SciPy-Data-Analyzer-Suite

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✅ Python Compatibility

This project is fully compatible with the following Python versions:

Version	Status
3.8	✅ Supported
3.9	✅ Supported
3.10+	⚠️ Untested
<3.8	❌ Not supported

✅ Tested and verified using GitHub Actions with setup-python and runtime environments for Python 3.8 and 3.9.

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🌟 Key Features

• ✅ CSV Data Import with automatic error handling.
• ✅ Statistical Analysis with descriptive statistics and normality tests (Shapiro-Wilk).
• ✅ Fourier Transformation (FFT) for frequency analysis of time series.
• ✅ Interpolation of missing data points using various methods.
• ✅ Numerical Integration of user-defined functions.
• ✅ Optimization of mathematical functions with constraints.
• ✅ Visual Representation of all results with intuitive and appealing plots.

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📂 Application Contents

The ZIP file contains:

scipy_application.zip
│
├── data_analyzer_suite.py    # Python script of the application
└── sample_data.csv           # Sample CSV data

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🚩 Requirements

Please ensure the following libraries are installed:

pip install numpy pandas scipy matplotlib

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🚦 Quick Start

1. Download and unzip the ZIP file:

unzip scipy_application.zip
cd scipy_application

2. Start the application:

python data_analyzer_suite.py

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🛠️ Customizing CSV Data

The sample CSV file (sample_data.csv) has the following structure:

time,value
0,0.5
1,0.7
2,
3,1.5
4,2.1
5,3.8
6,
7,5.1
8,6.2

Edit this file or replace it with your own data. Note that the value column contains numeric values and may allow missing values.

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📌 Explanation of Functions

1. Statistical Analysis

• Descriptive statistics (mean, variance, etc.)
• Normality test (Shapiro-Wilk)

2. FFT Frequency Analysis

• Fast Fourier Transform
• Detection of dominant frequencies

3. Interpolation

• Filling missing data points
• Methods such as linear, quadratic, cubic

4. Numerical Integration

• Calculation of definite integrals of user-defined functions

5. Functional Optimization

• Example using Rosenbrock function
• Easily adaptable for other optimizations

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🎯 Target Audience

• Scientific research
• Data analysts
• Engineers & developers
• Students & lecturers in STEM fields

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MIT LICENSE

LICENSE

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🌟 Developed with passion & expertise. Enhance your data analysis with the SciPy Data Analyzer Suite! 🌟