Maxxen

import duckdb
import flask

# Initialize Flask app
app = flask.Flask(__name__)

# Setup a global DuckDB connection with spatial extension loaded
# Connect to a persistent database file with the geometry data
config = {"allow_unsigned_extensions": "true"}
con = duckdb.connect("tiles.db", True, config)

jdbcode

darrenwiens

import ipyleaflet
import json
import leafmap
import requests

stac_api = "https://earth-search.aws.element84.com/v0"
search_endpoint = f"{stac_api}/search"

collection = "sentinel-s2-l2a-cogs"
payload = {

jdbcode

jdbcode

KMarkert

import numpy as np
import pandas as pd

def sklearn_tree_to_ee_string(estimator, feature_names):

    # extract out the information need to build the tree string
    n_nodes = estimator.tree_.node_count
    children_left = estimator.tree_.children_left
    children_right = estimator.tree_.children_right
    feature_idx = estimator.tree_.feature

KMarkert

jdbcode

"""
Copyright 2020 Justin Braaten

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

jdbcode

Mlawrence95

import pandas as pd

def confusion_matrix(df: pd.DataFrame, col1: str, col2: str):
    """
    Given a dataframe with at least
    two categorical columns, create a 
    confusion matrix of the count of the columns
    cross-counts
    
    use like: