ferrisup 0.2.5

A versatile Rust project bootstrapping tool - start anywhere, scale anywhere
Documentation 
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use anyhow::Result;
use polars::prelude::*;
use std::path::Path;

/// Generate a summary of the dataset
pub fn summarize(df: &DataFrame, columns: &[String]) -> Result<String> {
    let df = select_columns(df, columns)?;
    
    // Calculate descriptive statistics
    let mut result = String::new();
    result.push_str("## Dataset Summary\n\n");
    
    // Overall shape
    result.push_str(&format!("Shape: {} rows × {} columns\n\n", df.height(), df.width()));
    
    // Column names and types
    result.push_str("### Columns\n\n");
    for field in df.schema().iter_fields() {
        result.push_str(&format!("- `{}`: {}\n", field.0, field.1));
    }
    result.push_str("\n");
    
    // Descriptive statistics
    result.push_str("### Descriptive Statistics\n\n");
    match df.describe(None) {
        Ok(stats) => {
            result.push_str(&format!("{}\n", stats));
        },
        Err(e) => {
            result.push_str(&format!("Error calculating statistics: {}\n", e));
        }
    }
    
    // Missing values
    result.push_str("\n### Missing Values\n\n");
    for col in df.get_column_names() {
        let null_count = df.column(col)?.null_count();
        let percent = (null_count as f64 / df.height() as f64) * 100.0;
        result.push_str(&format!("- `{}`: {} ({:.2}%)\n", col, null_count, percent));
    }
    
    Ok(result)
}

/// Calculate correlations between numeric columns
pub fn calculate_correlations(df: &DataFrame, columns: &[String]) -> Result<String> {
    let df = select_columns(df, columns)?;
    
    // Filter numeric columns
    let numeric_cols: Vec<_> = df.get_column_names().iter()
        .filter(|&col| matches!(df.column(col).unwrap().dtype(), DataType::Float64 | DataType::Int64))
        .map(|&s| s.to_string())
        .collect();
    
    if numeric_cols.is_empty() {
        return Ok("No numeric columns found for correlation analysis.".to_string());
    }
    
    let mut result = String::new();
    result.push_str("## Correlation Analysis\n\n");
    
    // Create a correlation matrix
    match df.select(&numeric_cols).unwrap().corr() {
        Ok(corr_matrix) => {
            result.push_str(&format!("{}\n", corr_matrix));
        },
        Err(e) => {
            result.push_str(&format!("Error calculating correlation matrix: {}\n", e));
        }
    }
    
    Ok(result)
}

/// Cluster data using k-means or similar algorithm
pub fn cluster_data(df: &DataFrame, columns: &[String]) -> Result<String> {
    let df = select_columns(df, columns)?;
    
    // In a real implementation, this would:
    // 1. Scale the numeric data
    // 2. Apply a clustering algorithm (e.g., k-means)
    // 3. Assign cluster labels
    // 4. Report statistics by cluster
    
    let mut result = String::new();
    result.push_str("## Clustering Analysis\n\n");
    result.push_str("This is a placeholder for clustering analysis.\n");
    result.push_str("In a real implementation, this would perform k-means or other clustering algorithms.\n");
    result.push_str("\nSample code to implement with linfa would be:\n\n");
    result.push_str("```rust\n");
    result.push_str("use linfa::prelude::*;\n");
    result.push_str("use linfa_clustering::{KMeans, KMeansParams};\n\n");
    result.push_str("// Prepare data as a Dataset\n");
    result.push_str("let dataset = ...; // Convert DataFrame to Dataset\n\n");
    result.push_str("// Run k-means with k=3\n");
    result.push_str("let model = KMeans::params(3)\n");
    result.push_str("    .max_n_iterations(100)\n");
    result.push_str("    .tolerance(1e-5)\n");
    result.push_str("    .fit(&dataset)?;\n\n");
    result.push_str("// Get cluster assignments\n");
    result.push_str("let predictions = model.predict(&dataset);\n");
    result.push_str("```\n");
    
    Ok(result)
}

/// Analyze time series data
pub fn analyze_timeseries(df: &DataFrame, columns: &[String]) -> Result<String> {
    let df = select_columns(df, columns)?;
    
    // In a real implementation, this would:
    // 1. Identify date/time columns
    // 2. Resample time series as needed
    // 3. Calculate trends, seasonality, etc.
    // 4. Generate forecasts
    
    let mut result = String::new();
    result.push_str("## Time Series Analysis\n\n");
    
    // Look for datetime columns
    let datetime_cols: Vec<_> = df.get_column_names().iter()
        .filter(|&col| {
            matches!(df.column(col).unwrap().dtype(), 
                     DataType::Date | DataType::Datetime(_, _))
        })
        .map(|&s| s.to_string())
        .collect();
    
    if datetime_cols.is_empty() {
        result.push_str("No datetime columns found for time series analysis.\n");
    } else {
        result.push_str(&format!("Found {} datetime columns: {}\n\n", 
                                datetime_cols.len(), 
                                datetime_cols.join(", ")));
        
        result.push_str("This is a placeholder for time series analysis.\n");
        result.push_str("In a real implementation, this would perform trend analysis, seasonality decomposition, and forecasting.\n");
    }
    
    Ok(result)
}

/// Generate visualizations of the data
pub fn visualize(df: &DataFrame, columns: &[String], output_path: &Path) -> Result<()> {
    use plotters::prelude::*;
    
    // Select columns for visualization
    let df = select_columns(df, columns)?;
    
    // Create a simple plot (this is just a placeholder)
    let root = BitMapBackend::new(output_path, (800, 600)).into_drawing_area();
    root.fill(&WHITE)?;
    
    let mut chart_builder = ChartBuilder::on(&root)
        .caption("Data Visualization", ("sans-serif", 40))
        .margin(10)
        .x_label_area_size(30)
        .y_label_area_size(30);
    
    // Find numeric columns for plotting
    let numeric_cols: Vec<_> = df.get_column_names().iter()
        .filter(|&col| matches!(df.column(col).unwrap().dtype(), DataType::Float64 | DataType::Int64))
        .map(|&s| s.to_string())
        .collect();
    
    if numeric_cols.len() < 2 {
        // Create a simple bar chart for the first column if there's only one
        if numeric_cols.len() == 1 {
            let col_name = &numeric_cols[0];
            let series = df.column(col_name)?.i64()?;
            
            let mut chart = chart_builder
                .build_cartesian_2d(0..series.len() as i32, 0..*series.max()? as i32 + 10)?;
            
            chart.configure_mesh().draw()?;
            
            chart.draw_series(
                Histogram::vertical(&chart)
                    .style(BLUE.filled())
                    .data(series.iter().enumerate().map(|(i, v)| (i as i32, v.unwrap_or(0))))
            )?;
            
            chart.configure_series_labels().draw()?;
        } else {
            // Just write a message if there are no numeric columns
            root.draw_text(
                "No numeric columns available for visualization",
                &TextStyle::from(("sans-serif", 20)).color(&BLACK),
                (400, 300),
            )?;
        }
    } else {
        // Create a scatter plot of the first two numeric columns
        let x_col = &numeric_cols[0];
        let y_col = &numeric_cols[1];
        
        let x_series = df.column(x_col)?.f64()?;
        let y_series = df.column(y_col)?.f64()?;
        
        let x_min = *x_series.min()? - 1.0;
        let x_max = *x_series.max()? + 1.0;
        let y_min = *y_series.min()? - 1.0;
        let y_max = *y_series.max()? + 1.0;
        
        let mut chart = chart_builder
            .build_cartesian_2d(x_min..x_max, y_min..y_max)?;
        
        chart.configure_mesh()
            .x_desc(x_col)
            .y_desc(y_col)
            .draw()?;
        
        let points: Vec<_> = x_series.iter()
            .zip(y_series.iter())
            .filter_map(|(x, y)| match (x, y) {
                (Some(x), Some(y)) => Some((*x, *y)),
                _ => None,
            })
            .collect();
        
        chart.draw_series(
            points.iter().map(|point| Circle::new(*point, 3, BLUE.filled()))
        )?;
        
        chart.configure_series_labels()
            .background_style(WHITE.mix(0.8))
            .border_style(BLACK)
            .draw()?;
    }
    
    root.present()?;
    
    Ok(())
}

/// Helper function to select columns from DataFrame
fn select_columns(df: &DataFrame, columns: &[String]) -> Result<DataFrame> {
    if columns.is_empty() {
        Ok(df.clone())
    } else {
        // Filter to include only columns that exist in the dataframe
        let available_cols: Vec<_> = columns.iter()
            .filter(|col| df.get_column_names().contains(&col.as_str()))
            .map(|s| s.as_str())
            .collect();
            
        if available_cols.is_empty() {
            // If none of the requested columns exist, return the original dataframe
            Ok(df.clone())
        } else {
            Ok(df.select(&available_cols)?)
        }
    }
}