07. Analyse Trial Results

Retrieve Output IDs

To access trial results, first retrieve the available output IDs.

import json
import jinko_helpers as jinko

# Retrieving the output IDs
response = jinko.make_request(
    f"/core/v2/trial_manager/trial/{trial_core_item_id}/snapshots/{trial_snapshot_id}/output_ids",
    method="GET",
)
responseSummary = json.loads(response.content.decode("utf-8"))
print("Available time series:", responseSummary)

The response provides a list of available time series data points.

Retrieve Time Series Data

Once you have identified the output IDs, you can request time series data.

import jinko_helpers as jinko
import zipfile
import io

TIME_SERIES_IDS = ["x"]

try:
    print("Retrieving time series data...")
    response = jinko.make_request(
        "/core/v2/result_manager/timeseries_summary",
        method="POST",
        json={
            "select": TIME_SERIES_IDS,
            "trialId": {
                "coreItemId": trial_core_item_id,
                "snapshotId": trial_snapshot_id,
            },
        },
    )
    
    if response.status_code == 200:
        print("Time series data retrieved successfully.")
        archive = zipfile.ZipFile(io.BytesIO(response.content))
        filename = archive.namelist()[0]
        print(f"Extracted time series file: {filename}")
        csvTimeSeries = archive.read(filename).decode("utf-8")
    else:
        print(f"Failed to retrieve time series data: {response.status_code} - {response.reason}")
        response.raise_for_status()
except Exception as e:
    print(f"Error during time series retrieval or processing: {e}")
    raise

This request returns a compressed CSV file containing the time series data.

Visualizing Time Series Data

Once extracted, the data can be loaded into a Pandas DataFrame for analysis and visualization.

import pandas as pd
import matplotlib.pyplot as plt

# Convert CSV content to DataFrame
dfTimeSeries = pd.read_csv(io.StringIO(csvTimeSeries))

# Filter data for a single patient
unique_patient_ids = dfTimeSeries["Patient Id"].unique()
patient_data = dfTimeSeries[dfTimeSeries["Patient Id"] == unique_patient_ids[0]]

# Plot each arm separately
for arm, group in patient_data.groupby("Arm"):
    plt.plot(group["Time"], group["Value"], marker="o", linestyle="-", label=arm)

# Customize the plot
plt.title("Time Series of Selected Output")
plt.xlabel("Time (seconds)")
plt.ylabel("Value")
plt.legend(title="Arm")
plt.show()

Summary

In this module, you:

• Retrieved the available output IDs for a trial.
• Requested and extracted time series data.
• Processed and visualized the results using Python and Matplotlib.

Next, you can explore more advanced analyses, such as aggregating results across patients or applying statistical models to the data.