Job request: 14902
- Organisation:
- Bennett Institute
- Workspace:
- winter-pressures
- ID:
- hvnufidt3o4os65y
This page shows the technical details of what happened when the authorised researcher Iain Dillingham 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:
-
kqehvfozdfzwbe3o
-
- Job identifier:
-
so5ycueavhlxa37l
-
- Job identifier:
-
4l3rizykcplbxmpu
Pipeline
Show project.yaml
version: "3.0"
expectations:
population_size: 5000
actions:
# Other data
# ----------
# Add actions for other data to this section. Prefix them with a suitable name; place
# scripts in a similarly named sub-directory of the analysis directory; write outputs
# to a similarly named sub-directory of the output directory.
#
# For example, let's call our other data "metrics". We would prefix our actions
# "metrics_"; we would place our scripts in analysis/metrics; we would write outputs
# to output/metrics.
# Appointments data
# -----------------
appointments_generate_dataset_sql:
run: >
sqlrunner:latest
analysis/appointments/dataset_query.sql
--output output/appointments/dataset_long.csv
--dummy-data-file analysis/appointments/dummy_dataset_long.csv
outputs:
highly_sensitive:
dataset: output/appointments/dataset_long.csv
# appointments_generate_dataset:
# run: >
# databuilder:v0
# generate-dataset
# analysis/appointments/dataset_definition.py
# --output output/appointments/dataset_wide.arrow
# outputs:
# highly_sensitive:
# dataset: output/appointments/dataset_wide.arrow
# appointments_get_freq_na_values:
# run: >
# python:latest
# python
# -m analysis.appointments.get_freq_na_values
# needs: [appointments_generate_dataset]
# outputs:
# moderately_sensitive:
# dataset: output/appointments/freq_na_values.csv
# appointments_reshape_dataset:
# run: >
# python:latest
# python
# -m analysis.appointments.reshape_dataset
# needs: [appointments_generate_dataset]
# outputs:
# highly_sensitive:
# dataset: output/appointments/dataset_long.arrow
appointments_generate_measure:
run: >
python:latest
python
-m analysis.appointments.generate_measure
needs: [appointments_generate_dataset_sql]
outputs:
moderately_sensitive:
measure: output/appointments/measure_median_lead_time_in_days.csv
appointments_generate_deciles_charts:
run: >
deciles-charts:v0.0.33
--input-files output/appointments/measure_*.csv
--output-dir output/appointments
config:
show_outer_percentiles: true
needs: [appointments_generate_measure]
outputs:
moderately_sensitive:
deciles_charts: output/appointments/deciles_chart_*.png
deciles_tables: output/appointments/deciles_table_*.csv
Timeline
-
Created:
-
Started:
-
Finished:
-
Runtime: 01:52:20
These timestamps are generated and stored using the UTC timezone on the TPP backend.
Job information
- Status
-
Succeeded
- Backend
- TPP
- Workspace
- winter-pressures
- Requested by
- Iain Dillingham
- Branch
- main
- Force run dependencies
- Yes
- Git commit hash
- 9b2c091
- Requested actions
-
-
run_all
-
Code comparison
Compare the code used in this Job Request