lgmoneda

import json
import urllib.parse
import spacy
import requests
import re

from http.server import BaseHTTPRequestHandler, HTTPServer
from multi_language_utils import is_en_text, translate_to_english, load_language_detection_model
from urllib.parse import parse_qs, urlparse

lgmoneda

;; Blogging
;; The following function helps me to write in a single file in org-roam to produce my personal blog posts.
;; It avoids copying and pasting to the markdown file for jekyll.

(defun lgm/publish-org-roam-to-jekyll-html (github-repo-dir output-dir branch)
  "Export current Org Roam file to Jekyll-compatible HTML5 with YAML front matter.

GITHUB-REPO-DIR: Path to your GitHub Pages repository.
OUTPUT-DIR: Directory inside the repository where the HTML file will be placed.
BRANCH: Branch to which changes should be committed (e.g., 'master')."

lgmoneda

;; Textual topography
(defun textual-topography ()
  "Prompt for domain, characteristic/anti-characteristic pair, and selected text.
   Save selected text to a file and call a Python script with the arguments."
  (interactive)

  ;; Define the domains and their associated characteristic/anti-characteristic pairs
  (setq domain-characteristics
        '(("Mood" . (("Happy" . "Unhappy") ("Calm" . "Agitated") ("Optimistic" . "Pessimist")))
          ("Style" . (("Complex" . "Simple")))

lgmoneda

import numpy as np
import pandas as pd
import itertools
import seaborn as sns
import matplotlib.pyplot as plt
import openai
import os
import time

from dotenv import load_dotenv

lgmoneda

from datetime import date

today = date.today()

# Minor modification from langchain library example
PROMPT_PRE = """
My name is Luis Moneda. You are my personal assistant. Given the following extracted parts of long documents from my personal notes and a question, create a final answer with references ("SOURCES"). 
The content of the document will be preceeded by the title hierarchy it was taken from inside brackets, which you should use to get context about it.
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer that only contains numbers. Today is {}.

lgmoneda

import os
import re

import numpy as np
import pandas as pd
import torch
import warnings
from adjustText import adjust_text
from sentence_transformers import SentenceTransformer
from sklearn.manifold import TSNE

lgmoneda

(defun my-org-make-link ()
  "Turn selected text into an Org mode link by replacing the selected
text with a link that uses the selected text as the link description
and the clipboard contents as the link URL."
  (interactive)
  (let ((link-description (if (use-region-p)
                              (buffer-substring-no-properties (region-beginning) (region-end))
                            (read-string "Link description: ")))
        (clipboard-contents (current-kill 0)))
    (delete-region (region-beginning) (region-end))

lgmoneda

Adding Processing "color"

Add the rainbow-mode package to your init.el and the proper config to add it to the processing mode and recognize the color:

(use-package rainbow-mode
  :ensure t
  :config
  (add-to-list 'rainbow-html-colors-major-mode-list 'processing-mode)
 (add-to-list 'rainbow-html-rgb-colors-font-lock-keywords

lgmoneda

#TabsToolbar > * {
	visibility: collapse;
}

#titlebar{ margin-bottom: -22px}

/* hide navigation bar when it is not focused; use Ctrl+L to get focus */
#main-window:not([customizing]) #navigator-toolbox:not(:focus-within):not(:hover) {
	margin-top: -105px;			/**/
}

lgmoneda

import pandas as pd
import numpy as np

data = [[1, 5, 2], [2, 3, 1], [3, 1, 1], [1, 3, 2]]
df = pd.DataFrame(data, columns=["values_1", "values_2", "age"])

agg_columns = ["values_1", "values_2"]
agg_functions = [np.mean, np.std]
agg_dict = {col + "_" + f.__name__: (col, f) for col in agg_columns for f in agg_functions}
agg = df.groupby("age").agg(**agg_dict)