{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " skiping ...\n", " skiping ...\n", " skiping ...\n", " skiping ...\n", " skiping ...\n", " skiping ...\n", " skiping ...\n" ] }, { "data": { "text/plain": [ "2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", " This notebook is designed to generate SQL syntax all the quasi-identifiers for the patients in the database\n", " The resulting SQL will be run against bigquery to produce a table with every record mapping to a patient\n", " \n", "\"\"\"\n", "\n", "from risk import *\n", "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-prod.json',dataset='combined20180822',key_field='person_id',key_table='person',filter=['person','observation'])\n", "r = ihandle.migrate_tables()\n", "len(r)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "u' SELECT person.person_id , person.year_of_birth , person.month_of_birth , person.day_of_birth , person.birth_datetime , person.race_concept_id , person.ethnicity_concept_id , person.location_id , person.care_site_id , person.person_source_value , person.gender_source_value , person.gender_source_concept_id , person.race_source_value , person.ethnicity_source_value , basic_observation.sex_at_birth AS sex_at_birth1 , basic_observation.birth_date AS birth_date1 , basic_observation.race AS race1 , basic_observation.zip AS zip1 , basic_observation.city AS city1 , basic_observation.state AS state1 , basic_observation.gender AS gender1 FROM (select * from deid_image.person ) as person INNER JOIN (select * from deid_image.basic_observation ) as basic_observation ON basic_observation.person_id = person.person_id '" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ihandle = UtilHandler(path='/home/steve/dev/google-cloud-sdk/accounts/curation-test.json',dataset='deid_image',key_field='person_id',key_table='person',filter=['person','basic_observation'])\n", "ihandle.create_table().replace('\\n',' ')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15rc1" } }, "nbformat": 4, "nbformat_minor": 2 }