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
-
- Job identifier:
-
zihpdvik2o4l6wvn
-
- Job identifier:
-
7lcjjf2odgeyfx64
-
- Job identifier:
-
eskpm2ce5kqzsrt5
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