brainwalk 0.0.2

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, jaccard_coefficient

model = brainwave(vault_dir,jaccard_coefficient)

model.wv.key_to_index

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

model.wv.most_similar("Vulnera ubera")

[('Sussudio', 0.9991790056228638),
 ('Aetna', 0.9991780519485474),
 ('Tydides', 0.9991397857666016),
 ('Bacchus', 0.9991208910942078),
 ('Virtus', 0.9990988373756409),
 ('Brevissimus moenia', 0.9990975260734558),
 ('Ne fuit', 0.9990928769111633),
 ('Dives', 0.9990816116333008),
 ('Alimenta', 0.9990617036819458),
 ('Amor', 0.9990396499633789)]