migurski

migurski

import numpy as np
from scipy.optimize import root_scalar
from scipy.special import expit  # inverse logit

def logit_shift_baseline(df, ndv_col, nrv_col, target=0.5):
    ndv = df[ndv_col].values
    nrv = df[nrv_col].values
    turn = ndv + nrv

    # Compute log-odds where turnout > 0

migurski

#!/usr/bin/env python3
"""
Feature finder script to match OSM and Overture features based on tags and spatial proximity.

Usage examples:
    feature-finder.py aerodrome airport --name OAK
    feature-finder.py national_park --name Alcatraz
"""
from __future__ import annotations

migurski

#!/usr/bin/env python3
import itertools
import typing
import math

import cairo
import shapely.affinity
import shapely.geometry

DISTANCE_CUTOFF = 2  # max jump length to bridge with a curve

migurski

"""Convert input CSV to OpenElections CSV

Adapt large tabular sources like this Lenawee County example (1) converted from PDFs (2)
to format required by OpenElections (3) intepreting categories of votes, candidates, and
offices along the way.

1) https://docs.google.com/spreadsheets/d/1iOEqLFcwuA3J_HXUtEWIZUirA7J1_NdsnVUnc8z0TZY/edit?gid=2129694166#gid=2129694166
2) https://github.com/openelections/openelections-sources-mi/blob/master/2024/general/Gogebic%20MI%20Official%20Statement%20of%20Votes%20Cast%20with%20Certification%2011.5.2024.pdf
3) https://github.com/openelections/openelections-data-mi/issues/69

migurski

"""Coalesce Excel sheets that appear to be excerpts of larger sheets

Requires pylightxl: https://pylightxl.readthedocs.io/en/latest/

PDF sources like this Gogebic County example (1) can be converted by AWS Textract
into usable Excel workbooks with multiple sheets (2). This scripts makes a best-effort
attempt to stitch together large tables broken up across multiple pages based on
row counts, column counts, and cell values representing precincts. The results still
require a lot of manual interpretation to be turned into useful OpenElections data (3).

migurski

migurski

import datetime
import math
import mercantile
import pytz
import suncalc

def date(yyyy, mm, dd, hh):
    dt_naive = datetime.datetime(yyyy, mm, dd, int(hh), int((60 * hh) % 60))
    dt_local = dt_naive.astimezone(pytz.timezone('America/Los_Angeles'))
    dt_utc = dt_local.astimezone(pytz.utc)

migurski

#!/usr/bin/env python3
import datetime
import json
import os
import sys
import gzip

import ics
import requests
import pytz

migurski

INTRODUCTION
============
This archive was generated at the request of the following user:
- @username at the time the archive was generated: migurski
- Account ID: 2790981

The easiest way to navigate your archive is to open the HTML renderer in a desktop web browser by double clicking the “Your archive” file included in the main folder once the archive is unzipped.

Note that the HTML renderer only works if the archive is less than 50GB. Also note that the HTML renderer only includes a subset of the data included in the archive. To see all the data included in the archive, please navigate the JSON files located in the “data” folder.