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Job request: 5782

Organisation:
Bennett Institute
Workspace:
pincer-measures-emis
ID:
tv5enurtgycysswa
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Louis Fisher 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: 5000

actions:
  generate_study_population_1:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-09-01 to 2020-05-01 by month" --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input_*.feather

  generate_study_population_2:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2020-06-01 to 2021-02-01 by month" --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input*.feather

  generate_study_population_3:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2021-03-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/inpu*.csv.gz

  generate_study_definition_demographics_1:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2019-09-01 to 2020-05-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographics_*.csv.gz
  
  generate_study_definition_demographics_2:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2020-06-01 to 2021-02-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographics*.csv.gz

  generate_study_definition_demographics_3:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_demographics --index-date-range "2021-03-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_demographic*.csv.gz

  generate_study_population_region:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_region --index-date-range "2019-09-01 to 2021-09-01 by month" --output-format csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_region*.csv.gz


  generate_study_population_ethnicity:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_ethnicity --output-format feather
    outputs:
      highly_sensitive:
        cohort: output/input_ethnicity.feather
  
  generate_demographics:
    run: python:latest python analysis/demographics_summary.py
    needs:
      [
        generate_study_population_1,
        generate_study_population_2,
        generate_study_population_3,
        generate_study_population_ethnicity,
        generate_study_definition_demographics_1,
        generate_study_definition_demographics_2,
        generate_study_definition_demographics_3
      ]
    outputs:
      moderately_sensitive:
        demographics: output/demographics_summary*.csv


  # join_ethnicity_region:
  #   run: python:latest python analysis/join_ethnicity_region.py
  #   needs:
  #     [
  #       generate_study_population_1,
  #       generate_study_population_2,
  #       generate_study_population_3,
  #       generate_study_population_ethnicity,
  #     ]
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inp*.feather

  filter_population:
    run: python:latest python analysis/filter_population.py
    needs: [generate_study_population_1, generate_study_population_2, generate_study_population_3]
    outputs:
      highly_sensitive:
        cohort: output/input_filtered_*.feather

  calculate_numerators:
    run: python:latest python analysis/calculate_numerators.py
    needs: [filter_population]
    outputs:
      highly_sensitive:
        cohort: output/indicator_e_f_*.feather

  # calculate_composite_indicators:
  #   run: python:latest python analysis/composite_indicators.py
  #   needs: [calculate_numerators, filter_population]
  #   outputs:
  #     moderately_sensitive:
  #       counts: output/*_composite_measure.csv

  generate_measures:
    run: cohortextractor:latest generate_measures --study-definition study_definition --output-dir=output
    needs: [filter_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measure_*_rate.csv

  generate_measures_additional:
    run: python:latest python analysis/calculate_measures.py
    needs: [calculate_numerators, filter_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measure*_rate.csv
       

  generate_summary_counts:
    run: python:latest python analysis/summary_statistics.py
    needs:
      [
        filter_population,
        generate_measures,
        generate_measures_additional,
        calculate_numerators,
      ]
    outputs:
      moderately_sensitive:
        patient_count: output/patient_count_*.json
        practice_count: output/practice_count_*.json
        summary: output/indicator_summary_statistics_*.json

  produce_stripped_measures:
    run: python:latest python analysis/stripped_measures.py
    needs:
      [
        generate_measures,
        generate_measures_additional
      ]
    outputs:
      moderately_sensitive:
        measures: output/measure_stripped_*.csv

  generate_plots:
    run: python:latest python analysis/plot_measures.py
    needs:
      [
        generate_measures,
        generate_measures_additional
      ]
    outputs:
      moderately_sensitive:
        counts: output/figures/plot_*.jpeg
        combined: output/figures/combined_plot_*.png
        medians: output/medians.json

  generate_notebook:
    run: jupyter:latest jupyter nbconvert /workspace/analysis/report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
    needs: [generate_plots, generate_summary_counts]
    outputs:
      moderately_sensitive:
        notebook: output/report.html

  # generate_dem_notebook:
  #   run: jupyter:latest jupyter nbconvert /workspace/analysis/demographic_report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
  #   needs: [generate_plots]
  #   outputs:
  #     moderately_sensitive:
  #       notebook: output/demographic_report.html

  # plot_Q1_comparisons:
  #   run: r:latest analysis/generate_demographic_slope_plot.R
  #   needs: [generate_plots]
  #   outputs:
  #     moderately_sensitive:
  #       plots: output/figures/SLOPE_*.png

  # run_tests:
  #   run: python:latest python -m pytest --junit-xml=output/pytest.xml --verbose
  #   outputs:
  #     moderately_sensitive:
  #       log: output/pytest.xml

  # test_population:
  #   run: python:latest python analysis/test_population.py
  #   needs: [filter_population]
  #   outputs:
  #     moderately_sensitive:
  #       counts: output/population_counts.csv
  #       count: output/patient_count_check.json

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:00:17

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

Job information

Status
Failed
Backend
EMIS
Workspace
pincer-measures-emis
Requested by
Louis Fisher
Branch
emis
Force run dependencies
No
Git commit hash
fe8296b
Requested actions
  • generate_study_population_region

Code comparison

Compare the code used in this Job Request