protocol-bicorder/analysis/WORKFLOW.md

Protocol Bicorder Analysis Workflow

This directory contains scripts for analyzing protocols using the Protocol Bicorder framework with LLM assistance.

The scripts automatically draw the gradients from the current state of the bicorder.json file.

Scripts

Diagnostic data generation (LLM-based)

1. scripts/bicorder_batch.py - [RECOMMENDED] Process entire CSV with one command
2. scripts/bicorder_analyze.py - Prepares CSV with gradient columns
3. scripts/bicorder_query.py - Queries LLM for each gradient value and updates CSV (each query is a new chat)

Manual / JSON-based readings

4. scripts/json_to_csv.py - Convert a directory of individual bicorder JSON reading files into a readings.csv
5. scripts/sync_readings.sh - Sync a readings dataset from a remote git repository, then regenerate CSV and run analysis (see below)

Analysis

6. scripts/multivariate_analysis.py - Run clustering, PCA, correlation, and feature importance analysis on a readings CSV
7. scripts/lda_visualization.py - Generate LDA cluster separation plot and projection data
8. scripts/classify_readings.py - Apply the synthetic-trained LDA classifier to all readings; saves analysis/classifications.csv
9. scripts/visualize_clusters.py - Additional cluster visualizations
10. scripts/export_model_for_js.py - Export trained model to bicorder_model.json (read by bicorder-app at build time)

Syncing a manual readings dataset

If the dataset has a .sync_source file (e.g., data/manual_20260320/), one command handles everything:

scripts/sync_readings.sh data/manual_20260320

This fetches new JSON files from the remote repo, regenerates readings.csv, runs multivariate analysis (with --min-coverage 0.8 to handle shortform readings), generates the LDA visualization, and saves cluster classifications to analysis/classifications.csv.

Running analysis on any readings CSV

# Full analysis pipeline
python3 scripts/multivariate_analysis.py data/manual_20260320/readings.csv \
  --min-coverage 0.8 \
  --analyses clustering pca correlation importance

# LDA visualization (cluster separation plot)
python3 scripts/lda_visualization.py data/manual_20260320/readings.csv

# Classify all readings (uses synthetic dataset as training data by default)
python3 scripts/classify_readings.py data/manual_20260320/readings.csv

Use --min-coverage (0.0–1.0) to drop dimension columns below the given coverage fraction before analysis. This is important for datasets with many shortform readings where most dimensions are sparsely filled.

Converting JSON reading files to CSV

If you have a directory of individual bicorder JSON reading files:

python3 scripts/json_to_csv.py data/manual_20260320/json/ \
  -o data/manual_20260320/readings.csv

---

Quick Start (Recommended, LLM-based)

Process All Protocols with One Command

python3 scripts/bicorder_batch.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv

This will:

1. Create the analysis CSV with gradient columns
2. For each protocol row, query all gradients (each query is a new chat with full protocol context)
3. Update the CSV automatically with the results
4. Show progress and summary

Common Options

# Process only rows 1-5 (useful for testing)
python3 scripts/bicorder_batch.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv --start 1 --end 5

# Use specific LLM model
python3 scripts/bicorder_batch.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv -m mistral

# Add analyst metadata
python3 scripts/bicorder_batch.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv \
  -a "Your Name" -s "Your analytical standpoint"

---

Manual Workflow (Advanced)

Step 1: Prepare the Analysis CSV

Create a CSV with empty gradient columns:

python3 scripts/bicorder_analyze.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv

Optional: Add analyst metadata:

python3 scripts/bicorder_analyze.py data/synthetic_20251116/protocols_edited.csv -o analysis_output.csv \
  -a "Your Name" -s "Your analytical standpoint"

Step 2: Query Gradients for a Protocol Row

Query all gradients for a specific protocol:

python3 scripts/bicorder_query.py analysis_output.csv 1

• Replace 1 with the row number you want to analyze
• Each gradient is queried in a new chat with full protocol context
• Each response is automatically parsed and written to the CSV
• Progress is shown for each gradient

Optional: Specify a model:

python3 scripts/bicorder_query.py analysis_output.csv 1 -m mistral

Step 3: Repeat for All Protocols

For each protocol in your CSV:

python3 scripts/bicorder_query.py analysis_output.csv 1
python3 scripts/bicorder_query.py analysis_output.csv 2
python3 scripts/bicorder_query.py analysis_output.csv 3
# ... and so on

# OR: Use scripts/bicorder_batch.py to automate all of this!

Architecture

How It Works

Each gradient query is sent to the LLM as a new, independent chat. Every query includes:

• The protocol descriptor (name)
• The protocol description
• The gradient definition (left term, right term, and their descriptions)
• Instructions to rate 1-9

This approach:

• Simplifies the code - No conversation state management
• Prevents bias - Each evaluation is independent, not influenced by previous responses
• Enables parallelization - Queries could theoretically run concurrently
• Makes debugging easier - Each query/response pair is self-contained

Tips

Dry Run Mode

Test prompts without calling the LLM:

python3 scripts/bicorder_query.py analysis_output.csv 1 --dry-run

This shows you exactly what prompt will be sent for each gradient, including the full protocol context.

Check Your Progress

View completed values:

python3 -c "
import csv
with open('analysis_output.csv') as f:
    reader = csv.DictReader(f)
    for i, row in enumerate(reader, 1):
        empty = sum(1 for k, v in row.items() if 'vs' in k and not v)
        print(f'Row {i}: {empty}/23 gradients empty')
"

Batch Processing

Use the scripts/bicorder_batch.py script (see Quick Start section above) for processing multiple protocols.