iaindillingham

from ehrql import case, codelist_from_csv, create_dataset, days, when
from ehrql.tables.beta.core import medications, patients
from ehrql.tables.beta.tpp import (
    addresses,
    clinical_events,
    hospital_admissions,
    practice_registrations,
)

index_date = "2023-10-01"

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import glob
import pathlib


def match_paths_1(pattern):
    # not a generator function
    return (pathlib.Path(path) for path in glob.iglob(pattern))


def match_paths_2(pattern):

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# A common pattern when using OpenSAFELY for time series analysis is to extract one
# cohort for slow-to-extract variables that we don't expect to change over time, and
# multiple cohorts (e.g. by week or by month) for fast-to-extract variables that we
# expect to change over time. Each "fast" cohort is then joined to the "slow" cohort for
# analysis.
#
# In this gist, we compare the memory profiles of two join strategies found in the
# OpenSAFELY documentation: a map strategy and a merge strategy. We find that on a
# dataset with an order of magnitude difference between the population size and the
# sample size, the map strategy uses roughly 2.9 times more memory than the merge

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import logging

logging.basicConfig(level=logging.INFO)


class LoggingProperty:
    def __init__(self):
        logging.info(f"Initializing '{self.__class__.__name__}'")

    def __set_name__(self, owner, name):

iaindillingham

import dataclasses
import random
import typing


@dataclasses.dataclass
class PointDC:
    x: float
    y: float

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[
  {
    "id": 0,
    "parent": null,
    "name": "Name"
  },
  {
    "id": 1,
    "parent": 0,
    "name": "Name"

iaindillingham

countries_topology.json is derived from the Natural Earth 1:110m Cultural Vectors. Each feature has an id property, which is the ISO A2 country code for the country; and a properties.title property, which is the English-language name of the country.

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iaindillingham

iaindillingham

license: mit
height: 500
scrolling: no
border: yes