diff --git a/.gitignore b/.gitignore
index 4fe82f1..fcd11ea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,4 @@
 *.pdf
 *.json
-*.png
 __pycache__/
 testStatements/
diff --git a/CLAUDE.md b/CLAUDE.md
index e4f77b3..5b058a9 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -4,14 +4,11 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-Python tools for BAC Costa Rica credit card statement processing:
-- `bac_extract.py`: Extracts transactions from statement PDFs to JSON
-- `bac_analyze.py`: Analyzes JSON output with categorization and graphs
+Single-script Python tool that extracts credit card transactions from BAC Costa Rica statement PDFs. Parses sections B (purchases), D (other charges), and E (voluntary services) and outputs JSON.
 
 ## Dependencies
 
-- pdfplumber (>=0.10.0) - PDF extraction
-- matplotlib (>=3.5.0) - graphs (optional, only for bac_analyze.py --graph)
+- pdfplumber (>=0.10.0)
 
 ## Commands
 
@@ -19,22 +16,22 @@ Python tools for BAC Costa Rica credit card statement processing:
 # Run tests
 python testStatements/run_tests.py
 
-# Extract transactions from PDF
-python bac_extract.py statement.pdf --pretty
-python bac_extract.py statement.pdf -o output.json -v
+# Run extractor
+python bac_extract.py <pdf_file> [options]
 
-# Analyze transactions (supports multiple JSON files)
-python bac_analyze.py transactions.json
-python bac_analyze.py *.json --graph all
-python bac_analyze.py *.json --graph bar -o spending.png
-python bac_analyze.py *.json --categories my_categories.json
+# Examples
+python bac_extract.py EstadodeCuenta.pdf --pretty
+python bac_extract.py statement.pdf -o output.json -v
 ```
 
+Options:
+- `-o, --output`: Output JSON path (default: transactions.json)
+- `--pretty`: Pretty-print JSON
+- `-v, --verbose`: Enable debug logging
+
 ## Architecture
 
-### bac_extract.py
-
-Extraction pipeline:
+The extraction pipeline:
 1. Validates PDF is a BAC statement (`is_bac_statement`)
 2. Iterates pages line-by-line, detecting section boundaries via `SECTIONS` dict patterns
 3. Parses transactions matching `TRANSACTION_PATTERN` regex
@@ -44,10 +41,7 @@ Key data structures:
 - `SECTIONS`: Maps section IDs (B/D/E) to start/end regex patterns and output keys
 - `SPANISH_MONTHS`: Spanish month abbreviations for date parsing
 
-### bac_analyze.py
-
-Analysis pipeline:
-1. Loads transactions from one or more JSON files (purchases only)
-2. Categorizes by matching description against patterns in `categories.json`
-3. Aggregates by category and month, keeping CRC/USD separate
-4. Outputs text summary and optional graphs (bar/pie/timeline/all)
+Key parsing functions:
+- `parse_spanish_date`: Converts "15-ENE-25" to "2025-01-15"
+- `parse_amount`: Handles "1,234.56" and trailing negatives "100.00-"
+- `matches_patterns`: Generic regex pattern matcher for section detection
diff --git a/README.md b/README.md
index 709c00d..a56332a 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,13 @@
-# BAC Statement Tools
+# BAC Statement Extractor
 
-Tools for processing BAC Costa Rica credit card statement PDFs.
+Extracts credit card transactions from BAC Costa Rica statement PDFs. Parses sections B (purchases), D (other charges), and E (voluntary services) and outputs JSON.
 
 ## Dependencies
 
 - Python 3.10+
 - pdfplumber (>=0.10.0)
-- matplotlib (>=3.5.0) - optional, for graphs
 
-## Extraction
-
-Extract transactions from statement PDFs to JSON.
+## Usage
 
 ```bash
 python bac_extract.py <pdf_file> [options]
@@ -27,56 +24,6 @@ python bac_extract.py statement.pdf --pretty
 python bac_extract.py statement.pdf -o output.json -v
 ```
 
-## Analysis
-
-Analyze extracted transactions with category breakdowns and graphs.
-
-```bash
-python bac_analyze.py <json_files...> [options]
-```
-
-**Options:**
-- `--graph {bar,pie,timeline,all}`: Generate graph(s)
-- `-o, --output`: Output file for graph (default: spending_<type>.png)
-- `--show`: Display graph interactively
-- `--categories`: Custom categories file (default: categories.json)
-
-**Examples:**
-```bash
-# Text summary
-python bac_analyze.py transactions.json
-
-# Analyze multiple statements
-python bac_analyze.py *.json
-
-# Generate all graphs
-python bac_analyze.py *.json --graph all
-
-# Generate bar chart with custom output
-python bac_analyze.py *.json --graph bar -o spending.png
-
-# Use custom categories
-python bac_analyze.py *.json --categories my_categories.json
-```
-
-## Categories
-
-Create a `categories.json` file to customize spending categories. Each category maps to a list of merchant name patterns (case-insensitive substring match).
-
-```json
-{
-  "Groceries": ["SUPERMARKET", "WALMART", "FRESH MARKET"],
-  "Gas": ["SERVICENTRO", "DELTA", "SHELL"],
-  "Restaurants": ["RESTAURANT", "CAFE", "PIZZA", "SUSHI"],
-  "Transportation": ["UBER", "TAXI", "PARKING"],
-  "Entertainment": ["CINEMA", "NETFLIX", "STEAM"],
-  "Utilities": ["ELECTRIC", "WATER", "INTERNET"],
-  "Subscriptions": ["SPOTIFY", "YOUTUBE", "CHATGPT"]
-}
-```
-
-Transactions not matching any pattern are categorized as "Other".
-
 ## Output Format
 
 ```json
diff --git a/bac_analyze.py b/bac_analyze.py
deleted file mode 100755
index c3808c0..0000000
--- a/bac_analyze.py
+++ /dev/null
@@ -1,251 +0,0 @@
-#!/usr/bin/env python3
-"""
-BAC Spending Analysis Tool
-
-Analyzes transaction JSON output from bac_extract.py.
-Provides spending categorization and visualization.
-"""
-
-import argparse
-import json
-import sys
-from collections import defaultdict
-from pathlib import Path
-
-try:
-    import matplotlib.pyplot as plt
-    HAS_MATPLOTLIB = True
-except ImportError:
-    HAS_MATPLOTLIB = False
-
-
-def load_transactions(json_files: list[Path]) -> list[dict]:
-    """Load and merge transactions from multiple JSON files."""
-    transactions = []
-    for path in json_files:
-        with open(path, encoding="utf-8") as f:
-            data = json.load(f)
-        # Only include purchases, skip other_charges and voluntary_services
-        transactions.extend(data.get("purchases", []))
-    return transactions
-
-
-def load_categories(path: Path) -> dict[str, list[str]]:
-    """Load category patterns from JSON file."""
-    with open(path, encoding="utf-8") as f:
-        return json.load(f)
-
-
-def categorize(description: str, categories: dict[str, list[str]]) -> str:
-    """Return category for a transaction description."""
-    desc_upper = description.upper()
-    for category, patterns in categories.items():
-        for pattern in patterns:
-            if pattern.upper() in desc_upper:
-                return category
-    return "Other"
-
-
-def aggregate_by_category(
-    transactions: list[dict], categories: dict[str, list[str]]
-) -> dict[str, dict[str, float]]:
-    """Sum spending per category, separate CRC/USD."""
-    result = defaultdict(lambda: {"crc": 0.0, "usd": 0.0})
-    for txn in transactions:
-        cat = categorize(txn["description"], categories)
-        if txn["amount_crc"]:
-            result[cat]["crc"] += txn["amount_crc"]
-        if txn["amount_usd"]:
-            result[cat]["usd"] += txn["amount_usd"]
-    return dict(result)
-
-
-def aggregate_by_month(transactions: list[dict]) -> dict[str, dict[str, float]]:
-    """Sum spending per month (YYYY-MM), separate CRC/USD."""
-    result = defaultdict(lambda: {"crc": 0.0, "usd": 0.0})
-    for txn in transactions:
-        month = txn["date"][:7]  # YYYY-MM
-        if txn["amount_crc"]:
-            result[month]["crc"] += txn["amount_crc"]
-        if txn["amount_usd"]:
-            result[month]["usd"] += txn["amount_usd"]
-    return dict(result)
-
-
-def print_summary(by_category: dict, by_month: dict):
-    """Print text summary to stdout."""
-    print("=== Spending by Category ===")
-
-    # Sort by CRC amount descending
-    sorted_cats = sorted(by_category.items(), key=lambda x: x[1]["crc"], reverse=True)
-    total_crc = 0.0
-    total_usd = 0.0
-
-    for cat, amounts in sorted_cats:
-        crc, usd = amounts["crc"], amounts["usd"]
-        total_crc += crc
-        total_usd += usd
-        print(f"{cat:20} CRC {crc:>12,.2f}    USD {usd:>8,.2f}")
-
-    print("-" * 50)
-    print(f"{'Total':20} CRC {total_crc:>12,.2f}    USD {total_usd:>8,.2f}")
-
-    print("\n=== Monthly Spending ===")
-    for month in sorted(by_month.keys()):
-        amounts = by_month[month]
-        print(f"{month}:             CRC {amounts['crc']:>12,.2f}    USD {amounts['usd']:>8,.2f}")
-
-
-def plot_bar(data: dict, output: Path, show: bool):
-    """Bar chart of category spending (CRC)."""
-    # Sort by amount descending
-    sorted_items = sorted(data.items(), key=lambda x: x[1]["crc"], reverse=True)
-    categories = [item[0] for item in sorted_items]
-    amounts = [item[1]["crc"] for item in sorted_items]
-
-    fig, ax = plt.subplots(figsize=(10, 6))
-    bars = ax.barh(categories, amounts, color="steelblue")
-    ax.set_xlabel("Amount (CRC)")
-    ax.set_title("Spending by Category")
-    ax.invert_yaxis()
-
-    # Add value labels
-    for bar, amount in zip(bars, amounts):
-        ax.text(bar.get_width() + max(amounts) * 0.01, bar.get_y() + bar.get_height() / 2,
-                f"{amount:,.0f}", va="center", fontsize=9)
-
-    plt.tight_layout()
-    plt.savefig(output, dpi=150)
-    print(f"Saved bar chart to {output}")
-
-    if show:
-        plt.show()
-    plt.close()
-
-
-def plot_pie(data: dict, output: Path, show: bool):
-    """Pie chart of category distribution (CRC)."""
-    # Filter out zero/negative and sort
-    filtered = {k: v["crc"] for k, v in data.items() if v["crc"] > 0}
-    sorted_items = sorted(filtered.items(), key=lambda x: x[1], reverse=True)
-
-    categories = [item[0] for item in sorted_items]
-    amounts = [item[1] for item in sorted_items]
-
-    fig, ax = plt.subplots(figsize=(10, 8))
-    wedges, texts, autotexts = ax.pie(
-        amounts, labels=categories, autopct="%1.1f%%",
-        startangle=90, pctdistance=0.75
-    )
-    ax.set_title("Spending Distribution by Category (CRC)")
-
-    plt.tight_layout()
-    plt.savefig(output, dpi=150)
-    print(f"Saved pie chart to {output}")
-
-    if show:
-        plt.show()
-    plt.close()
-
-
-def plot_timeline(data: dict, output: Path, show: bool):
-    """Line chart of monthly spending (CRC)."""
-    months = sorted(data.keys())
-    amounts = [data[m]["crc"] for m in months]
-
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.plot(months, amounts, marker="o", linewidth=2, markersize=8, color="steelblue")
-    ax.fill_between(months, amounts, alpha=0.3, color="steelblue")
-
-    ax.set_xlabel("Month")
-    ax.set_ylabel("Amount (CRC)")
-    ax.set_title("Monthly Spending")
-    ax.tick_params(axis="x", rotation=45)
-
-    # Add value labels
-    for month, amount in zip(months, amounts):
-        ax.annotate(f"{amount:,.0f}", (month, amount),
-                    textcoords="offset points", xytext=(0, 10),
-                    ha="center", fontsize=9)
-
-    plt.tight_layout()
-    plt.savefig(output, dpi=150)
-    print(f"Saved timeline chart to {output}")
-
-    if show:
-        plt.show()
-    plt.close()
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Analyze spending from BAC transaction JSON files"
-    )
-    parser.add_argument(
-        "json_files", type=Path, nargs="+",
-        help="JSON files from bac_extract.py"
-    )
-    parser.add_argument(
-        "--graph", choices=["bar", "pie", "timeline", "all"],
-        help="Generate graph type (use 'all' for all graphs)"
-    )
-    parser.add_argument(
-        "-o", "--output", type=Path,
-        help="Output file for graph (default: spending_<type>.png)"
-    )
-    parser.add_argument(
-        "--show", action="store_true",
-        help="Display graph interactively"
-    )
-    parser.add_argument(
-        "--categories", type=Path, default=Path("categories.json"),
-        help="Custom categories file (default: categories.json)"
-    )
-    args = parser.parse_args()
-
-    # Validate input files
-    for path in args.json_files:
-        if not path.exists():
-            sys.exit(f"Error: File not found: {path}")
-
-    # Check matplotlib early if graph requested
-    if args.graph and not HAS_MATPLOTLIB:
-        sys.exit("Error: matplotlib is required for graphs. Install with: pip install matplotlib")
-
-    # Load categories
-    if not args.categories.exists():
-        sys.exit(f"Error: Categories file not found: {args.categories}")
-    categories = load_categories(args.categories)
-
-    # Load transactions
-    transactions = load_transactions(args.json_files)
-    if not transactions:
-        sys.exit("Error: No transactions found in input files")
-
-    # Aggregate data
-    by_category = aggregate_by_category(transactions, categories)
-    by_month = aggregate_by_month(transactions)
-
-    # Print summary
-    print_summary(by_category, by_month)
-
-    # Generate graph if requested
-    if args.graph:
-        if args.graph == "all":
-            prefix = args.output.stem if args.output else "spending"
-            suffix = args.output.suffix if args.output else ".png"
-            plot_bar(by_category, Path(f"{prefix}_bar{suffix}"), args.show)
-            plot_pie(by_category, Path(f"{prefix}_pie{suffix}"), args.show)
-            plot_timeline(by_month, Path(f"{prefix}_timeline{suffix}"), args.show)
-        else:
-            output = args.output or Path(f"spending_{args.graph}.png")
-            if args.graph == "bar":
-                plot_bar(by_category, output, args.show)
-            elif args.graph == "pie":
-                plot_pie(by_category, output, args.show)
-            elif args.graph == "timeline":
-                plot_timeline(by_month, output, args.show)
-
-
-if __name__ == "__main__":
-    main()