geobabbler

import geopandas as gpd
from shapely.geometry import Point
import pandas as pd
from datetime import datetime
import os
import shutil
import zipfile

%pip install geopandas shapely geojsonio

geobabbler

WITH geodata AS (
SELECT
	ROW_NUMBER() OVER () AS OBJECTID,
    *
FROM
    read_parquet('~/Downloads/IRL.parquet') LIMIT 1000)
select json_object(
'type', 'FeatureCollection',
    'features', array_agg(
        json_object(

geobabbler

import requests
import openai
import urllib
import json
from duckduckgo_search import DDGS

# Set your OpenAI API key here
openai.api_key = "your-openai-api-key"

def search_duckduckgo(query):

geobabbler

-- public.image_embeddings definition

-- Drop table

-- DROP TABLE public.image_embeddings;

CREATE TABLE public.image_embeddings (
	id serial4 NOT NULL,
	image_path text NULL,
	embedding public.vector NULL,

geobabbler

import os
import psycopg2
import numpy as np
from PIL import Image
from scipy.spatial.distance import cosine
import torch
from torchvision import models, transforms

# Database configuration
DB_NAME = "database"

geobabbler

import os
import psycopg2
from PIL import Image
import torch
from torchvision import models, transforms

# Database configuration
DB_NAME = "database"
DB_USER = "user"
DB_PASSWORD = "password"

geobabbler

import requests
from bs4 import BeautifulSoup
import os

qry = "glacier" #wikimedia commons query
# Define the search URL
search_url = f"https://commons.wikimedia.org/w/index.php?search={qry}&title=Special:MediaSearch&go=Go&type=image"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"

geobabbler

import pdfkit
import requests

'''
The current theme will be applied to outputs, so it is recommended to switch to a simple theme before exporting.
'''

#Generate PDF from individual post URL
def url_to_pdf(url, output_filename):
    try:

geobabbler

SELECT
  ARRAY_TO_STRING(ARRAY_AGG(CONCAT(st_x(q.loc),',',st_y(q.loc))), ';') AS coords
FROM (
  WITH
    DATA AS (
    SELECT
      loc,
      resource_id,
      wkt,
      capture_date,

geobabbler

WITH data AS (
SELECT name, loc, resource_id, wkt, capture_date, (LAG(capture_date) OVER (PARTITION BY resource_id ORDER BY capture_date ASC)) AS prev_date,
        (LAG(wkt) OVER (PARTITION BY resource_id ORDER BY capture_date ASC)) AS prev_loc
FROM
(SELECT name, resource_id, loc, st_astext(loc) as wkt, capture_date FROM `my_project.my_dataset.geo_sample`
 order by capture_date desc) q
ORDER BY capture_date
)
SELECT name, resource_id, loc, capture_date FROM data
 WHERE