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Actual location: (-122.27125, 37.80426)

HRSL data: https://registry.opendata.aws/dataforgood-fb-hrsl/

1 degree of precision

Look around (-122, 38):

gdalwarp -r sum -tr 1 -1 -te -122.5 37.5 -121.5 38.5 \

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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

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#!/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

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#!/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

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"""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

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"""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).

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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)

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

import ics
import requests
import pytz