Which patient is going to survive longer? Another guide to using techtonique dot net’s API (with R + Python + the command line) for survival analysis
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In today’s post, we’ll see how to use rush and the probabilistic survival analysis API provided by techtonique.net (along with R and Python) to plot survival curves . Note that the web app also contains a page for plotting these curves, in 1 click. You can also read this post for more Python examples.
First, you’d need to install rush. Here is how I did it:
cd /Users/t/Documents/Python_Packages git clone https://github.com/jeroenjanssens/rush.git export PATH="/Users/t/Documents/Python_Packages/rush/exec:$PATH" source ~/.zshrc # or source ~/.bashrc rush --help # check if rush is installed
Now, download and save the following script in your current directory (note that there’s nothing malicious in it). Replace AUTH_TOKEN below by a token that can be found at techtonique.net/token:
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| #!/bin/bash | |
| # first, install jq: on macOS, brew install jq | |
| wget https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/survival/gbsg2_2.csv | |
| # === CONFIG === | |
| JSON_FILE="survival_curves.json" | |
| CSV_FILE="survival_curves.csv" | |
| PLOT_FILE="survival_plot.png" | |
| # Prompt for auth token | |
| read -p "Enter your Bearer token: " AUTH_TOKEN | |
| # === 1. Submit requests for multiple patients === | |
| python3 <<END | |
| import subprocess | |
| import sys | |
| try: | |
| # Use --user to install in the user's site-packages if direct install is restricted (PEP 668) | |
| subprocess.run([sys.executable, '-m', 'pip', 'install', 'requests'], check=True) | |
| except subprocess.CalledProcessError: | |
| print("Failed to install requests package") | |
| sys.exit(1) | |
| import requests | |
| import json | |
| headers = {'Authorization': 'Bearer $AUTH_TOKEN'} | |
| responses = [] | |
| for patient_id in range(4): | |
| params = {'method': 'RidgeCV', 'patient_id': str(patient_id)} | |
| files = {'file': ('gbsg2_2.csv', open('gbsg2_2.csv', 'rb'), 'text/csv')} | |
| print(f"Sending request for patient {patient_id}") | |
| response = requests.post('https://www.techtonique.net/survivalcurve', params=params, headers=headers, files=files) | |
| print(response) | |
| responses.append(response.json()) | |
| with open('$JSON_FILE', 'w') as f: | |
| json.dump(responses, f) | |
| END | |
| # === 2. Convert JSON to CSV === | |
| jq -r '.[] | .times as $t | .survival_probabilities as $s | range(0; $t|length) | "\(.),\($t[.]),\($s[.])"' "$JSON_FILE" > "$CSV_FILE" | |
| echo "patient_id,time,survival" > temp.csv && tail -n +2 "$CSV_FILE" >> temp.csv && mv temp.csv "$CSV_FILE" | |
| # === 3. Plot with rush === | |
| rush plot \ | |
| --x time \ | |
| --y survival \ | |
| --group patient_id \ | |
| --color patient_id \ | |
| --title "Patient Survival Curves" \ | |
| "$CSV_FILE" > "$PLOT_FILE" | |
| echo "✅ Final plot saved to: $PLOT_FILE" |
Then, at the command line, run:
./2025-05-31-survival.sh
The result plot can be found in your current directory as a PNG file.
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