brainwalk 0.0.3

Spatial graph embeddings for ObsidianMD

brainwalk

::: {.cell 0=‘h’ 1=‘i’ 2=‘d’ 3=‘e’}

import sys
sys.path.append("..")
from brainwalk.core import *

:::

Spatial graph embeddings for ObsidianMD

Install

pip install brainwalk

How to use

# Find the Obsidian Vault directory and assert that it exists
import os
from pathlib import Path
vault_dir = Path(os.getcwd()) / 'vault-stub'
assert vault_dir.exists()

# Retrieve a Gensim word2vec model of your Obsidian Graph
from brainwalk.core import brainwave
from wirewalk.core import jaccard_coefficient

model = brainwave(vault_dir,jaccard_coefficient)

model.wv.key_to_index

{'Vulnera ubera': 0,
 'Causam mihi': 1,
 'Sussudio': 2,
 'Ne fuit': 3,
 'Brevissimus moenia': 4,
 'Alimenta': 5,
 'American Psycho (film)': 6,
 'Aras Teucras': 7,
 'Tydides': 8,
 'Dives': 9,
 'Caelum': 10,
 'Vita': 11,
 'Isolated note': 12,
 'Tarpeia': 13,
 'Manus': 14,
 'Amor': 15,
 'lipsum/Isolated note': 16,
 'Bacchus': 17,
 'Virtus': 18,
 'Aetna': 19}

model.wv.most_similar("Vulnera ubera")

[('Ne fuit', 0.9992153644561768),
 ('Bacchus', 0.9991818070411682),
 ('Manus', 0.9991797804832458),
 ('Tydides', 0.9991616606712341),
 ('Sussudio', 0.9991515278816223),
 ('Vita', 0.9991357922554016),
 ('American Psycho (film)', 0.9991220831871033),
 ('lipsum/Isolated note', 0.9991198182106018),
 ('Virtus', 0.9991057515144348),
 ('Causam mihi', 0.999089777469635)]