=object)
ss['Spyn 01 - Potentials.ipynb', 6]'# Potentials - A key data structure to Discrete Bayesian Inference'
ss['Spyn 01 - Potentials.ipynb', 87]'### Making a few potentials from pts data'
