mitoolspro 1.0.1

miToolsPro is a collection of tools for data analysis and visualization.

miToolsPro

A comprehensive Python toolkit for data analysis, visualization, and research workflows. Features 17 specialized modules covering plotting, econometric modeling, clustering analysis, economic complexity, document processing, and modern API integrations.

Installation

pip install mitoolspro

Requirements: Python 3.12+

Quick Start

from mitoolspro.plotting import LinePlotter
from mitoolspro.clustering import kmeans_clustering
import numpy as np

# Create sample data
data = np.random.rand(100, 2)

# Create a line plot
plotter = LinePlotter(x_data=data[:, 0], y_data=data[:, 1])
plotter.plot()

# Perform clustering
model, labels = kmeans_clustering(data, n_clusters=3)

Core Modules

📊 Plotting (mitoolspro.plotting)

Professional-grade plotting with matplotlib compatibility, type validation, and composable architecture.

Available Plotters:

• BarPlotter, BoxPlotter, ScatterPlotter, LinePlotter
• HistogramPlotter, PiePlotter, SankeyPlotter
• DistributionPlotter, ErrorPlotter

Features:

• Type-safe parameter validation with Pydantic
• Plot composition with PlotComposer
• Mixin architecture for shared functionality
• matplotlib API compatibility

from mitoolspro.plotting import PlotComposer, BarPlotter, LinePlotter

# Create individual plots
bar = BarPlotter(x_data=['A', 'B', 'C'], y_data=[1, 2, 3])
line = LinePlotter(x_data=[1, 2, 3], y_data=[1, 4, 2])

# Compose multiple plots
composer = PlotComposer()
composer.add_plot(bar, position=(0, 0))
composer.add_plot(line, position=(0, 1))
composer.compose(figsize=(12, 6))

📈 Econometric Analysis (mitoolspro.regressions)

Professional econometric modeling with comprehensive diagnostic tools.

Model Types:

• OLS: Ordinary Least Squares with robust standard errors
• Panel: Fixed/Random effects, time series cross-sectional data
• IV: Instrumental Variables, Two-Stage Least Squares
• Quantile: Quantile regression across multiple quantiles
• Regime: Regime switching and structural break models
• Factor: Multi-factor models and principal components

from mitoolspro.regressions.linear_models import OLSModel
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Fit OLS model
model = OLSModel(
    data=data,
    dependent_variable='price',
    independent_variables=['size', 'location'],
    control_variables=['year']
)
results = model.fit()
print(results.results.summary())

Advanced Quantile Regression:

from mitoolspro.regressions.managers import QuantilesRegression
from mitoolspro.regressions.wrappers.linear_models import QuantilesRegressionSpecs

# Define regression specifications
specs = QuantilesRegressionSpecs(
    dataframe=data,
    dependent_variable='price',
    independent_variables=['size', 'location']
)

# Run quantile regression across multiple quantiles
qr = QuantilesRegression(specs)
results = qr.fit(quantiles=[0.25, 0.5, 0.75])

🎯 Clustering Analysis (mitoolspro.clustering)

Complete clustering pipeline with evaluation metrics and visualization.

Algorithms:

• K-means clustering with automatic optimization
• Agglomerative hierarchical clustering
• Automatic cluster number detection

Evaluation & Visualization:

• Silhouette analysis and scoring
• Centroid calculation and visualization
• Distance metrics and similarity measures
• Growth analysis and cluster size tracking

from mitoolspro.clustering import clustering_ncluster_search, plot_clusters_growth
import numpy as np

# Generate sample data
data = np.random.rand(200, 4)

# Find optimal number of clusters
best_n, results = clustering_ncluster_search(data, n_range=(2, 10))
print(f"Optimal clusters: {best_n}")

# Visualize cluster analysis
plot_clusters_growth(results)

🌍 Economic Complexity Analysis (mitoolspro.economic_complexity)

Advanced tools for trade analysis and economic complexity calculations.

Core Functions:

• ECI/PCI Calculation: Economic and Product Complexity Indices
• RCA Analysis: Revealed Comparative Advantage matrices
• Proximity Networks: Product and country similarity analysis
• GPU Acceleration: PyTorch integration for large datasets

from mitoolspro.economic_complexity import (
    calculate_economic_complexity,
    calculate_proximity_matrix,
    exports_data_to_matrix
)
import pandas as pd

# Process trade data
trade_data = pd.read_csv('trade_data.csv')
rca_matrix = calculate_exports_matrix_rca(trade_data, 'country', 'product', 'value')

# Calculate complexity indices
eci, pci = calculate_economic_complexity(rca_matrix.values)

# Build proximity networks
proximity = calculate_proximity_matrix(rca_matrix.values)

🤖 LLM Integration (mitoolspro.llms)

Production-ready LLM clients with usage tracking and cost management.

Supported Providers:

• OpenAI: GPT models with structured output support
• Ollama: Local LLM deployment and management

Features:

• Token usage tracking and cost calculation
• Persistent usage history across sessions
• Model registry with pricing information
• Beta features support (structured outputs)

from mitoolspro.llms import OpenAIClient, PersistentTokensCounter

# Set up usage tracking
counter = PersistentTokensCounter(
    file_path="usage.json", 
    source="openai", 
    model="gpt-4o-mini"
)

# Create client with usage tracking
client = OpenAIClient(
    api_key="your-api-key",
    model="gpt-4o-mini",
    counter=counter
)

# Make requests with automatic cost tracking
response = client.request("Analyze this data trend...")
print(f"Total cost: ${counter.calculate_total_cost():.4f}")

📄 Document Processing (mitoolspro.document)

Extract and analyze content from PDF and DOCX documents.

PDF Processing:

• Text extraction with layout preservation
• Document structure analysis (pages, blocks, lines)
• Font and formatting detection
• Metadata extraction

Document Generation:

• DOCX file creation and manipulation
• Text styling and formatting
• Table and image insertion

from mitoolspro.document import pdf_to_document
from mitoolspro.document.write_document import create_docx_document

# Extract structured content from PDF
document = pdf_to_document("report.pdf")
for page in document.pages:
    print(f"Page {page.page_number}: {len(page.lines)} lines")

# Create new DOCX document
create_docx_document(
    filename="output.docx",
    title="Analysis Report",
    content_blocks=[("heading", "Results"), ("paragraph", "Analysis complete.")]
)

Specialized Modules

🌐 Google API Integration (mitoolspro.google_utils)

Places API:

• Location search and analysis
• Geospatial data processing
• Business saturation studies

YouTube API:

• Video download and conversion
• Metadata extraction
• Batch processing workflows

🕸️ Networks (mitoolspro.networks)

Interactive network visualization with pyvis integration.

🗄️ Databases (mitoolspro.databases)

SQLAlchemy and SQLite utilities for data persistence.

📁 Files (mitoolspro.files)

Multi-format file handlers: Excel, PDF, ICS, and document conversion.

🔤 NLP (mitoolspro.nlp)

Text processing with spaCy and Transformers integration.

🕷️ Scraping (mitoolspro.scraping)

Web scraping tools with Selenium automation.

🛠️ Utilities (mitoolspro.utils)

Development tools, decorators, and context managers.

Example Notebooks

Comprehensive examples in the examples/ directory:

Plotting: bar_plotter.ipynb, composer.ipynb

Analysis: clustering.ipynb, networks.ipynb

Regression: ols.ipynb, ivars.ipynb

Development

# Clone and install for development
git clone https://github.com/montanon/miToolsPro.git
cd miToolsPro
uv sync --group dev

# Run tests with coverage
uv run pytest tests/ --cov=mitoolspro

# Generate coverage report
uv run coverage html

Technical Details

• 77% Test Coverage across 94 test files
• Type Safety with comprehensive annotations
• Exception Handling with 83 custom exception classes
• Modern Architecture with lazy loading and abstract base classes
• Performance optimized with parallel processing support

Dependencies

Core Stack:

• Data: pandas, numpy, scipy
• Visualization: matplotlib, seaborn, plotly
• ML/Stats: scikit-learn, statsmodels, torch
• Documents: pymupdf, python-docx, pdfminer
• Web: seleniumbase, requests

Full dependency list: See pyproject.toml

License

MIT License - see LICENSE file.

Copyright (c) 2025 Sebastián Montagna

Support

• Documentation: Coming soon
• Issues: GitHub Issues