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# https://github.com/nerfstudio-project/nerfstudio/issues/2438 | |
conda create --name nerfstudio -y python=3.8 | |
conda activate nerfstudio | |
python -m pip install --upgrade pip | |
pip uninstall torch torchvision functorch tinycudann | |
pip install torch==2.0.1 torchvision==0.15.2 | |
git clone https://github.com/nerfstudio-project/nerfstudio.git | |
cd nerfstudio |
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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. |
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import requests | |
class OpenContextSearch(): | |
""" | |
Open Context Search Retriever | |
""" | |
def __init__(self, query, content_type='subjects'): | |
""" | |
Initializes the OpenContextSearch object | |
Args: |
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# OpenAlex Search Retriever | |
import requests | |
import os | |
class OpenAlexSearch(): | |
""" | |
OpenAlex Search Retriever | |
""" | |
def __init__(self, query): |
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# 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, |
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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) |
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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): |
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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 |
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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. |
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# 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 |
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