shawngraham

You are an excellent assistant with deep knowledge of research in the field of illegal and illicit antiquities. Generate an ontology for the text that follows, using the following guidelines: 1 – Denote subjects and objects using relative hash-based hyperlinks i.e., negating the use of example.com. 2- Output response to a code-block. 3 – RETURN data as well-formatted HIGH QUALITY RDF-TURTLE triples 

THOUGHT: I should identify all of the people involved first. 
THOUGHT: I should then identify any organizations involved. 
THOUGHT: I should then identify any artefacts. 
THOUGHT: I should then connect people, organizations, and objects by determining the predicates that join them. 
THOUGHT: It is important that I respect subject-predicate-object order of meaning. 
THOUGHT: There should be no empty nodes. 
THOUGHT: I should ignore in-text citations using parentheses. 

shawngraham

# OpenAlex Search Retriever

import requests
import os

class OpenAlexSearch():
    """
    OpenAlex Search Retriever
    """
    def __init__(self, query):

shawngraham

import requests

class OpenContextSearch():
    """
    Open Context Search Retriever
    """
    def __init__(self, query, content_type='subjects'):
        """
        Initializes the OpenContextSearch object
        Args:

shawngraham

# 1384: Letters from Barcelona - sonification version
# Data is travel time from Barcelona to Pisa and Barcelona to Avignon for letters (piano: to Pisa; string pluck: to Avignon). Duration is a function of the travel time scaled to 4 beats = 2 weeks. Notes are scored to correspond with the start date; pitch is a function of duration it took for the letter to reach its destination, so silences are meaningful. Longer journeys have higher and longer tones. The percussion plays the beat so that we can hear the progression of time; there is a background drone to indicate the passage of months.
# this code was rubber-ducked using GPT4-preview
require 'date'
ba_data = [9,9,8,7,9,8,8,8,12,9,8,9,10,8,8,8,8,11] #barcelona - avignon
ba_data_trip_start_date = ["1384-05-02","1384-05-09","1384-05-12","1384-05-20","1384-05-23","1384-05-26","1384-06-03","1384-06-15","1384-06-23","1384-07-11","1384-08-02","1384-08-06","1384-08-12","1384-09-01","1384-09-06","1384-09-06","1384-09-12","1384-09-19"]

bp_data = [26,22,34,23,23,21,

shawngraham

import os
import numpy as np
from PIL import Image
from skimage.color import rgb2gray
from skimage import filters, morphology
import argparse

def find_nonzero_extents(mask):
    rows = np.any(mask, axis=1)
    first_nonzero_row = next((i for i, row in enumerate(rows) if row), None)

shawngraham

#!/bin/bash

input_folder="articles" # Replace with your input directory path
output_folder="processed_articles" # Replace with your output directory path

# Create the output directory if it does not exist
mkdir -p "$output_folder"

# Set target token count and the acceptable range (20% of 100,000) 4t has 128k window
target_token_count=100000

shawngraham

import pandas as pd
import os
import argparse

def create_markdown_files(csv_filepath, output_dir):
    # Read the CSV file into a pandas DataFrame
    df = pd.read_csv(csv_filepath)
    
    # Check if the output directory exists, if not, create it
    if not os.path.exists(output_dir):

shawngraham

import argparse
import csv

# Initialize the argument parser
parser = argparse.ArgumentParser(description='Match entities from two CSV files and save the results to a new file.')
parser.add_argument('first_csv', help='The CSV file with "source" and "target" columns.')
parser.add_argument('second_csv', help='The CSV file to compare against.')
parser.add_argument('--output_csv', default='matched_rows.csv', help='The output CSV file with matched rows. Defaults to "matched_rows.csv".')

# Parse the command-line arguments

shawngraham

import tkinter as tk
from tkinter import filedialog
from tkinter import ttk, filedialog
import pandas as pd
from newsapi import NewsApiClient
import llm
import requests
from strip_tags import strip_tags
model = llm.get_model("orca-mini-3b-gguf2-q4_0") #local model through llm plugin llm-gpt4all.

shawngraham

# Stratigraphic Representation with Mermaid.js: A Guide to Conventions



This document outlines a set of conventions for representing complex stratigraphic sequences using Mermaid.js diagram syntax. The aim is to provide a standardized approach to visualize archaeological stratigraphy in a clear and comprehensible manner.



## 1. Superposition