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Job request: 386

Organisation:
The London School of Hygiene & Tropical Medicine
Workspace:
households
ID:
pv34slwgquvkqbt4

This page shows the technical details of what happened when the authorised researcher Kevin Wing requested one or more actions to be run against real patient data in the project, within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

Pipeline

Show project.yaml
version: "3.0"

expectations:
  population_size: 100000

actions:
  generate_cohort:
    run: cohortextractor:latest generate_cohort
    outputs:
      highly_sensitive:
        cohort: output/input.csv

  prepare_data:
    run: stata-mp:latest analysis/01_hh_cr_analysis_dataset.do
    needs: [generate_cohort]
    outputs:
      highly_sensitive:
        allvars: output/hh_analysis_dataset.dta

  prepare_epid_periods:
    run: stata-mp:latest analysis/01_hh_cr_epidemicPeriod_datasets.do
    needs: [prepare_data]
    outputs:
      highly_sensitive:
        allvars: output/hhsThatCrossedBinaryEpidemicPeriod.dta
      moderately_sensitive:
        graph: released_outputs/overall_hhEpidemicDistributions.svg

  output_descriptive:
    run: stata-mp:latest analysis/02_hh_an_caseFreq_descriptive_plots.do
    needs: [prepare_data]
    outputs:
      moderately_sensitive:
        graph1: released_outputs/an_caseFreq_descr_overall_HH_Histogram.svg
        graph2: released_outputs/an_caseFreq_descr_overall_HH_HistogramOverlay.svg
        log1: released_outputs/02_hh_an_caseFreq_descriptive_plots.log
        log2: released_outputs/an_caseDescrTable.txt


  generate_model_data:
    run: python:latest python analysis/generate_model_data.py
    needs: [prepare_data]
    outputs:
      moderately_sensitive:
        log: generate_model_data.log
        timeseries: output/case_series.pickle
        agecats: output/age_categories_series.pickle

  run_model_20_1_1:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 1 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_1.log


  run_model_20_1_3:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 3 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_3.log


  run_model_20_1_5:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 5 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_5.log


  run_model_20_1_81:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 81 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_81.log


  run_model_20_1_83:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 83 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_83.log


  run_model_20_1_85:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 85 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_85.log


  run_model_20_1_23:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 23 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_23.log


  run_model_20_1_37:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 37 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_37.log


  run_model_20_1_42:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 42 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_42.log


  run_model_20_1_13:
    run: python:latest python analysis/opensafely_age_hh_th.py --starting-parameter 13 --add-ridge 20.1
    needs: [generate_model_data]
    outputs:
      moderately_sensitive:
        log: opensafely_age_hh_ridge_20_1_and_seed_13.log

  run_all:
    needs:
      [run_model_20_1_1, run_model_20_1_3, run_model_20_1_5, run_model_20_1_81, run_model_20_1_83, run_model_20_1_85, run_model_20_1_23, run_model_20_1_37, run_model_20_1_42, run_model_20_1_13]
    # In order to be valid this action needs to define a run commmand and some
    # output. We don't really care what these are but the below does the trick.
    # In a future release of the platform, this special action won't need to be
    # defined at all.
    run: cohortextractor:latest --version
    outputs:
      moderately_sensitive:
        whatever: project.yaml

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:01:05

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job information

Status
Succeeded
Backend
TPP
Workspace
households
Requested by
Kevin Wing
Branch
master
Force run dependencies
Yes
Git commit hash
b5d4020
Requested actions
  • prepare_epid_periods

Code comparison

Compare the code used in this Job Request