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Automate Machine Learning Tasks

MCP server for automated machine learning on CSV data: analysis, preprocessing, model selection, evaluation, hyperparameter tuning and prediction.

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Works with githubpandaspyarrowxgboost
Emircan Güneş
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88
Spark score
out of 100
Status Verified Official
Updated Jul 2025
Fresher alternatives ↓
Source checked Oct 14, 2026
Version 1.0.0
Models
claude
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Why it matters

Streamline your machine learning projects with an intelligent platform for data analysis, preprocessing, model selection, and hyperparameter tuning.

Outcomes

What it gets done

01

Analyze dataset statistics and identify missing values.

02

Automate data preprocessing, including outlier handling and feature encoding.

03

Select, train, and evaluate multiple machine learning models.

04

Tune hyperparameters for optimal model performance.

Source

Get it from source

Spark does not host a copy of it.

Open source

Reports

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Capabilities

Tools your agent gets

information_about_data

Provides detailed information about the data including statistics and missing values

reading_csv

Reads a CSV file

visualize_correlation_num

Visualizes correlation matrix for numerical columns

visualize_correlation_cat

Visualizes correlation matrix for categorical columns

visualize_correlation_final

Visualizes correlation matrix after preprocessing

visualize_outliers

Visualizes outliers in the data

visualize_outliers_final

Visualizes outliers after preprocessing

preprocessing_data

Preprocesses data by removing outliers and filling missing values

+7 tools

Overview

AutoML MCP Server

This is an automated machine learning platform exposed through Model Context Protocol tools. It provides data analysis, preprocessing, model selection, evaluation, hyperparameter tuning and prediction for regression and classification problems on CSV files. Use it when an assistant in Claude Desktop should explore a tabular CSV dataset, prepare it, compare several models and tune the best one. It reads CSV files from a data folder you configure and needs Python 3.8 or higher.

What it does

This is an intelligent automated machine learning platform that provides comprehensive data analysis, preprocessing, model selection and hyperparameter tuning through MCP tools.

Data analysis and exploration covers dataset statistics (shape, memory usage, data types and missing values), efficient CSV reading with pandas and pyarrow support, correlation matrices for numerical and categorical variables, and outlier detection and visualization. Preprocessing handles missing values, encodes categorical variables and scales numerical features, prepares features for both regression and classification problems, and checks for duplicates and data quality issues.

Supported algorithms are, for regression, Linear Regression, Ridge, Lasso, ElasticNet, Random Forest, XGBoost, SVR, KNN and CatBoost, and for classification, Logistic Regression, Ridge Classifier, Random Forest, XGBoost, SVM, KNN, Decision Tree, Naive Bayes and CatBoost. Regression is scored with R-squared, MAE and MSE, and classification with accuracy and F1-score, with a confusion matrix visualization for classification and side-by-side model comparison. Hyperparameter tuning uses advanced search algorithms, with a choice of scoring metric and control over the number of optimization trials.

When to use - and when NOT to

Use it for tabular problems delivered as CSV files, for regression or classification. Sample datasets from Kaggle are included for testing: heart disease, salaries, calories, personality, digital behavior, lifestyle, mobiles, habits, sleep, cost, Ford cars, AI and cat data, plus train and test files.

The server reads CSV files from a data directory that you set: update the path variable in utils/read_csv_file.py to point at your own project's data folder. Most tools take a file_name and a target_column, and where relevant a problem_type (regression or classification); test_external_data takes main_file_name and test_file_name.

Capabilities

Tools by group:

  • Data analysis: information_about_data, reading_csv, visualize_correlation_num, visualize_correlation_cat, visualize_correlation_final (after preprocessing) and visualize_outliers, visualize_outliers_final (after preprocessing).
  • Preprocessing: preprocessing_data removes outliers and fills nulls, and prepare_data(file_name, target_column, problem_type) does encoding and scaling.
  • Training and evaluation: models(problem_type, file_name, target_column) selects and evaluates models, visualize_accuracy_matrix shows the confusion matrix, and best_model_hyperparameter tunes the best model with n_trials, scoring and random_state.
  • Prediction and insight: test_external_data tests an external file with the best model and returns predictions, predict_value predicts the target for a new input, and feature_importance_analysis analyzes feature importance using XGBoost.

A typical run: analyze the data, preprocess it, prepare features for classification, train and evaluate models, visualize the confusion matrix, then tune the best model, for example a Random Forest classifier with 100 trials and accuracy scoring. Set a random seed for reproducible results. New algorithms can be added in utils/model_selection.py, preprocessing steps in utils/preprocessing.py and visualizations in utils/visualize_data.py.

How to install

It runs as an MCP server for Claude Desktop.

git clone https://github.com/emircansoftware/AutoML.git
cd AutoML
pip install -r requirements.txt
pip install uv

Then add an AutoML entry to claude_desktop_config.json that runs uv with the arguments --directory (the path to your AutoML folder), run and main.py, adjust the paths for your system, and start it from Claude Desktop.

Version requirements are mcp[cli] 1.9.4 or newer, pandas 2.3.0, pyarrow 20.0.0, numpy 2.3.1, scikit-learn 1.3.0, xgboost 2.0.0 and lightgbm 4.3.0 (each or newer), with catboost for CatBoost models; Python 3.8 or higher and pip or uv are needed. The server starts from main.py (or from server import mcp followed by mcp.run()). The server is MIT licensed.

Source README

Auto ML - Automated Machine Learning Platform

Python
License
MCP

An intelligent automated machine learning platform that provides comprehensive data analysis, preprocessing, model selection, and hyperparameter tuning capabilities through Model Context Protocol (MCP) tools.

🚀 Features

📊 Data Analysis & Exploration

  • Data Information: Get comprehensive dataset statistics including shape, memory usage, data types, and missing values
  • CSV Reading: Efficient CSV file reading with pandas and pyarrow support
  • Correlation Analysis: Visualize correlation matrices for numerical and categorical variables
  • Outlier Detection: Identify and visualize outliers in your datasets

🔧 Data Preprocessing

  • Automated Preprocessing: Handle missing values, encode categorical variables, and scale numerical features
  • Feature Engineering: Prepare features for both regression and classification problems
  • Data Validation: Check for duplicates and data quality issues

🤖 Machine Learning Models

  • Multiple Algorithms: Support for various ML algorithms including:
    • Regression: Linear Regression, Ridge, Lasso, ElasticNet, Random Forest, XGBoost, SVR, KNN, CatBoost
    • Classification: Logistic Regression, Ridge Classifier, Random Forest, XGBoost, SVM, KNN, Decision Tree, Naive Bayes, CatBoost

📈 Model Evaluation & Visualization

  • Performance Metrics:
    • Regression: R², MAE, MSE
    • Classification: Accuracy, F1-Score
  • Confusion Matrix Visualization: For classification problems
  • Model Comparison: Compare multiple models side-by-side

⚙️ Hyperparameter Tuning

  • Automated Tuning: Optimize model hyperparameters using advanced search algorithms
  • Customizable Scoring: Choose from various evaluation metrics
  • Trial Management: Control the number of optimization trials

📁 Project Structure

AutoML/
├── data/                   # Sample datasets
│   ├── Ai.csv
│   ├── Calories.csv
│   ├── Cost.csv
│   ├── Digital.csv
│   ├── Electricity.csv
│   ├── ford.csv
│   ├── Habits.csv
│   ├── heart.csv
│   ├── Lifestyle.csv
│   ├── Mobiles.csv
│   ├── Personality.csv
│   ├── Salaries.csv
│   ├── Shopper.csv
│   ├── Sleep.csv
│   ├── cat.csv
│   ├── test.csv
│   └── train.csv
├── tools/
│   └── all_tools.py       # MCP tool definitions
├── utils/
│   ├── before_model.py        # Feature preparation
│   ├── details.py             # Data information
│   ├── external_test.py       # External data test with XGBoost
│   ├── feature_importance.py  # Feature importance analysis
│   ├── hyperparameter.py      # Hyperparameter tuning
│   ├── model_selection.py     # Model selection and evaluation
│   ├── prediction.py          # Prediction utilities
│   ├── preprocessing.py       # Data preprocessing
│   ├── read_csv_file.py       # CSV reading utilities
│   └── visualize_data.py      # Visualization functions
├── main.py                # Application entry point
├── server.py              # MCP server configuration
├── requirements.txt       # Python dependencies
└── README.md             # This file

🛠️ Installation

Prerequisites

  • Python 3.8 or higher
  • pip or uv package manager

Setup

  1. Clone the repository

    git clone https://github.com/emircansoftware/AutoML.git
cd AutoML

  2. Install dependencies

    # Using pip
pip install -r requirements.txt
pip install uv

Using with Claude Desktop

1. Data Path Setting

In utils/read_csv_file.py, update the path variable to match your own project directory on your computer:

# Example:
path = r"C:\\YOUR\\PROJECT\\PATH\\AutoML\\data"

2. Claude Desktop Configuration

In Claude Desktop, add the following block to your claude_desktop_config.json file and adjust the paths to match your own system:

{
  "mcpServers": {
    "AutoML": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\YOUR\\PROJECT\\PATH\\AutoML",
        "run",
        "main.py"
      ]
    }
  }
}

You can now start your project from Claude Desktop.

📋 Dependencies

  • MCP Framework: mcp[cli]>=1.9.4 - Model Context Protocol for tool integration
  • Data Processing: pandas>=2.3.0, pyarrow>=20.0.0, numpy>=2.3.1
  • Machine Learning: scikit-learn>=1.3.0, xgboost>=2.0.0, lightgbm>=4.3.0
  • Additional ML: catboost (for CatBoost models)

🎯 Usage

Starting the MCP Server

from server import mcp

# Run the server
mcp.run()

Available Tools

The platform provides the following MCP tools:

Data Analysis Tools
  • information_about_data(file_name): Give detailed information about the data
  • reading_csv(file_name): Read the csv file
  • visualize_correlation_num(file_name): Visualize the correlation matrix for numerical columns
  • visualize_correlation_cat(file_name): Visualize the correlation matrix for categorical columns
  • visualize_correlation_final(file_name, target_column): Visualize the correlation matrix after preprocessing
  • visualize_outliers(file_name): Visualize outliers in the data
  • visualize_outliers_final(file_name, target_column): Visualize outliers after preprocessing
Preprocessing Tools
  • preprocessing_data(file_name, target_column): Preprocess the data (remove outliers, fill nulls, etc.)
  • prepare_data(file_name, target_column, problem_type): Prepare the data for models (encoding, scaling, etc.)
Model Training & Evaluation
  • models(problem_type, file_name, target_column): Select and evaluate models based on problem type
  • visualize_accuracy_matrix(file_name, target_column, problem_type): Visualize the confusion matrix for predictions
  • best_model_hyperparameter(model_name, file_name, target_column, problem_type, n_trials, scoring, random_state): Tune the hyperparameters of the best model
  • test_external_data(main_file_name, target_column, problem_type, test_file_name): Test external data with the best model and return predictions
  • predict_value(model_name, file_name, target_column, problem_type, n_trials, scoring, random_state, input): Predict the value of the target column for new input
  • feature_importance_analysis(file_name, target_column, problem_type): Analyze the feature importance of the data using XGBoost

Example Workflow

# 1. Analyze your data
info = information_about_data("data/heart.csv")

# 2. Preprocess the data
preprocessed = preprocessing_data("data/heart.csv", "target")

# 3. Prepare features for classification
features = prepare_data("data/heart.csv", "target", "classification")

# 4. Train and evaluate models
results = models("classification", "data/heart.csv", "target")

# 5. Visualize results
confusion_matrix = visualize_accuracy_matrix("data/heart.csv", "target", "classification")

# 6. Optimize best model
best_model = best_model_hyperparameter("RandomForestClassifier", "data/heart.csv", "target", "classification", 100, "accuracy", 42)

📊 Sample Datasets (All CSV datasets are from Kaggle.)

The project includes various sample datasets for testing:

  • heart.csv: Heart disease prediction dataset
  • Salaries.csv: Salary prediction dataset
  • Calories.csv: Calorie prediction dataset
  • Personality.csv: Personality analysis dataset
  • Digital.csv: Digital behavior dataset
  • Lifestyle.csv: Lifestyle analysis dataset
  • Mobiles.csv: Mobile phone dataset
  • Habits.csv: Habit analysis dataset
  • Sleep.csv: Sleep pattern dataset
  • Cost.csv: Cost analysis dataset
  • ford.csv: Ford car dataset
  • Ai.csv: AI-related dataset
  • cat.csv: Cat-related dataset

🔧 Configuration

Environment Variables

  • Set your preferred random seed for reproducible results
  • Configure MCP server settings in server.py

Customization

  • Add new ML algorithms in utils/model_selection.py
  • Extend preprocessing steps in utils/preprocessing.py
  • Create custom visualization functions in utils/visualize_data.py

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

📞 Support

If you encounter any issues or have questions:

  1. Check the Issues page
  2. Create a new issue with detailed information
  3. Contact the maintainers

data/heart.csv

Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,HeartDisease
40,M,ATA,140,289,0,Normal,172,N,0,Up,0
49,F,NAP,160,180,0,Normal,156,N,1,Flat,1
37,M,ATA,130,283,0,ST,98,N,0,Up,0
48,F,ASY,138,214,0,Normal,108,Y,1.5,Flat,1
54,M,NAP,150,195,0,Normal,122,N,0,Up,0
39,M,NAP,120,339,0,Normal,170,N,0,Up,0
45,F,ATA,130,237,0,Normal,170,N,0,Up,0
54,M,ATA,110,208,0,Normal,142,N,0,Up,0
37,M,ASY,140,207,0,Normal,130,Y,1.5,Flat,1
48,F,ATA,120,284,0,Normal,120,N,0,Up,0
37,F,NAP,130,211,0,Normal,142,N,0,Up,0
58,M,ATA,136,164,0,ST,99,Y,2,Flat,1
39,M,ATA,120,204,0,Normal,145,N,0,Up,0
49,M,ASY,140,234,0,Normal,140,Y,1,Flat,1
42,F,NAP,115,211,0,ST,137,N,0,Up,0
54,F,ATA,120,273,0,Normal,150,N,1.5,Flat,0
38,M,ASY,110,196,0,Normal,166,N,0,Flat,1
43,F,ATA,120,201,0,Normal,165,N,0,Up,0
60,M,ASY,100,248,0,Normal,125,N,1,Flat,1
36,M,ATA,120,267,0,Normal,160,N,3,Flat,1
43,F,TA,100,223,0,Normal,142,N,0,Up,0
44,M,ATA,120,184,0,Normal,142,N,1,Flat,0
49,F,ATA,124,201,0,Normal,164,N,0,Up,0
44,M,ATA,150,288,0,Normal,150,Y,3,Flat,1
40,M,NAP,130,215,0,Normal,138,N,0,Up,0
36,M,NAP,130,209,0,Normal,178,N,0,Up,0
53,M,ASY,124,260,0,ST,112,Y,3,Flat,0
52,M,ATA,120,284,0,Normal,118,N,0,Up,0
53,F,ATA,113,468,0,Normal,127,N,0,Up,0
51,M,ATA,125,188,0,Normal,145,N,0,Up,0
53,M,NAP,145,518,0,Normal,130,N,0,Flat,1
56,M,NAP,130,167,0,Normal,114,N,0,Up,0
54,M,ASY,125,224,0,Normal,122,N,2,Flat,1
41,M,ASY,130,172,0,ST,130,N,2,Flat,1
43,F,ATA,150,186,0,Normal,154,N,0,Up,0
32,M,ATA,125,254,0,Normal,155,N,0,Up,0
65,M,ASY,140,306,1,Normal,87,Y,1.5,Flat,1
41,F,ATA,110,250,0,ST,142,N,0,Up,0
48,F,ATA,120,177,1,ST,148,N,0,Up,0
48,F,ASY,150,227,0,Normal,130,Y,1,Flat,0
54,F,ATA,150,230,0,Normal,130,N,0,Up,0
54,F,NAP,130,294,0,ST,100,Y,0,Flat,1
35,M,ATA,150,264,0,Normal,168,N,0,Up,0
52,M,NAP,140,259,0,ST,170,N,0,Up,0
43,M,ASY,120,175,0,Normal,120,Y,1,Flat,1
59,M,NAP,130,318,0,Normal,120,Y,1,Flat,0
37,M,ASY,120,223,0,Normal,168,N,0,Up,0
50,M,ATA,140,216,0,Normal,170,N,0,Up,0
36,M,NAP,112,340,0,Normal,184,N,1,Flat,0
41,M,ASY,110,289,0,Normal,170,N,0,Flat,1
50,M,ASY,130,233,0,Normal,121,Y,2,Flat,1
47,F,ASY,120,205,0,Normal,98,Y,2,Flat,1
45,M,ATA,140,224,1,Normal,122,N,0,Up,0
41,F,ATA,130,245,0,Normal,150,N,0,Up,0
52,F,ASY,130,180,0,Normal,140,Y,1.5,Flat,0
51,F,ATA,160,194,0,Normal,170,N,0,Up,0
31,M,ASY,120,270,0,Normal,153,Y,1.5,Flat,1
58,M,NAP,130,213,0,ST,140,N,0,Flat,1
54,M,ASY,150,365,0,ST,134,N,1,Up,0
52,M,ASY,112,342,0,ST,96,Y,1,Flat,1
49,M,ATA,100,253,0,Normal,174,N,0,Up,0
43,F,NAP,150,254,0,Normal,175,N,0,Up,0
45,M,ASY,140,224,0,Normal,144,N,0,Up,0
46,M,ASY,120,277,0,Normal,125,Y,1,Flat,1
50,F,ATA,110,202,0,Normal,145,N,0,Up,0
37,F,ATA,120,260,0,Normal,130,N,0,Up,0
45,F,ASY,132,297,0,Normal,144,N,0,Up,0
32,M,ATA,110,225,0,Normal,184,N,0,Up,0
52,M,ASY,160,246,0,ST,82,Y,4,Flat,1
44,M,ASY,150,412,0,Normal,170,N,0,Up,0
57,M,ATA,140,265,0,ST,145,Y,1,Flat,1
44,M,ATA,130,215,0,Normal,135,N,0,Up,0
52,M,ASY,120,182,0,Normal,150,N,0,Flat,1
44,F,ASY,120,218,0,ST,115,N,0,Up,0
55,M,ASY,140,268,0,Normal,128,Y,1.5,Flat,1
46,M,NAP,150,163,0,Normal,116,N,0,Up,0
32,M,ASY,118,529,0,Normal,130,N,0,Flat,1
35,F,ASY,140,167,0,Normal,150,N,0,Up,0
52,M,ATA,140,100,0,Normal,138,Y,0,Up,0
49,M,ASY,130,206,0,Normal,170,N,0,Flat,1
55,M,NAP,110,277,0,Normal,160,N,0,Up,0
54,M,ATA,120,238,0,Normal,154,N,0,Up,0
63,M,ASY,150,223,0,Normal,115,N,0,Flat,1
52,M,ATA,160,196,0,Normal,165,N,0,Up,0
56,M,ASY,150,213,1,Normal,125,Y,1,Flat,1
66,M,ASY,140,139,0,Normal,94,Y,1,Flat,1
65,M,ASY,170,263,1,Normal,112,Y,2,Flat,1
53,F,ATA,140,216,0,Normal,142,Y,2,Flat,0
43,M,TA,120,291,0,ST,155,N,0,Flat,1
55,M,ASY,140,229,0,Normal,110,Y,0.5,Flat,0
49,F,ATA,110,208,0,Normal,160,N,0,Up,0
39,M,ASY,130,307,0,Normal,140,N,0,Up,0
52,F,ATA,120,210,0,Normal,148,N,0,Up,0
48,M,ASY,160,329,0,Normal,92,Y,1.5,Flat,1
39,F,NAP,110,182,0,ST,180,N,0,Up,0
58,M,ASY,130,263,0,Normal,140,Y,2,Flat,1
43,M,ATA,142,207,0,Normal,138,N,0,Up,0
39,M,NAP,160,147,1,Normal,160,N,0,Up,0
56,M,ASY,120,85,0,Normal,140,N,0,Up,0
41,M,ATA,125,269,0,Normal,144,N,0,Up,0
65,M,ASY,130,275,0,ST,115,Y,1,Flat,1
51,M,ASY,130,179,0,Normal,100,N,0,Up,0
40,F,ASY,150,392,0,Normal,130,N,2,Flat,1
40,M,ASY,120,466,1,Normal,152,Y,1,Flat,1
46,M,ASY,118,186,0,Normal,124,N,0,Flat,1
57,M,ATA,140,260,1,Normal,140,N,0,Up,0
48,F,ASY,120,254,0,ST,110,N,0,Up,0
34,M,ATA,150,214,0,ST,168,N,0,Up,0
50,M,ASY,140,129,0,Normal,135,N,0,Up,0
39,M,ATA,190,241,0,Normal,106,N,0,Up,0
59,F,ATA,130,188,0,Normal,124,N,1,Flat,0
57,M,ASY,150,255,0,Normal,92,Y,3,Flat,1
47,M,ASY,140,276,1,Normal,125,Y,0,Up,0
38,M,ATA,140,297,0,Normal,150,N,0,Up,0
49,F,NAP,130,207,0,ST,135,N,0,Up,0
33,F,ASY,100,246,0,Normal,150,Y,1,Flat,1
38,M,ASY,120,282,0,Normal,170,N,0,Flat,1
59,F,ASY,130,338,1,ST,130,Y,1.5,Flat,1
35,F,TA,120,160,0,ST,185,N,0,Up,0
34,M,TA,140,156,0,Normal,180,N,0,Flat,1
47,F,NAP,135,248,1,Normal,170,N,0,Flat,1
52,F,NAP,125,272,0,Normal,139,N,0,Up,0
46,M,ASY,110,240,0,ST,140,N,0,Up,0
58,F,ATA,180,393,0,Normal,110,Y,1,Flat,1
58,M,ATA,130,230,0,Normal,150,N,0,Up,0
54,M,ATA,120,246,0,Normal,110,N,0,Up,0
34,F,ATA,130,161,0,Normal,190,N,0,Up,0
48,F,ASY,108,163,0,Normal,175,N,2,Up,0
54,F,ATA,120,230,1,Normal,140,N,0,Up,0
42,M,NAP,120,228,0,Normal,152,Y,1.5,Flat,0
38,M,NAP,145,292,0,Normal,130,N,0,Up,0
46,M,ASY,110,202,0,Normal,150,Y,0,Flat,1
56,M,ASY,170,388,0,ST,122,Y,2,Flat,1
56,M,ASY,150,230,0,ST,124,Y,1.5,Flat,1
61,F,ASY,130,294,0,ST,120,Y,1,Flat,0
49,M,NAP,115,265,0,Normal,175,N,0,Flat,1
43,F,ATA,120,215,0,ST,175,N,0,Up,0
39,M,ATA,120,241,0,ST,146,N,2,Up,0
54,M,ASY,140,166,0,Normal,118,Y,0,Flat,1
43,M,ASY,150,247,0,Normal,130,Y,2,Flat,1
52,M,ASY,160,331,0,Normal,94,Y,2.5,Flat,1
50,M,ASY,140,341,0,ST,125,Y,2.5,Flat,1
47,M,ASY,160,291,0,ST,158,Y,3,Flat,1
53,M,ASY,140,243,0,Normal,155,N,0,Up,0
56,F,ATA,120,279,0,Normal,150,N,1,Flat,1
39,M,ASY,110,273,0,Normal,132,N,0,Up,0
42,M,ATA,120,198,0,Normal,155,N,0,Up,0
43,F,ATA,120,249,0,ST,176,N,0,Up,0
50,M,ATA,120,168,0,Normal,160,N,0,Up,0
54,M,ASY,130,603,1,Normal,125,Y,1,Flat,1
39,M,ATA,130,215,0,Normal,120,N,0,Up,0
48,M,ATA,100,159,0,Normal,100,N,0,Up,0
40,M,ATA,130,275,0,Normal,150,N,0,Up,0
55,M,ASY,120,270,0,Normal,140,N,0,Up,0
41,M,ATA,120,291,0,ST,160,N,0,Up,0
56,M,ASY,155,342,1,Normal,150,Y,3,Flat,1
38,M,ASY,110,190,0,Normal,150,Y,1,Flat,1
49,M,ASY,140,185,0,Normal,130,N,0,Up,0
44,M,ASY,130,290,0,Normal,100,Y,2,Flat,1
54,M,ATA,160,195,0,ST,130,N,1,Up,0
59,M,ASY,140,264,1,LVH,119,Y,0,Flat,1
49,M,ASY,128,212,0,Normal,96,Y,0,Flat,1
47,M,ATA,160,263,0,Normal,174,N,0,Up,0
42,M,ATA,120,196,0,Normal,150,N,0,Up,0
52,F,ATA,140,225,0,Normal,140,N,0,Up,0
46,M,TA,140,272,1,Normal,175,N,2,Flat,1
50,M,ASY,140,231,0,ST,140,Y,5,Flat,1
48,M,ATA,140,238,0,Normal,118,N,0,Up,0
58,M,ASY,135,222,0,Normal,100,N,0,Up,0
58,M,NAP,140,179,0,Normal,160,N,0,Up,0
29,M,ATA,120,243,0,Normal,160,N,0,Up,0
40,M,NAP,140,235,0,Normal,188,N,0,Up,0
53,M,ATA,140,320,0,Normal,162,N,0,Up,0
49,M,NAP,140,187,0,Normal,172,N,0,Up,0
52,M,ASY,140,266,0,Normal,134,Y,2,Flat,1
43,M,ASY,140,288,0,Normal,135,Y,2,Flat,1
54,M,ASY,140,216,0,Normal,105,N,1.5,Flat,1
59,M,ATA,140,287,0,Normal,150,N,0,Up,0
37,M,NAP,130,194,0,Normal,150,N,0,Up,0
46,F,ASY,130,238,0,Normal,90,N,0,Up,0
52,M,ASY,130,225,0,Normal,120,Y,2,Flat,1
51,M,ATA,130,224,0,Normal,150,N,0,Up,0
52,M,ASY,140,404,0,Normal,124,Y,2,Flat,1
46,M,ASY,110,238,0,ST,140,Y,1,Flat,0
54,F,ATA,160,312,0,Normal,130,N,0,Up,0
58,M,NAP,160,211,1,ST,92,N,0,Flat,1
58,M,ATA,130,251,0,Normal,110,N,0,Up,0
41,M,ASY,120,237,1,Normal,138,Y,1,Flat,1
50,F,ASY,120,328,0,Normal,110,Y,1,Flat,0
53,M,ASY,180,285,0,ST,120,Y,1.5,Flat,1
46,M,ASY,180,280,0,ST,120,N,0,Up,0
50,M,ATA,170,209,0,ST,116,N,0,Up,0
48,M,ATA,130,245,0,Normal,160,N,0,Up,0
45,M,NAP,135,192,0,Normal,110,N,0,Up,0
41,F,ATA,125,184,0,Normal,180,N,0,Up,0
62,F,TA,160,193,0,Normal,116,N,0,Up,0
49,M,ASY,120,297,0,Normal,132,N,1,Flat,0
42,M,ATA,150,268,0,Normal,136,N,0,Up,0
53,M,ASY,120,246,0,Normal,116,Y,0,Flat,1
57,F,TA,130,308,0,Normal,98,N,1,Flat,0
47,M,TA,110,249,0,Normal,150,N,0,Up,0
46,M,NAP,120,230,0,Normal,150,N,0,Up,0
42,M,NAP,160,147,0,Normal,146,N,0,Up,0
31,F,ATA,100,219,0,ST,150,N,0,Up,0
56,M,ATA,130,184,0,Normal,100,N,0,Up,0
50,M,ASY,150,215,0,Normal,140,Y,0,Up,0
35,M,ATA,120,308,0,LVH,180,N,0,Up,0
35,M,ATA,110,257,0,Normal,140,N,0,Flat,1
28,M,ATA,130,132,0,LVH,185,N,0,Up,0
54,M,ASY,125,216,0,Normal,140,N,0,Flat,1
48,M,ASY,106,263,1,Normal,110,N,0,Flat,1
50,F,NAP,140,288,0,Normal,140,Y,0,Flat,1
56,M,NAP,130,276,0,Normal,128,Y,1,Up,0
56,F,NAP,130,219,0,ST,164,N,0,Up,0
47,M,ASY,150,226,0,Normal,98,Y,1.5,Flat,1
30,F,TA,170,237,0,ST,170,N,0,Up,0
39,M,ASY,110,280,0,Normal,150,N,0,Flat,1
54,M,NAP,120,217,0,Normal,137,N,0,Up,0
55,M,ATA,140,196,0,Normal,150,N,0,Up,0
29,M,ATA,140,263,0,Normal,170,N,0,Up,0
46,M,ASY,130,222,0,Normal,112,N,0,Flat,1
51,F,ASY,160,303,0,Normal,150,Y,1,Flat,1
48,F,NAP,120,195,0,Normal,125,N,0,Up,0
33,M,NAP,120,298,0,Normal,185,N,0,Up,0
55,M,ATA,120,256,1,Normal,137,N,0,Up,0
50,M,ASY,145,264,0,Normal,150,N,0,Flat,1
53,M,NAP,120,195,0,Normal,140,N,0,Up,0
38,M,ASY,92,117,0,Normal,134,Y,2.5,Flat,1
41,M,ATA,120,295,0,Normal,170,N,0,Up,0
37,F,ASY,130,173,0,ST,184,N,0,Up,0
37,M,ASY,130,315,0,Normal,158,N,0,Up,0
40,M,NAP,130,281,0,Normal,167,N,0,Up,0
38,F,ATA,120,275,0,Normal,129,N,0,Up,0
41,M,ASY,112,250,0,Normal,142,N,0,Up,0
54,F,ATA,140,309,0,ST,140,N,0,Up,0
39,M,ATA,120,200,0,Normal,160,Y,1,Flat,0
41,M,ASY,120,336,0,Normal,118,Y,3,Flat,1
55,M,TA,140,295,0,Normal,136,N,0,Flat,1
48,M,ASY,160,355,0,Normal,99,Y,2,Flat,1
48,M,ASY,160,193,0,Normal,102,Y,3,Flat,1
55,M,ATA,145,326,0,Normal,155,N,0,Up,0
54,M,ASY,200,198,0,Normal,142,Y,2,Flat,1
55,M,ATA,160,292,1,Normal,143,Y,2,Flat,1
43,F,ATA,120,266,0,Normal,118,N,0,Up,0
48,M,ASY,160,268,0,Normal,103,Y,1,Flat,1
54,M,TA,120,171,0,Normal,137,N,2,Up,0
54,M,NAP,120,237,0,Normal,150,Y,1.5,Flat,1
48,M,ASY,122,275,1,ST,150,Y,2,Down,1
45,M,ASY,130,219,0,ST,130,Y,1,Flat,1
49,M,ASY,130,341,0,Normal,120,Y,1,Flat,1
44,M,ASY,135,491,0,Normal,135,N,0,Flat,1
48,M,ASY,120,260,0,Normal,115,N,2,Flat,1
61,M,ASY,125,292,0,ST,115,Y,0,Up,0
62,M,ATA,140,271,0,Normal,152,N,1,Up,0
55,M,ASY,145,248,0,Normal,96,Y,2,Flat,1
53,F,NAP,120,274,0,Normal,130,N,0,Up,0
55,F,ATA,130,394,0,LVH,150,N,0,Up,0
36,M,NAP,150,160,0,Normal,172,N,0,Up,0
51,F,NAP,150,200,0,Normal,120,N,0.5,Up,0
55,F,ATA,122,320,0,Normal,155,N,0,Up,0
46,M,ATA,140,275,0,Normal,165,Y,0,Up,0
54,F,ATA,120,221,0,Normal,138,N,1,Up,0
46,M,ASY,120,231,0,Normal,115,Y,0,Flat,1
59,M,ASY,130,126,0,Normal,125,N,0,Flat,1
47,M,NAP,140,193,0,Normal,145,Y,1,Flat,1
54,M,ATA,160,305,0,Normal,175,N,0,Up,0
52,M,ASY,130,298,0,Normal,110,Y,1,Flat,1
34,M,ATA,98,220,0,Normal,150,N,0,Up,0
54,M,ASY,130,242,0,Normal,91,Y,1,Flat,1
47,F,NAP,130,235,0,Normal,145,N,2,Flat,0
45,M,ASY,120,225,0,Normal,140,N,0,Up,0
32,F,ATA,105,198,0,Normal,165,N,0,Up,0
55,M,ASY,140,201,0,Normal,130,Y,3,Flat,1
55,M,NAP,120,220,0,LVH,134,N,0,Up,0
45,F,ATA,180,295,0,Normal,180,N,0,Up,0
59,M,NAP,180,213,0,Normal,100,N,0,Up,0
51,M,NAP,135,160,0,Normal,150,N,2,Flat,1
52,M,ASY,170,223,0,Normal,126,Y,1.5,Flat,1
57,F,ASY,180,347,0,ST,126,Y,0.8,Flat,0
54,F,ATA,130,253,0,ST,155,N,0,Up,0
60,M,NAP,120,246,0,LVH,135,N,0,Up,0
49,M,ASY,150,222,0,Normal,122,N,2,Flat,1
51,F,NAP,130,220,0,Normal,160,Y,2,Up,0
55,F,ATA,110,344,0,ST,160,N,0,Up,0
42,M,ASY,140,358,0,Normal,170,N,0,Up,0
51,F,NAP,110,190,0,Normal,120,N,0,Up,0
59,M,ASY,140,169,0,Normal,140,N,0,Up,0
53,M,ATA,120,181,0,Normal,132,N,0,Up,0
48,F,ATA,133,308,0,ST,156,N,2,Up,0
36,M,ATA,120,166,0,Normal,180,N,0,Up,0
48,M,NAP,110,211,0,Normal,138,N,0,Up,0
47,F,ATA,140,257,0,Normal,135,N,1,Up,0
53,M,ASY,130,182,0,Normal,148,N,0,Up,0
65,M,ASY,115,0,0,Normal,93,Y,0,Flat,1
32,M,TA,95,0,1,Normal,127,N,0.7,Up,1
61,M,ASY,105,0,1,Normal,110,Y,1.5,Up,1
50,M,ASY,145,0,1,Normal,139,Y,0.7,Flat,1
57,M,ASY,110,0,1,ST,131,Y,1.4,Up,1
51,M,ASY,110,0,1,Normal,92,N,0,Flat,1
47,M,ASY,110,0,1,ST,149,N,2.1,Up,1
60,M,ASY,160,0,1,Normal,149,N,0.4,Flat,1
55,M,ATA,140,0,0,ST,150,N,0.2,Up,0
53,M,ASY,125,0,1,Normal,120,N,1.5,Up,1
62,F,ASY,120,0,1,ST,123,Y,1.7,Down,1
51,M,ASY,95,0,1,Normal,126,N,2.2,Flat,1
51,F,ASY,120,0,1,Normal,127,Y,1.5,Up,1
55,M,ASY,115,0,1,Normal,155,N,0.1,Flat,1
53,M,ATA,130,0,0,ST,120,N,0.7,Down,0
58,M,ASY,115,0,1,Normal,138,N,0.5,Up,1
57,M,ASY,95,0,1,Normal,182,N,0.7,Down,1
65,M,ASY,155,0,0,Normal,154,N,1,Up,0
60,M,ASY,125,0,1,Normal,110,N,0.1,Up,1
41,M,ASY,125,0,1,Normal,176,N,1.6,Up,1
34,M,ASY,115,0,1,Normal,154,N,0.2,Up,1
53,M,ASY,80,0,0,Normal,141,Y,2,Down,0
74,M,ATA,145,0,1,ST,123,N,1.3,Up,1
57,M,NAP,105,0,1,Normal,148,N,0.3,Flat,1
56,M,ASY,140,0,1,Normal,121,Y,1.8,Up,1
61,M,ASY,130,0,1,Normal,77,N,2.5,Flat,1
68,M,ASY,145,0,1,Normal,136,N,1.8,Up,1
59,M,NAP,125,0,1,Normal,175,N,2.6,Flat,1
63,M,ASY,100,0,1,Normal,109,N,-0.9,Flat,1
38,F,ASY,105,0,1,Normal,166,N,2.8,Up,1
62,M,ASY,115,0,1,Normal,128,Y,2.5,Down,1
46,M,ASY,100,0,1,ST,133,N,-2.6,Flat,1
42,M,ASY,105,0,1,Normal,128,Y,-1.5,Down,1
45,M,NAP,110,0,0,Normal,138,N,-0.1,Up,0
59,M,ASY,125,0,1,Normal,119,Y,0.9,Up,1
52,M,ASY,95,0,1,Normal,82,Y,0.8,Flat,1
60,M,ASY,130,0,1,ST,130,Y,1.1,Down,1
60,M,NAP,115,0,1,Normal,143,N,2.4,Up,1
56,M,ASY,115,0,1,ST,82,N,-1,Up,1
38,M,NAP,100,0,0,Normal,179,N,-1.1,Up,0
40,M,ASY,95,0,1,ST,144,N,0,Up,1
51,M,ASY,130,0,1,Normal,170,N,-0.7,Up,1
62,M,TA,120,0,1,LVH,134,N,-0.8,Flat,1
72,M,NAP,160,0,0,LVH,114,N,1.6,Flat,0
63,M,ASY,150,0,1,ST,154,N,3.7,Up,1
63,M,ASY,140,0,1,LVH,149,N,2,Up,1
64,F,ASY,95,0,1,Normal,145,N,1.1,Down,1
43,M,ASY,100,0,1,Normal,122,N,1.5,Down,1
64,M,ASY,110,0,1,Normal,114,Y,1.3,Down,1
61,M,ASY,110,0,1,Normal,113,N,1.4,Flat,1
52,M,ASY,130,0,1,Normal,120,N,0,Flat,1
51,M,ASY,120,0,1,Normal,104,N,0,Flat,1
69,M,ASY,135,0,0,Normal,130,N,0,Flat,1
59,M,ASY,120,0,0,Normal,115,N,0,Flat,1
48,M,ASY,115,0,1,Normal,128,N,0,Flat,1
69,M,ASY,137,0,0,ST,104,Y,1.6,Flat,1
36,M,ASY,110,0,1,Normal,125,Y,1,Flat,1
53,M,ASY,120,0,1,Normal,120,N,0,Flat,1
43,M,ASY,140,0,0,ST,140,Y,0.5,Up,1
56,M,ASY,120,0,0,ST,100,Y,-1,Down,1
58,M,ASY,130,0,0,ST,100,Y,1,Flat,1
55,M,ASY,120,0,0,ST,92,N,0.3,Up,1
67,M,TA,145,0,0,LVH,125,N,0,Flat,1
46,M,ASY,115,0,0,Normal,113,Y,1.5,Flat,1
53,M,ATA,120,0,0,Normal,95,N,0,Flat,1
38,M,NAP,115,0,0,Normal,128,Y,0,Flat,1
53,M,NAP,105,0,0,Normal,115,N,0,Flat,1
62,M,NAP,160,0,0,Normal,72,Y,0,Flat,1
47,M,ASY,160,0,0,Normal,124,Y,0,Flat,1
56,M,NAP,155,0,0,ST,99,N,0,Flat,1
56,M,ASY,120,0,0,ST,148,N,0,Flat,1
56,M,NAP,120,0,0,Normal,97,N,0,Flat,0
64,F,ASY,200,0,0,Normal,140,Y,1,Flat,1
61,M,ASY,150,0,0,Normal,117,Y,2,Flat,1
68,M,ASY,135,0,0,ST,120,Y,0,Up,1
57,M,ASY,140,0,0,Normal,120,Y,2,Flat,1
63,M,ASY,150,0,0,Normal,86,Y,2,Flat,1
60,M,ASY,135,0,0,Normal,63,Y,0.5,Up,1
66,M,ASY,150,0,0,Normal,108,Y,2,Flat,1
63,M,ASY,185,0,0,Normal,98,Y,0,Up,1
59,M,ASY,135,0,0,Normal,115,Y,1,Flat,1
61,M,ASY,125,0,0,Normal,105,Y,0,Down,1
73,F,NAP,160,0,0,ST,121,N,0,Up,1
47,M,NAP,155,0,0,Normal,118,Y,1,Flat,1
65,M,ASY,160,0,1,ST,122,N,1.2,Flat,1
70,M,ASY,140,0,1,Normal,157,Y,2,Flat,1
50,M,ASY,120,0,0,ST,156,Y,0,Up,1
60,M,ASY,160,0,0,ST,99,Y,0.5,Flat,1
50,M,ASY,115,0,0,Normal,120,Y,0.5,Flat,1
43,M,ASY,115,0,0,Normal,145,Y,2,Flat,1
38,F,ASY,110,0,0,Normal,156,N,0,Flat,1
54,M,ASY,120,0,0,Normal,155,N,0,Flat,1
61,M,ASY,150,0,0,Normal,105,Y,0,Flat,1
42,M,ASY,145,0,0,Normal,99,Y,0,Flat,1
53,M,ASY,130,0,0,LVH,135,Y,1,Flat,1
55,M,ASY,140,0,0,Normal,83,N,0,Flat,1
61,M,ASY,160,0,1,ST,145,N,1,Flat,1
51,M,ASY,140,0,0,Normal,60,N,0,Flat,1
70,M,ASY,115,0,0,ST,92,Y,0,Flat,1
61,M,ASY,130,0,0,LVH,115,N,0,Flat,1
38,M,ASY,150,0,1,Normal,120,Y,0.7,Flat,1
57,M,ASY,160,0,1,Normal,98,Y,2,Flat,1
38,M,ASY,135,0,1,Normal,150,N,0,Flat,1
62,F,TA,140,0,1,Normal,143,N,0,Flat,1
58,M,ASY,170,0,1,ST,105,Y,0,Flat,1
52,M,ASY,165,0,1,Normal,122,Y,1,Up,1
61,M,NAP,200,0,1,ST,70,N,0,Flat,1
50,F,ASY,160,0,1,Normal,110,N,0,Flat,1
51,M,ASY,130,0,1,ST,163,N,0,Flat,1
65,M,ASY,145,0,1,ST,67,N,0.7,Flat,1
52,M,ASY,135,0,1,Normal,128,Y,2,Flat,1
47,M,NAP,110,0,1,Normal,120,Y,0,Flat,1
35,M,ASY,120,0,1,Normal,130,Y,1.2,Flat,1
57,M,ASY,140,0,1,Normal,100,Y,0,Flat,1
62,M,ASY,115,0,1,Normal,72,Y,-0.5,Flat,1
59,M,ASY,110,0,1,Normal,94,N,0,Flat,1
53,M,NAP,160,0,1,LVH,122,Y,0,Flat,1
62,M,ASY,150,0,1,ST,78,N,2,Flat,1
54,M,ASY,180,0,1,Normal,150,N,1.5,Flat,1
56,M,ASY,125,0,1,Normal,103,Y,1,Flat,1
56,M,NAP,125,0,1,Normal,98,N,-2,Flat,1
54,M,ASY,130,0,1,Normal,110,Y,3,Flat,1
66,F,ASY,155,0,1,Normal,90,N,0,Flat,1
63,M,ASY,140,260,0,ST,112,Y,3,Flat,1
44,M,ASY,130,209,0,ST,127,N,0,Up,0
60,M,ASY,132,218,0,ST,140,Y,1.5,Down,1
55,M,ASY,142,228,0,ST,149,Y,2.5,Up,1
66,M,NAP,110,213,1,LVH,99,Y,1.3,Flat,0
66,M,NAP,120,0,0,ST,120,N,-0.5,Up,0
65,M,ASY,150,236,1,ST,105,Y,0,Flat,1
60,M,NAP,180,0,0,ST,140,Y,1.5,Flat,0
60,M,NAP,120,0,1,Normal,141,Y,2,Up,1
60,M,ATA,160,267,1,ST,157,N,0.5,Flat,1
56,M,ATA,126,166,0,ST,140,N,0,Up,0
59,M,ASY,140,0,0,ST,117,Y,1,Flat,1
62,M,ASY,110,0,0,Normal,120,Y,0.5,Flat,1
63,M,NAP,133,0,0,LVH,120,Y,1,Flat,1
57,M,ASY,128,0,1,ST,148,Y,1,Flat,1
62,M,ASY,120,220,0,ST,86,N,0,Up,0
63,M,ASY,170,177,0,Normal,84,Y,2.5,Down,1
46,M,ASY,110,236,0,Normal,125,Y,2,Flat,1
63,M,ASY,126,0,0,ST,120,N,1.5,Down,0
60,M,ASY,152,0,0,ST,118,Y,0,Up,0
58,M,ASY,116,0,0,Normal,124,N,1,Up,1
64,M,ASY,120,0,1,ST,106,N,2,Flat,1
63,M,NAP,130,0,0,ST,111,Y,0,Flat,1
74,M,NAP,138,0,0,Normal,116,N,0.2,Up,0
52,M,NAP,128,0,0,ST,180,N,3,Up,1
69,M,ASY,130,0,1,ST,129,N,1,Flat,1
51,M,ASY,128,0,1,ST,125,Y,1.2,Flat,1
60,M,ASY,130,186,1,ST,140,Y,0.5,Flat,1
56,M,ASY,120,100,0,Normal,120,Y,1.5,Flat,1
55,M,NAP,136,228,0,ST,124,Y,1.6,Flat,1
54,M,ASY,130,0,0,ST,117,Y,1.4,Flat,1
77,M,ASY,124,171,0,ST,110,Y,2,Up,1
63,M,ASY,160,230,1,Normal,105,Y,1,Flat,1
55,M,NAP,0,0,0,Normal,155,N,1.5,Flat,1
52,M,NAP,122,0,0,Normal,110,Y,2,Down,1
64,M,ASY,144,0,0,ST,122,Y,1,Flat,1
60,M,ASY,140,281,0,ST,118,Y,1.5,Flat,1
60,M,ASY,120,0,0,Normal,133,Y,2,Up,0
58,M,ASY,136,203,1,Normal,123,Y,1.2,Flat,1
59,M,ASY,154,0,0,ST,131,Y,1.5,Up,0
61,M,NAP,120,0,0,Normal,80,Y,0,Flat,1
40,M,ASY,125,0,1,Normal,165,N,0,Flat,1
61,M,ASY,134,0,1,ST,86,N,1.5,Flat,1
41,M,ASY,104,0,0,ST,111,N,0,Up,0
57,M,ASY,139,277,1,ST,118,Y,1.9,Flat,1
63,M,ASY,136,0,0,Normal,84,Y,0,Flat,1
59,M,ASY,122,233,0,Normal,117,Y,1.3,Down,1
51,M,ASY,128,0,0,Normal,107,N,0,Up,0
59,M,NAP,131,0,0,Normal,128,Y,2,Down,1
42,M,NAP,134,240,0,Normal,160,N,0,Up,0
55,M,NAP,120,0,0,ST,125,Y,2.5,Flat,1
63,F,ATA,132,0,0,Normal,130,N,0.1,Up,0
62,M,ASY,152,153,0,ST,97,Y,1.6,Up,1
56,M,ATA,124,224,1,Normal,161,N,2,Flat,0
53,M,ASY,126,0,0,Normal,106,N,0,Flat,1
68,M,ASY,138,0,0,Normal,130,Y,3,Flat,1
53,M,ASY,154,0,1,ST,140,Y,1.5,Flat,1
60,M,NAP,141,316,1,ST,122,Y,1.7,Flat,1
62,M,ATA,131,0,0,Normal,130,N,0.1,Up,0
59,M,ASY,178,0,1,LVH,120,Y,0,Flat,1
51,M,ASY,132,218,1,LVH,139,N,0.1,Up,0
61,M,ASY,110,0,1,Normal,108,Y,2,Down,1
57,M,ASY,130,311,1,ST,148,Y,2,Flat,1
56,M,NAP,170,0,0,LVH,123,Y,2.5,Flat,1
58,M,ATA,126,0,1,Normal,110,Y,2,Flat,1
69,M,NAP,140,0,1,ST,118,N,2.5,Down,1
67,M,TA,142,270,1,Normal,125,N,2.5,Up,1
58,M,ASY,120,0,0,LVH,106,Y,1.5,Down,1
65,M,ASY,134,0,0,Normal,112,Y,1.1,Flat,1
63,M,ATA,139,217,1,ST,128,Y,1.2,Flat,1
55,M,ATA,110,214,1,ST,180,N,0.4,Up,0
57,M,ASY,140,214,0,ST,144,Y,2,Flat,1
65,M,TA,140,252,0,Normal,135,N,0.3,Up,0
54,M,ASY,136,220,0,Normal,140,Y,3,Flat,1
72,M,NAP,120,214,0,Normal,102,Y,1,Flat,1
75,M,ASY,170,203,1,ST,108,N,0,Flat,1
49,M,TA,130,0,0,ST,145,N,3,Flat,1
51,M,NAP,137,339,0,Normal,127,Y,1.7,Flat,1
60,M,ASY,142,216,0,Normal,110,Y,2.5,Flat,1
64,F,ASY,142,276,0,Normal,140,Y,1,Flat,1
58,M,ASY,132,458,1,Normal,69,N,1,Down,0
61,M,ASY,146,241,0,Normal,148,Y,3,Down,1
67,M,ASY,160,384,1,ST,130,Y,0,Flat,1
62,M,ASY,135,297,0,Normal,130,Y,1,Flat,1
65,M,ASY,136,248,0,Normal,140,Y,4,Down,1
63,M,ASY,130,308,0,Normal,138,Y,2,Flat,1
69,M,ASY,140,208,0,ST,140,Y,2,Flat,1
51,M,ASY,132,227,1,ST,138,N,0.2,Up,0
62,M,ASY,158,210,1,Normal,112,Y,3,Down,1
55,M,NAP,136,245,1,ST,131,Y,1.2,Flat,1
75,M,ASY,136,225,0,Normal,112,Y,3,Flat,1
40,M,NAP,106,240,0,Normal,80,Y,0,Up,0
67,M,ASY,120,0,1,Normal,150,N,1.5,Down,1
58,M,ASY,110,198,0,Normal,110,N,0,Flat,1
60,M,ASY,136,195,0,Normal,126,N,0.3,Up,0
63,M,ASY,160,267,1,ST,88,Y,2,Flat,1
35,M,NAP,123,161,0,ST,153,N,-0.1,Up,0
62,M,TA,112,258,0,ST,150,Y,1.3,Flat,1
43,M,ASY,122,0,0,Normal,120,N,0.5,Up,1
63,M,NAP,130,0,1,ST,160,N,3,Flat,0
68,M,NAP,150,195,1,Normal,132,N,0,Flat,1
65,M,ASY,150,235,0,Normal,120,Y,1.5,Flat,1
48,M,NAP,102,0,1,ST,110,Y,1,Down,1
63,M,ASY,96,305,0,ST,121,Y,1,Up,1
64,M,ASY,130,223,0,ST,128,N,0.5,Flat,0
61,M,ASY,120,282,0,ST,135,Y,4,Down,1
50,M,ASY,144,349,0,LVH,120,Y,1,Up,1
59,M,ASY,124,160,0,Normal,117,Y,1,Flat,1
55,M,ASY,150,160,0,ST,150,N,0,Up,0
45,M,NAP,130,236,0,Normal,144,N,0.1,Up,0
65,M,ASY,144,312,0,LVH,113,Y,1.7,Flat,1
61,M,ATA,139,283,0,Normal,135,N,0.3,Up,0
49,M,NAP,131,142,0,Normal,127,Y,1.5,Flat,1
72,M,ASY,143,211,0,Normal,109,Y,1.4,Flat,1
50,M,ASY,133,218,0,Normal,128,Y,1.1,Flat,1
64,M,ASY,143,306,1,ST,115,Y,1.8,Flat,1
55,M,ASY,116,186,1,ST,102,N,0,Flat,1
63,M,ASY,110,252,0,ST,140,Y,2,Flat,1
59,M,ASY,125,222,0,Normal,135,Y,2.5,Down,1
56,M,ASY,130,0,0,LVH,122,Y,1,Flat,1
62,M,NAP,133,0,1,ST,119,Y,1.2,Flat,1
74,M,ASY,150,258,1,ST,130,Y,4,Down,1
54,M,ASY,130,202,1,Normal,112,Y,2,Flat,1
57,M,ASY,110,197,0,LVH,100,N,0,Up,0
62,M,NAP,138,204,0,ST,122,Y,1.2,Flat,1
76,M,NAP,104,113,0,LVH,120,N,3.5,Down,1
54,F,ASY,138,274,0,Normal,105,Y,1.5,Flat,1
70,M,ASY,170,192,0,ST,129,Y,3,Down,1
61,F,ATA,140,298,1,Normal,120,Y,0,Up,0
48,M,ASY,132,272,0,ST,139,N,0.2,Up,0
48,M,NAP,132,220,1,ST,162,N,0,Flat,1
61,M,TA,142,200,1,ST,100,N,1.5,Down,1
66,M,ASY,112,261,0,Normal,140,N,1.5,Up,1
68,M,TA,139,181,1,ST,135,N,0.2,Up,0
55,M,ASY,172,260,0,Normal,73,N,2,Flat,1
62,M,NAP,120,220,0,LVH,86,N,0,Up,0
71,M,NAP,144,221,0,Normal,108,Y,1.8,Flat,1
74,M,TA,145,216,1,Normal,116,Y,1.8,Flat,1
53,M,NAP,155,175,1,ST,160,N,0.3,Up,0
58,M,NAP,150,219,0,ST,118,Y,0,Flat,1
75,M,ASY,160,310,1,Normal,112,Y,2,Down,0
56,M,NAP,137,208,1,ST,122,Y,1.8,Flat,1
58,M,NAP,137,232,0,ST,124,Y,1.4,Flat,1
64,M,ASY,134,273,0,Normal,102,Y,4,Down,1
54,M,NAP,133,203,0,ST,137,N,0.2,Up,0
54,M,ATA,132,182,0,ST,141,N,0.1,Up,0
59,M,ASY,140,274,0,Normal,154,Y,2,Flat,0
55,M,ASY,135,204,1,ST,126,Y,1.1,Flat,1
57,M,ASY,144,270,1,ST,160,Y,2,Flat,1
61,M,ASY,141,292,0,ST,115,Y,1.7,Flat,1
41,M,ASY,150,171,0,Normal,128,Y,1.5,Flat,0
71,M,ASY,130,221,0,ST,115,Y,0,Flat,1
38,M,ASY,110,289,0,Normal,105,Y,1.5,Down,1
55,M,ASY,158,217,0,Normal,110,Y,2.5,Flat,1
56,M,ASY,128,223,0,ST,119,Y,2,Down,1
69,M,ASY,140,110,1,Normal,109,Y,1.5,Flat,1
64,M,ASY,150,193,0,ST,135,Y,0.5,Flat,1
72,M,ASY,160,123,1,LVH,130,N,1.5,Flat,1
69,M,ASY,142,210,1,ST,112,Y,1.5,Flat,1
56,M,ASY,137,282,1,Normal,126,Y,1.2,Flat,1
62,M,ASY,139,170,0,ST,120,Y,3,Flat,1
67,M,ASY,146,369,0,Normal,110,Y,1.9,Flat,1
57,M,ASY,156,173,0,LVH,119,Y,3,Down,1
69,M,ASY,145,289,1,ST,110,Y,1.8,Flat,1
51,M,ASY,131,152,1,LVH,130,Y,1,Flat,1
48,M,ASY,140,208,0,Normal,159,Y,1.5,Up,1
69,M,ASY,122,216,1,LVH,84,Y,0,Flat,1
69,M,NAP,142,271,0,LVH,126,N,0.3,Up,0
64,M,ASY,141,244,1,ST,116,Y,1.5,Flat,1
57,M,ATA,180,285,1,ST,120,N,0.8,Flat,1
53,M,ASY,124,243,0,Normal,122,Y,2,Flat,1
37,M,NAP,118,240,0,LVH,165,N,1,Flat,0
67,M,ASY,140,219,0,ST,122,Y,2,Flat,1
74,M,NAP,140,237,1,Normal,94,N,0,Flat,1
63,M,ATA,136,165,0,ST,133,N,0.2,Up,0
58,M,ASY,100,213,0,ST,110,N,0,Up,0
61,M,ASY,190,287,1,LVH,150,Y,2,Down,1
64,M,ASY,130,258,1,LVH,130,N,0,Flat,1
58,M,ASY,160,256,1,LVH,113,Y,1,Up,1
60,M,ASY,130,186,1,LVH,140,Y,0.5,Flat,1
57,M,ASY,122,264,0,LVH,100,N,0,Flat,1
55,M,NAP,133,185,0,ST,136,N,0.2,Up,0
55,M,ASY,120,226,0,LVH,127,Y,1.7,Down,1
56,M,ASY,130,203,1,Normal,98,N,1.5,Flat,1
57,M,ASY,130,207,0,ST,96,Y,1,Flat,0
61,M,NAP,140,284,0,Normal,123,Y,1.3,Flat,1
61,M,NAP,120,337,0,Normal,98,Y,0,Flat,1
74,M,ASY,155,310,0,Normal,112,Y,1.5,Down,1
68,M,NAP,134,254,1,Normal,151,Y,0,Up,0
51,F,ASY,114,258,1,LVH,96,N,1,Up,0
62,M,ASY,160,254,1,ST,108,Y,3,Flat,1
53,M,ASY,144,300,1,ST,128,Y,1.5,Flat,1
62,M,ASY,158,170,0,ST,138,Y,0,Flat,1
46,M,ASY,134,310,0,Normal,126,N,0,Flat,1
54,F,ASY,127,333,1,ST,154,N,0,Flat,1
62,M,TA,135,139,0,ST,137,N,0.2,Up,0
55,M,ASY,122,223,1,ST,100,N,0,Flat,1
58,M,ASY,140,385,1,LVH,135,N,0.3,Up,0
62,M,ATA,120,254,0,LVH,93,Y,0,Flat,1
70,M,ASY,130,322,0,LVH,109,N,2.4,Flat,1
67,F,NAP,115,564,0,LVH,160,N,1.6,Flat,0
57,M,ATA,124,261,0,Normal,141,N,0.3,Up,1
64,M,ASY,128,263,0,Normal,105,Y,0.2,Flat,0
74,F,ATA,120,269,0,LVH,121,Y,0.2,Up,0
65,M,ASY,120,177,0,Normal,140,N,0.4,Up,0
56,M,NAP,130,256,1,LVH,142,Y,0.6,Flat,1
59,M,ASY,110,239,0,LVH,142,Y,1.2,Flat,1
60,M,ASY,140,293,0,LVH,170,N,1.2,Flat,1
63,F,ASY,150,407,0,LVH,154,N,4,Flat,1
59,M,ASY,135,234,0,Normal,161,N,0.5,Flat,0
53,M,ASY,142,226,0,LVH,111,Y,0,Up,0
44,M,NAP,140,235,0,LVH,180,N,0,Up,0
61,M,TA,134,234,0,Normal,145,N,2.6,Flat,1
57,F,ASY,128,303,0,LVH,159,N,0,Up,0
71,F,ASY,112,149,0,Normal,125,N,1.6,Flat,0
46,M,ASY,140,311,0,Normal,120,Y,1.8,Flat,1
53,M,ASY,140,203,1,LVH,155,Y,3.1,Down,1
64,M,TA,110,211,0,LVH,144,Y,1.8,Flat,0
40,M,TA,140,199,0,Normal,178,Y,1.4,Up,0
67,M,ASY,120,229,0,LVH,129,Y,2.6,Flat,1
48,M,ATA,130,245,0,LVH,180,N,0.2,Flat,0
43,M,ASY,115,303,0,Normal,181,N,1.2,Flat,0
47,M,ASY,112,204,0,Normal,143,N,0.1,Up,0
54,F,ATA,132,288,1,LVH,159,Y,0,Up,0
48,F,NAP,130,275,0,Normal,139,N,0.2,Up,0
46,F,ASY,138,243,0,LVH,152,Y,0,Flat,0
51,F,NAP,120,295,0,LVH,157,N,0.6,Up,0
58,M,NAP,112,230,0,LVH,165,N,2.5,Flat,1
71,F,NAP,110,265,1,LVH,130,N,0,Up,0
57,M,NAP,128,229,0,LVH,150,N,0.4,Flat,1
66,M,ASY,160,228,0,LVH,138,N,2.3,Up,0
37,F,NAP,120,215,0,Normal,170,N,0,Up,0
59,M,ASY,170,326,0,LVH,140,Y,3.4,Down,1
50,M,ASY,144,200,0,LVH,126,Y,0.9,Flat,1
48,M,ASY,130,256,1,LVH,150,Y,0,Up,1
61,M,ASY,140,207,0,LVH,138,Y,1.9,Up,1
59,M,TA,160,273,0,LVH,125,N,0,Up,1
42,M,NAP,130,180,0,Normal,150,N,0,Up,0
48,M,ASY,122,222,0,LVH,186,N,0,Up,0
40,M,ASY,152,223,0,Normal,181,N,0,Up,1
62,F,ASY,124,209,0,Normal,163,N,0,Up,0
44,M,NAP,130,233,0,Normal,179,Y,0.4,Up,0
46,M,ATA,101,197,1,Normal,156,N,0,Up,0
59,M,NAP,126,218,1,Normal,134,N,2.2,Flat,1
58,M,NAP,140,211,1,LVH,165,N,0,Up,0
49,M,NAP,118,149,0,LVH,126,N,0.8,Up,1
44,M,ASY,110,197,0,LVH,177,N,0,Up,1
66,M,ATA,160,246,0,Normal,120,Y,0,Flat,1
65,F,ASY,150,225,0,LVH,114,N,1,Flat,1
42,M,ASY,136,315,0,Normal,125,Y,1.8,Flat,1
52,M,ATA,128,205,1,Normal,184,N,0,Up,0
65,F,NAP,140,417,1,LVH,157,N,0.8,Up,0
63,F,ATA,140,195,0,Normal,179,N,0,Up,0
45,F,ATA,130,234,0,LVH,175,N,0.6,Flat,0
41,F,ATA,105,198,0,Normal,168,N,0,Up,0
61,M,ASY,138,166,0,LVH,125,Y,3.6,Flat,1
60,F,NAP,120,178,1,Normal,96,N,0,Up,0
59,F,ASY,174,249,0,Normal,143,Y,0,Flat,1
62,M,ATA,120,281,0,LVH,103,N,1.4,Flat,1
57,M,NAP,150,126,1,Normal,173,N,0.2,Up,0
51,F,ASY,130,305,0,Normal,142,Y,1.2,Flat,1
44,M,NAP,120,226,0,Normal,169,N,0,Up,0
60,F,TA,150,240,0,Normal,171,N,0.9,Up,0
63,M,TA,145,233,1,LVH,150,N,2.3,Down,0
57,M,ASY,150,276,0,LVH,112,Y,0.6,Flat,1
51,M,ASY,140,261,0,LVH,186,Y,0,Up,0
58,F,ATA,136,319,1,LVH,152,N,0,Up,1
44,F,NAP,118,242,0,Normal,149,N,0.3,Flat,0
47,M,NAP,108,243,0,Normal,152,N,0,Up,1
61,M,ASY,120,260,0,Normal,140,Y,3.6,Flat,1
57,F,ASY,120,354,0,Normal,163,Y,0.6,Up,0
70,M,ATA,156,245,0,LVH,143,N,0,Up,0
76,F,NAP,140,197,0,ST,116,N,1.1,Flat,0
67,F,ASY,106,223,0,Normal,142,N,0.3,Up,0
45,M,ASY,142,309,0,LVH,147,Y,0,Flat,1
45,M,ASY,104,208,0,LVH,148,Y,3,Flat,0
39,F,NAP,94,199,0,Normal,179,N,0,Up,0
42,F,NAP,120,209,0,Normal,173,N,0,Flat,0
56,M,ATA,120,236,0,Normal,178,N,0.8,Up,0
58,M,ASY,146,218,0,Normal,105,N,2,Flat,1
35,M,ASY,120,198,0,Normal,130,Y,1.6,Flat,1
58,M,ASY,150,270,0,LVH,111,Y,0.8,Up,1
41,M,NAP,130,214,0,LVH,168,N,2,Flat,0
57,M,ASY,110,201,0,Normal,126,Y,1.5,Flat,0
42,M,TA,148,244,0,LVH,178,N,0.8,Up,0
62,M,ATA,128,208,1,LVH,140,N,0,Up,0
59,M,TA,178,270,0,LVH,145,N,4.2,Down,0
41,F,ATA,126,306,0,Normal,163,N,0,Up,0
50,M,ASY,150,243,0,LVH,128,N,2.6,Flat,1
59,M,ATA,140,221,0,Normal,164,Y,0,Up,0
61,F,ASY,130,330,0,LVH,169,N,0,Up,1
54,M,ASY,124,266,0,LVH,109,Y,2.2,Flat,1
54,M,ASY,110,206,0,LVH,108,Y,0,Flat,1
52,M,ASY,125,212,0,Normal,168,N,1,Up,1
47,M,ASY,110,275,0,LVH,118,Y,1,Flat,1
66,M,ASY,120,302,0,LVH,151,N,0.4,Flat,0
58,M,ASY,100,234,0,Normal,156,N,0.1,Up,1
64,F,NAP,140,313,0,Normal,133,N,0.2,Up,0
50,F,ATA,120,244,0,Normal,162,N,1.1,Up,0
44,F,NAP,108,141,0,Normal,175,N,0.6,Flat,0
67,M,ASY,120,237,0,Normal,71,N,1,Flat,1
49,F,ASY,130,269,0,Normal,163,N,0,Up,0
57,M,ASY,165,289,1,LVH,124,N,1,Flat,1
63,M,ASY,130,254,0,LVH,147,N,1.4,Flat,1
48,M,ASY,124,274,0,LVH,166,N,0.5,Flat,1
51,M,NAP,100,222,0,Normal,143,Y,1.2,Flat,0
60,F,ASY,150,258,0,LVH,157,N,2.6,Flat,1
59,M,ASY,140,177,0,Normal,162,Y,0,Up,1
45,F,ATA,112,160,0,Normal,138,N,0,Flat,0
55,F,ASY,180,327,0,ST,117,Y,3.4,Flat,1
41,M,ATA,110,235,0,Normal,153,N,0,Up,0
60,F,ASY,158,305,0,LVH,161,N,0,Up,1
54,F,NAP,135,304,1,Normal,170,N,0,Up,0
42,M,ATA,120,295,0,Normal,162,N,0,Up,0
49,F,ATA,134,271,0,Normal,162,N,0,Flat,0
46,M,ASY,120,249,0,LVH,144,N,0.8,Up,1
56,F,ASY,200,288,1,LVH,133,Y,4,Down,1
66,F,TA,150,226,0,Normal,114,N,2.6,Down,0
56,M,ASY,130,283,1,LVH,103,Y,1.6,Down,1
49,M,NAP,120,188,0,Normal,139,N,2,Flat,1
54,M,ASY,122,286,0,LVH,116,Y,3.2,Flat,1
57,M,ASY,152,274,0,Normal,88,Y,1.2,Flat,1
65,F,NAP,160,360,0,LVH,151,N,0.8,Up,0
54,M,NAP,125,273,0,LVH,152,N,0.5,Down,0
54,F,NAP,160,201,0,Normal,163,N,0,Up,0
62,M,ASY,120,267,0,Normal,99,Y,1.8,Flat,1
52,F,NAP,136,196,0,LVH,169,N,0.1,Flat,0
52,M,ATA,134,201,0,Normal,158,N,0.8,Up,0
60,M,ASY,117,230,1,Normal,160,Y,1.4,Up,1
63,F,ASY,108,269,0,Normal,169,Y,1.8,Flat,1
66,M,ASY,112,212,0,LVH,132,Y,0.1,Up,1
42,M,ASY,140,226,0,Normal,178,N,0,Up,0
64,M,ASY,120,246,0,LVH,96,Y,2.2,Down,1
54,M,NAP,150,232,0,LVH,165,N,1.6,Up,0
46,F,NAP,142,177,0,LVH,160,Y,1.4,Down,0
67,F,NAP,152,277,0,Normal,172,N,0,Up,0
56,M,ASY,125,249,1,LVH,144,Y,1.2,Flat,1
34,F,ATA,118,210,0,Normal,192,N,0.7,Up,0
57,M,ASY,132,207,0,Normal,168,Y,0,Up,0
64,M,ASY,145,212,0,LVH,132,N,2,Flat,1
59,M,ASY,138,271,0,LVH,182,N,0,Up,0
50,M,NAP,140,233,0,Normal,163,N,0.6,Flat,1
51,M,TA,125,213,0,LVH,125,Y,1.4,Up,0
54,M,ATA,192,283,0,LVH,195,N,0,Up,1
53,M,ASY,123,282,0,Normal,95,Y,2,Flat,1
52,M,ASY,112,230,0,Normal,160,N,0,Up,1
40,M,ASY,110,167,0,LVH,114,Y,2,Flat,1
58,M,NAP,132,224,0,LVH,173,N,3.2,Up,1
41,F,NAP,112,268,0,LVH,172,Y,0,Up,0
41,M,NAP,112,250,0,Normal,179,N,0,Up,0
50,F,NAP,120,219,0,Normal,158,N,1.6,Flat,0
54,F,NAP,108,267,0,LVH,167,N,0,Up,0
64,F,ASY,130,303,0,Normal,122,N,2,Flat,0
51,F,NAP,130,256,0,LVH,149,N,0.5,Up,0
46,F,ATA,105,204,0,Normal,172,N,0,Up,0
55,M,ASY,140,217,0,Normal,111,Y,5.6,Down,1
45,M,ATA,128,308,0,LVH,170,N,0,Up,0
56,M,TA,120,193,0,LVH,162,N,1.9,Flat,0
66,F,ASY,178,228,1,Normal,165,Y,1,Flat,1
38,M,TA,120,231,0,Normal,182,Y,3.8,Flat,1
62,F,ASY,150,244,0,Normal,154,Y,1.4,Flat,1
55,M,ATA,130,262,0,Normal,155,N,0,Up,0
58,M,ASY,128,259,0,LVH,130,Y,3,Flat,1
43,M,ASY,110,211,0,Normal,161,N,0,Up,0
64,F,ASY,180,325,0,Normal,154,Y,0,Up,0
50,F,ASY,110,254,0,LVH,159,N,0,Up,0
53,M,NAP,130,197,1,LVH,152,N,1.2,Down,0
45,F,ASY,138,236,0,LVH,152,Y,0.2,Flat,0
65,M,TA,138,282,1,LVH,174,N,1.4,Flat,1
69,M,TA,160,234,1,LVH,131,N,0.1,Flat,0
69,M,NAP,140,254,0,LVH,146,N,2,Flat,1
67,M,ASY,100,299,0,LVH,125,Y,0.9,Flat,1
68,F,NAP,120,211,0,LVH,115,N,1.5,Flat,0
34,M,TA,118,182,0,LVH,174,N,0,Up,0
62,F,ASY,138,294,1,Normal,106,N,1.9,Flat,1
51,M,ASY,140,298,0,Normal,122,Y,4.2,Flat,1
46,M,NAP,150,231,0,Normal,147,N,3.6,Flat,1
67,M,ASY,125,254,1,Normal,163,N,0.2,Flat,1
50,M,NAP,129,196,0,Normal,163,N,0,Up,0
42,M,NAP,120,240,1,Normal,194,N,0.8,Down,0
56,F,ASY,134,409,0,LVH,150,Y,1.9,Flat,1
41,M,ASY,110,172,0,LVH,158,N,0,Up,1
42,F,ASY,102,265,0,LVH,122,N,0.6,Flat,0
53,M,NAP,130,246,1,LVH,173,N,0,Up,0
43,M,NAP,130,315,0,Normal,162,N,1.9,Up,0
56,M,ASY,132,184,0,LVH,105,Y,2.1,Flat,1
52,M,ASY,108,233,1,Normal,147,N,0.1,Up,0
62,F,ASY,140,394,0,LVH,157,N,1.2,Flat,0
70,M,NAP,160,269,0,Normal,112,Y,2.9,Flat,1
54,M,ASY,140,239,0,Normal,160,N,1.2,Up,0
70,M,ASY,145,174,0,Normal,125,Y,2.6,Down,1
54,M,ATA,108,309,0,Normal,156,N,0,Up,0
35,M,ASY,126,282,0,LVH,156,Y,0,Up,1
48,M,NAP,124,255,1,Normal,175,N,0,Up,0
55,F,ATA,135,250,0,LVH,161,N,1.4,Flat,0
58,F,ASY,100,248,0,LVH,122,N,1,Flat,0
54,F,NAP,110,214,0,Normal,158,N,1.6,Flat,0
69,F,TA,140,239,0,Normal,151,N,1.8,Up,0
77,M,ASY,125,304,0,LVH,162,Y,0,Up,1
68,M,NAP,118,277,0,Normal,151,N,1,Up,0
58,M,ASY,125,300,0,LVH,171,N,0,Up,1
60,M,ASY,125,258,0,LVH,141,Y,2.8,Flat,1
51,M,ASY,140,299,0,Normal,173,Y,1.6,Up,1
55,M,ASY,160,289,0,LVH,145,Y,0.8,Flat,1
52,M,TA,152,298,1,Normal,178,N,1.2,Flat,0
60,F,NAP,102,318,0,Normal,160,N,0,Up,0
58,M,NAP,105,240,0,LVH,154,Y,0.6,Flat,0
64,M,NAP,125,309,0,Normal,131,Y,1.8,Flat,1
37,M,NAP,130,250,0,Normal,187,N,3.5,Down,0
59,M,TA,170,288,0,LVH,159,N,0.2,Flat,1
51,M,NAP,125,245,1,LVH,166,N,2.4,Flat,0
43,F,NAP,122,213,0,Normal,165,N,0.2,Flat,0
58,M,ASY,128,216,0,LVH,131,Y,2.2,Flat,1
29,M,ATA,130,204,0,LVH,202,N,0,Up,0
41,F,ATA,130,204,0,LVH,172,N,1.4,Up,0
63,F,NAP,135,252,0,LVH,172,N,0,Up,0
51,M,NAP,94,227,0,Normal,154,Y,0,Up,0
54,M,NAP,120,258,0,LVH,147,N,0.4,Flat,0
44,M,ATA,120,220,0,Normal,170,N,0,Up,0
54,M,ASY,110,239,0,Normal,126,Y,2.8,Flat,1
65,M,ASY,135,254,0,LVH,127,N,2.8,Flat,1
57,M,NAP,150,168,0,Normal,174,N,1.6,Up,0
63,M,ASY,130,330,1,LVH,132,Y,1.8,Up,1
35,F,ASY,138,183,0,Normal,182,N,1.4,Up,0
41,M,ATA,135,203,0,Normal,132,N,0,Flat,0
62,F,NAP,130,263,0,Normal,97,N,1.2,Flat,1
43,F,ASY,132,341,1,LVH,136,Y,3,Flat,1
58,F,TA,150,283,1,LVH,162,N,1,Up,0
52,M,TA,118,186,0,LVH,190,N,0,Flat,0
61,F,ASY,145,307,0,LVH,146,Y,1,Flat,1
39,M,ASY,118,219,0,Normal,140,N,1.2,Flat,1
45,M,ASY,115,260,0,LVH,185,N,0,Up,0
52,M,ASY,128,255,0,Normal,161,Y,0,Up,1
62,M,NAP,130,231,0,Normal,146,N,1.8,Flat,0
62,F,ASY,160,164,0,LVH,145,N,6.2,Down,1
53,F,ASY,138,234,0,LVH,160,N,0,Up,0
43,M,ASY,120,177,0,LVH,120,Y,2.5,Flat,1
47,M,NAP,138,257,0,LVH,156,N,0,Up,0
52,M,ATA,120,325,0,Normal,172,N,0.2,Up,0
68,M,NAP,180,274,1,LVH,150,Y,1.6,Flat,1
39,M,NAP,140,321,0,LVH,182,N,0,Up,0
53,F,ASY,130,264,0,LVH,143,N,0.4,Flat,0
62,F,ASY,140,268,0,LVH,160,N,3.6,Down,1
51,F,NAP,140,308,0,LVH,142,N,1.5,Up,0
60,M,ASY,130,253,0,Normal,144,Y,1.4,Up,1
65,M,ASY,110,248,0,LVH,158,N,0.6,Up,1
65,F,NAP,155,269,0,Normal,148,N,0.8,Up,0
60,M,NAP,140,185,0,LVH,155,N,3,Flat,1
60,M,ASY,145,282,0,LVH,142,Y,2.8,Flat,1
54,M,ASY,120,188,0,Normal,113,N,1.4,Flat,1
44,M,ATA,130,219,0,LVH,188,N,0,Up,0
44,M,ASY,112,290,0,LVH,153,N,0,Up,1
51,M,NAP,110,175,0,Normal,123,N,0.6,Up,0
59,M,NAP,150,212,1,Normal,157,N,1.6,Up,0
71,F,ATA,160,302,0,Normal,162,N,0.4,Up,0
61,M,NAP,150,243,1,Normal,137,Y,1,Flat,0
55,M,ASY,132,353,0,Normal,132,Y,1.2,Flat,1
64,M,NAP,140,335,0,Normal,158,N,0,Up,1
43,M,ASY,150,247,0,Normal,171,N,1.5,Up,0
58,F,NAP,120,340,0,Normal,172,N,0,Up,0
60,M,ASY,130,206,0,LVH,132,Y,2.4,Flat,1
58,M,ATA,120,284,0,LVH,160,N,1.8,Flat,1
49,M,ATA,130,266,0,Normal,171,N,0.6,Up,0
48,M,ATA,110,229,0,Normal,168,N,1,Down,1
52,M,NAP,172,199,1,Normal,162,N,0.5,Up,0
44,M,ATA,120,263,0,Normal,173,N,0,Up,0
56,F,ATA,140,294,0,LVH,153,N,1.3,Flat,0
57,M,ASY,140,192,0,Normal,148,N,0.4,Flat,0
67,M,ASY,160,286,0,LVH,108,Y,1.5,Flat,1
53,F,NAP,128,216,0,LVH,115,N,0,Up,0
52,M,NAP,138,223,0,Normal,169,N,0,Up,0
43,M,ASY,132,247,1,LVH,143,Y,0.1,Flat,1
52,M,ASY,128,204,1,Normal,156,Y,1,Flat,1
59,M,TA,134,204,0,Normal,162,N,0.8,Up,1
64,M,TA,170,227,0,LVH,155,N,0.6,Flat,0
66,F,NAP,146,278,0,LVH,152,N,0,Flat,0
39,F,NAP,138,220,0,Normal,152,N,0,Flat,0
57,M,ATA,154,232,0,LVH,164,N,0,Up,1
58,F,ASY,130,197,0,Normal,131,N,0.6,Flat,0
57,M,ASY,110,335,0,Normal,143,Y,3,Flat,1
47,M,NAP,130,253,0,Normal,179,N,0,Up,0
55,F,ASY,128,205,0,ST,130,Y,2,Flat,1
35,M,ATA,122,192,0,Normal,174,N,0,Up,0
61,M,ASY,148,203,0,Normal,161,N,0,Up,1
58,M,ASY,114,318,0,ST,140,N,4.4,Down,1
58,F,ASY,170,225,1,LVH,146,Y,2.8,Flat,1
58,M,ATA,125,220,0,Normal,144,N,0.4,Flat,0
56,M,ATA,130,221,0,LVH,163,N,0,Up,0
56,M,ATA,120,240,0,Normal,169,N,0,Down,0
67,M,NAP,152,212,0,LVH,150,N,0.8,Flat,1
55,F,ATA,132,342,0,Normal,166,N,1.2,Up,0
44,M,ASY,120,169,0,Normal,144,Y,2.8,Down,1
63,M,ASY,140,187,0,LVH,144,Y,4,Up,1
63,F,ASY,124,197,0,Normal,136,Y,0,Flat,1
41,M,ATA,120,157,0,Normal,182,N,0,Up,0
59,M,ASY,164,176,1,LVH,90,N,1,Flat,1
57,F,ASY,140,241,0,Normal,123,Y,0.2,Flat,1
45,M,TA,110,264,0,Normal,132,N,1.2,Flat,1
68,M,ASY,144,193,1,Normal,141,N,3.4,Flat,1
57,M,ASY,130,131,0,Normal,115,Y,1.2,Flat,1
57,F,ATA,130,236,0,LVH,174,N,0,Flat,1
38,M,NAP,138,175,0,Normal,173,N,0,Up,0

FAQ

Common questions

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